research papers covid 19

• Research article
• Open access
• Published: 04 June 2021

Coronavirus disease (COVID-19) pandemic: an overview of systematic reviews

• Israel Júnior Borges do Nascimento 1 , 2 ,
• Dónal P. O’Mathúna 3 , 4 ,
• Thilo Caspar von Groote 5 ,
• Hebatullah Mohamed Abdulazeem 6 ,
• Ishanka Weerasekara 7 , 8 ,
• Ana Marusic 9 ,
• Livia Puljak   ORCID: orcid.org/0000-0002-8467-6061 10 ,
• Vinicius Tassoni Civile 11 ,
• Irena Zakarija-Grkovic 9 ,
• Tina Poklepovic Pericic 9 ,
• Alvaro Nagib Atallah 11 ,
• Santino Filoso 12 ,
• Nicola Luigi Bragazzi 13 &
• Milena Soriano Marcolino 1

On behalf of the International Network of Coronavirus Disease 2019 (InterNetCOVID-19)

BMC Infectious Diseases volume  21 , Article number:  525 ( 2021 ) Cite this article

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Navigating the rapidly growing body of scientific literature on the SARS-CoV-2 pandemic is challenging, and ongoing critical appraisal of this output is essential. We aimed to summarize and critically appraise systematic reviews of coronavirus disease (COVID-19) in humans that were available at the beginning of the pandemic.

Nine databases (Medline, EMBASE, Cochrane Library, CINAHL, Web of Sciences, PDQ-Evidence, WHO’s Global Research, LILACS, and Epistemonikos) were searched from December 1, 2019, to March 24, 2020. Systematic reviews analyzing primary studies of COVID-19 were included. Two authors independently undertook screening, selection, extraction (data on clinical symptoms, prevalence, pharmacological and non-pharmacological interventions, diagnostic test assessment, laboratory, and radiological findings), and quality assessment (AMSTAR 2). A meta-analysis was performed of the prevalence of clinical outcomes.

Eighteen systematic reviews were included; one was empty (did not identify any relevant study). Using AMSTAR 2, confidence in the results of all 18 reviews was rated as “critically low”. Identified symptoms of COVID-19 were (range values of point estimates): fever (82–95%), cough with or without sputum (58–72%), dyspnea (26–59%), myalgia or muscle fatigue (29–51%), sore throat (10–13%), headache (8–12%) and gastrointestinal complaints (5–9%). Severe symptoms were more common in men. Elevated C-reactive protein and lactate dehydrogenase, and slightly elevated aspartate and alanine aminotransferase, were commonly described. Thrombocytopenia and elevated levels of procalcitonin and cardiac troponin I were associated with severe disease. A frequent finding on chest imaging was uni- or bilateral multilobar ground-glass opacity. A single review investigated the impact of medication (chloroquine) but found no verifiable clinical data. All-cause mortality ranged from 0.3 to 13.9%.

Conclusions

In this overview of systematic reviews, we analyzed evidence from the first 18 systematic reviews that were published after the emergence of COVID-19. However, confidence in the results of all reviews was “critically low”. Thus, systematic reviews that were published early on in the pandemic were of questionable usefulness. Even during public health emergencies, studies and systematic reviews should adhere to established methodological standards.

Peer Review reports

The spread of the “Severe Acute Respiratory Coronavirus 2” (SARS-CoV-2), the causal agent of COVID-19, was characterized as a pandemic by the World Health Organization (WHO) in March 2020 and has triggered an international public health emergency [ 1 ]. The numbers of confirmed cases and deaths due to COVID-19 are rapidly escalating, counting in millions [ 2 ], causing massive economic strain, and escalating healthcare and public health expenses [ 3 , 4 ].

The research community has responded by publishing an impressive number of scientific reports related to COVID-19. The world was alerted to the new disease at the beginning of 2020 [ 1 ], and by mid-March 2020, more than 2000 articles had been published on COVID-19 in scholarly journals, with 25% of them containing original data [ 5 ]. The living map of COVID-19 evidence, curated by the Evidence for Policy and Practice Information and Co-ordinating Centre (EPPI-Centre), contained more than 40,000 records by February 2021 [ 6 ]. More than 100,000 records on PubMed were labeled as “SARS-CoV-2 literature, sequence, and clinical content” by February 2021 [ 7 ].

Due to publication speed, the research community has voiced concerns regarding the quality and reproducibility of evidence produced during the COVID-19 pandemic, warning of the potential damaging approach of “publish first, retract later” [ 8 ]. It appears that these concerns are not unfounded, as it has been reported that COVID-19 articles were overrepresented in the pool of retracted articles in 2020 [ 9 ]. These concerns about inadequate evidence are of major importance because they can lead to poor clinical practice and inappropriate policies [ 10 ].

Systematic reviews are a cornerstone of today’s evidence-informed decision-making. By synthesizing all relevant evidence regarding a particular topic, systematic reviews reflect the current scientific knowledge. Systematic reviews are considered to be at the highest level in the hierarchy of evidence and should be used to make informed decisions. However, with high numbers of systematic reviews of different scope and methodological quality being published, overviews of multiple systematic reviews that assess their methodological quality are essential [ 11 , 12 , 13 ]. An overview of systematic reviews helps identify and organize the literature and highlights areas of priority in decision-making.

In this overview of systematic reviews, we aimed to summarize and critically appraise systematic reviews of coronavirus disease (COVID-19) in humans that were available at the beginning of the pandemic.

Methodology

Research question.

This overview’s primary objective was to summarize and critically appraise systematic reviews that assessed any type of primary clinical data from patients infected with SARS-CoV-2. Our research question was purposefully broad because we wanted to analyze as many systematic reviews as possible that were available early following the COVID-19 outbreak.

Study design

We conducted an overview of systematic reviews. The idea for this overview originated in a protocol for a systematic review submitted to PROSPERO (CRD42020170623), which indicated a plan to conduct an overview.

Overviews of systematic reviews use explicit and systematic methods for searching and identifying multiple systematic reviews addressing related research questions in the same field to extract and analyze evidence across important outcomes. Overviews of systematic reviews are in principle similar to systematic reviews of interventions, but the unit of analysis is a systematic review [ 14 , 15 , 16 ].

We used the overview methodology instead of other evidence synthesis methods to allow us to collate and appraise multiple systematic reviews on this topic, and to extract and analyze their results across relevant topics [ 17 ]. The overview and meta-analysis of systematic reviews allowed us to investigate the methodological quality of included studies, summarize results, and identify specific areas of available or limited evidence, thereby strengthening the current understanding of this novel disease and guiding future research [ 13 ].

A reporting guideline for overviews of reviews is currently under development, i.e., Preferred Reporting Items for Overviews of Reviews (PRIOR) [ 18 ]. As the PRIOR checklist is still not published, this study was reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2009 statement [ 19 ]. The methodology used in this review was adapted from the Cochrane Handbook for Systematic Reviews of Interventions and also followed established methodological considerations for analyzing existing systematic reviews [ 14 ].

Approval of a research ethics committee was not necessary as the study analyzed only publicly available articles.

Eligibility criteria

Systematic reviews were included if they analyzed primary data from patients infected with SARS-CoV-2 as confirmed by RT-PCR or another pre-specified diagnostic technique. Eligible reviews covered all topics related to COVID-19 including, but not limited to, those that reported clinical symptoms, diagnostic methods, therapeutic interventions, laboratory findings, or radiological results. Both full manuscripts and abbreviated versions, such as letters, were eligible.

No restrictions were imposed on the design of the primary studies included within the systematic reviews, the last search date, whether the review included meta-analyses or language. Reviews related to SARS-CoV-2 and other coronaviruses were eligible, but from those reviews, we analyzed only data related to SARS-CoV-2.

No consensus definition exists for a systematic review [ 20 ], and debates continue about the defining characteristics of a systematic review [ 21 ]. Cochrane’s guidance for overviews of reviews recommends setting pre-established criteria for making decisions around inclusion [ 14 ]. That is supported by a recent scoping review about guidance for overviews of systematic reviews [ 22 ].

Thus, for this study, we defined a systematic review as a research report which searched for primary research studies on a specific topic using an explicit search strategy, had a detailed description of the methods with explicit inclusion criteria provided, and provided a summary of the included studies either in narrative or quantitative format (such as a meta-analysis). Cochrane and non-Cochrane systematic reviews were considered eligible for inclusion, with or without meta-analysis, and regardless of the study design, language restriction and methodology of the included primary studies. To be eligible for inclusion, reviews had to be clearly analyzing data related to SARS-CoV-2 (associated or not with other viruses). We excluded narrative reviews without those characteristics as these are less likely to be replicable and are more prone to bias.

Scoping reviews and rapid reviews were eligible for inclusion in this overview if they met our pre-defined inclusion criteria noted above. We included reviews that addressed SARS-CoV-2 and other coronaviruses if they reported separate data regarding SARS-CoV-2.

Information sources

Nine databases were searched for eligible records published between December 1, 2019, and March 24, 2020: Cochrane Database of Systematic Reviews via Cochrane Library, PubMed, EMBASE, CINAHL (Cumulative Index to Nursing and Allied Health Literature), Web of Sciences, LILACS (Latin American and Caribbean Health Sciences Literature), PDQ-Evidence, WHO’s Global Research on Coronavirus Disease (COVID-19), and Epistemonikos.

The comprehensive search strategy for each database is provided in Additional file 1 and was designed and conducted in collaboration with an information specialist. All retrieved records were primarily processed in EndNote, where duplicates were removed, and records were then imported into the Covidence platform [ 23 ]. In addition to database searches, we screened reference lists of reviews included after screening records retrieved via databases.

Study selection

All searches, screening of titles and abstracts, and record selection, were performed independently by two investigators using the Covidence platform [ 23 ]. Articles deemed potentially eligible were retrieved for full-text screening carried out independently by two investigators. Discrepancies at all stages were resolved by consensus. During the screening, records published in languages other than English were translated by a native/fluent speaker.

Data collection process

We custom designed a data extraction table for this study, which was piloted by two authors independently. Data extraction was performed independently by two authors. Conflicts were resolved by consensus or by consulting a third researcher.

We extracted the following data: article identification data (authors’ name and journal of publication), search period, number of databases searched, population or settings considered, main results and outcomes observed, and number of participants. From Web of Science (Clarivate Analytics, Philadelphia, PA, USA), we extracted journal rank (quartile) and Journal Impact Factor (JIF).

We categorized the following as primary outcomes: all-cause mortality, need for and length of mechanical ventilation, length of hospitalization (in days), admission to intensive care unit (yes/no), and length of stay in the intensive care unit.

The following outcomes were categorized as exploratory: diagnostic methods used for detection of the virus, male to female ratio, clinical symptoms, pharmacological and non-pharmacological interventions, laboratory findings (full blood count, liver enzymes, C-reactive protein, d-dimer, albumin, lipid profile, serum electrolytes, blood vitamin levels, glucose levels, and any other important biomarkers), and radiological findings (using radiography, computed tomography, magnetic resonance imaging or ultrasound).

We also collected data on reporting guidelines and requirements for the publication of systematic reviews and meta-analyses from journal websites where included reviews were published.

Quality assessment in individual reviews

Two researchers independently assessed the reviews’ quality using the “A MeaSurement Tool to Assess Systematic Reviews 2 (AMSTAR 2)”. We acknowledge that the AMSTAR 2 was created as “a critical appraisal tool for systematic reviews that include randomized or non-randomized studies of healthcare interventions, or both” [ 24 ]. However, since AMSTAR 2 was designed for systematic reviews of intervention trials, and we included additional types of systematic reviews, we adjusted some AMSTAR 2 ratings and reported these in Additional file 2 .

Adherence to each item was rated as follows: yes, partial yes, no, or not applicable (such as when a meta-analysis was not conducted). The overall confidence in the results of the review is rated as “critically low”, “low”, “moderate” or “high”, according to the AMSTAR 2 guidance based on seven critical domains, which are items 2, 4, 7, 9, 11, 13, 15 as defined by AMSTAR 2 authors [ 24 ]. We reported our adherence ratings for transparency of our decision with accompanying explanations, for each item, in each included review.

One of the included systematic reviews was conducted by some members of this author team [ 25 ]. This review was initially assessed independently by two authors who were not co-authors of that review to prevent the risk of bias in assessing this study.

Synthesis of results

For data synthesis, we prepared a table summarizing each systematic review. Graphs illustrating the mortality rate and clinical symptoms were created. We then prepared a narrative summary of the methods, findings, study strengths, and limitations.

For analysis of the prevalence of clinical outcomes, we extracted data on the number of events and the total number of patients to perform proportional meta-analysis using RStudio© software, with the “meta” package (version 4.9–6), using the “metaprop” function for reviews that did not perform a meta-analysis, excluding case studies because of the absence of variance. For reviews that did not perform a meta-analysis, we presented pooled results of proportions with their respective confidence intervals (95%) by the inverse variance method with a random-effects model, using the DerSimonian-Laird estimator for τ 2 . We adjusted data using Freeman-Tukey double arcosen transformation. Confidence intervals were calculated using the Clopper-Pearson method for individual studies. We created forest plots using the RStudio© software, with the “metafor” package (version 2.1–0) and “forest” function.

Managing overlapping systematic reviews

Some of the included systematic reviews that address the same or similar research questions may include the same primary studies in overviews. Including such overlapping reviews may introduce bias when outcome data from the same primary study are included in the analyses of an overview multiple times. Thus, in summaries of evidence, multiple-counting of the same outcome data will give data from some primary studies too much influence [ 14 ]. In this overview, we did not exclude overlapping systematic reviews because, according to Cochrane’s guidance, it may be appropriate to include all relevant reviews’ results if the purpose of the overview is to present and describe the current body of evidence on a topic [ 14 ]. To avoid any bias in summary estimates associated with overlapping reviews, we generated forest plots showing data from individual systematic reviews, but the results were not pooled because some primary studies were included in multiple reviews.

Our search retrieved 1063 publications, of which 175 were duplicates. Most publications were excluded after the title and abstract analysis ( n = 860). Among the 28 studies selected for full-text screening, 10 were excluded for the reasons described in Additional file 3 , and 18 were included in the final analysis (Fig. 1 ) [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. Reference list screening did not retrieve any additional systematic reviews.

PRISMA flow diagram

Characteristics of included reviews

Summary features of 18 systematic reviews are presented in Table 1 . They were published in 14 different journals. Only four of these journals had specific requirements for systematic reviews (with or without meta-analysis): European Journal of Internal Medicine, Journal of Clinical Medicine, Ultrasound in Obstetrics and Gynecology, and Clinical Research in Cardiology . Two journals reported that they published only invited reviews ( Journal of Medical Virology and Clinica Chimica Acta ). Three systematic reviews in our study were published as letters; one was labeled as a scoping review and another as a rapid review (Table 2 ).

All reviews were published in English, in first quartile (Q1) journals, with JIF ranging from 1.692 to 6.062. One review was empty, meaning that its search did not identify any relevant studies; i.e., no primary studies were included [ 36 ]. The remaining 17 reviews included 269 unique studies; the majority ( N = 211; 78%) were included in only a single review included in our study (range: 1 to 12). Primary studies included in the reviews were published between December 2019 and March 18, 2020, and comprised case reports, case series, cohorts, and other observational studies. We found only one review that included randomized clinical trials [ 38 ]. In the included reviews, systematic literature searches were performed from 2019 (entire year) up to March 9, 2020. Ten systematic reviews included meta-analyses. The list of primary studies found in the included systematic reviews is shown in Additional file 4 , as well as the number of reviews in which each primary study was included.

Population and study designs

Most of the reviews analyzed data from patients with COVID-19 who developed pneumonia, acute respiratory distress syndrome (ARDS), or any other correlated complication. One review aimed to evaluate the effectiveness of using surgical masks on preventing transmission of the virus [ 36 ], one review was focused on pediatric patients [ 34 ], and one review investigated COVID-19 in pregnant women [ 37 ]. Most reviews assessed clinical symptoms, laboratory findings, or radiological results.

Systematic review findings

The summary of findings from individual reviews is shown in Table 2 . Overall, all-cause mortality ranged from 0.3 to 13.9% (Fig. 2 ).

A meta-analysis of the prevalence of mortality

Clinical symptoms

Seven reviews described the main clinical manifestations of COVID-19 [ 26 , 28 , 29 , 34 , 35 , 39 , 41 ]. Three of them provided only a narrative discussion of symptoms [ 26 , 34 , 35 ]. In the reviews that performed a statistical analysis of the incidence of different clinical symptoms, symptoms in patients with COVID-19 were (range values of point estimates): fever (82–95%), cough with or without sputum (58–72%), dyspnea (26–59%), myalgia or muscle fatigue (29–51%), sore throat (10–13%), headache (8–12%), gastrointestinal disorders, such as diarrhea, nausea or vomiting (5.0–9.0%), and others (including, in one study only: dizziness 12.1%) (Figs. 3 , 4 , 5 , 6 , 7 , 8 and 9 ). Three reviews assessed cough with and without sputum together; only one review assessed sputum production itself (28.5%).

A meta-analysis of the prevalence of fever

A meta-analysis of the prevalence of cough

A meta-analysis of the prevalence of dyspnea

A meta-analysis of the prevalence of fatigue or myalgia

A meta-analysis of the prevalence of headache

A meta-analysis of the prevalence of gastrointestinal disorders

A meta-analysis of the prevalence of sore throat

Diagnostic aspects

Three reviews described methodologies, protocols, and tools used for establishing the diagnosis of COVID-19 [ 26 , 34 , 38 ]. The use of respiratory swabs (nasal or pharyngeal) or blood specimens to assess the presence of SARS-CoV-2 nucleic acid using RT-PCR assays was the most commonly used diagnostic method mentioned in the included studies. These diagnostic tests have been widely used, but their precise sensitivity and specificity remain unknown. One review included a Chinese study with clinical diagnosis with no confirmation of SARS-CoV-2 infection (patients were diagnosed with COVID-19 if they presented with at least two symptoms suggestive of COVID-19, together with laboratory and chest radiography abnormalities) [ 34 ].

Therapeutic possibilities

Pharmacological and non-pharmacological interventions (supportive therapies) used in treating patients with COVID-19 were reported in five reviews [ 25 , 27 , 34 , 35 , 38 ]. Antivirals used empirically for COVID-19 treatment were reported in seven reviews [ 25 , 27 , 34 , 35 , 37 , 38 , 41 ]; most commonly used were protease inhibitors (lopinavir, ritonavir, darunavir), nucleoside reverse transcriptase inhibitor (tenofovir), nucleotide analogs (remdesivir, galidesivir, ganciclovir), and neuraminidase inhibitors (oseltamivir). Umifenovir, a membrane fusion inhibitor, was investigated in two studies [ 25 , 35 ]. Possible supportive interventions analyzed were different types of oxygen supplementation and breathing support (invasive or non-invasive ventilation) [ 25 ]. The use of antibiotics, both empirically and to treat secondary pneumonia, was reported in six studies [ 25 , 26 , 27 , 34 , 35 , 38 ]. One review specifically assessed evidence on the efficacy and safety of the anti-malaria drug chloroquine [ 27 ]. It identified 23 ongoing trials investigating the potential of chloroquine as a therapeutic option for COVID-19, but no verifiable clinical outcomes data. The use of mesenchymal stem cells, antifungals, and glucocorticoids were described in four reviews [ 25 , 34 , 35 , 38 ].

Laboratory and radiological findings

Of the 18 reviews included in this overview, eight analyzed laboratory parameters in patients with COVID-19 [ 25 , 29 , 30 , 32 , 33 , 34 , 35 , 39 ]; elevated C-reactive protein levels, associated with lymphocytopenia, elevated lactate dehydrogenase, as well as slightly elevated aspartate and alanine aminotransferase (AST, ALT) were commonly described in those eight reviews. Lippi et al. assessed cardiac troponin I (cTnI) [ 25 ], procalcitonin [ 32 ], and platelet count [ 33 ] in COVID-19 patients. Elevated levels of procalcitonin [ 32 ] and cTnI [ 30 ] were more likely to be associated with a severe disease course (requiring intensive care unit admission and intubation). Furthermore, thrombocytopenia was frequently observed in patients with complicated COVID-19 infections [ 33 ].

Chest imaging (chest radiography and/or computed tomography) features were assessed in six reviews, all of which described a frequent pattern of local or bilateral multilobar ground-glass opacity [ 25 , 34 , 35 , 39 , 40 , 41 ]. Those six reviews showed that septal thickening, bronchiectasis, pleural and cardiac effusions, halo signs, and pneumothorax were observed in patients suffering from COVID-19.

Quality of evidence in individual systematic reviews

Table 3 shows the detailed results of the quality assessment of 18 systematic reviews, including the assessment of individual items and summary assessment. A detailed explanation for each decision in each review is available in Additional file 5 .

Using AMSTAR 2 criteria, confidence in the results of all 18 reviews was rated as “critically low” (Table 3 ). Common methodological drawbacks were: omission of prospective protocol submission or publication; use of inappropriate search strategy: lack of independent and dual literature screening and data-extraction (or methodology unclear); absence of an explanation for heterogeneity among the studies included; lack of reasons for study exclusion (or rationale unclear).

Risk of bias assessment, based on a reported methodological tool, and quality of evidence appraisal, in line with the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) method, were reported only in one review [ 25 ]. Five reviews presented a table summarizing bias, using various risk of bias tools [ 25 , 29 , 39 , 40 , 41 ]. One review analyzed “study quality” [ 37 ]. One review mentioned the risk of bias assessment in the methodology but did not provide any related analysis [ 28 ].

This overview of systematic reviews analyzed the first 18 systematic reviews published after the onset of the COVID-19 pandemic, up to March 24, 2020, with primary studies involving more than 60,000 patients. Using AMSTAR-2, we judged that our confidence in all those reviews was “critically low”. Ten reviews included meta-analyses. The reviews presented data on clinical manifestations, laboratory and radiological findings, and interventions. We found no systematic reviews on the utility of diagnostic tests.

Symptoms were reported in seven reviews; most of the patients had a fever, cough, dyspnea, myalgia or muscle fatigue, and gastrointestinal disorders such as diarrhea, nausea, or vomiting. Olfactory dysfunction (anosmia or dysosmia) has been described in patients infected with COVID-19 [ 43 ]; however, this was not reported in any of the reviews included in this overview. During the SARS outbreak in 2002, there were reports of impairment of the sense of smell associated with the disease [ 44 , 45 ].

The reported mortality rates ranged from 0.3 to 14% in the included reviews. Mortality estimates are influenced by the transmissibility rate (basic reproduction number), availability of diagnostic tools, notification policies, asymptomatic presentations of the disease, resources for disease prevention and control, and treatment facilities; variability in the mortality rate fits the pattern of emerging infectious diseases [ 46 ]. Furthermore, the reported cases did not consider asymptomatic cases, mild cases where individuals have not sought medical treatment, and the fact that many countries had limited access to diagnostic tests or have implemented testing policies later than the others. Considering the lack of reviews assessing diagnostic testing (sensitivity, specificity, and predictive values of RT-PCT or immunoglobulin tests), and the preponderance of studies that assessed only symptomatic individuals, considerable imprecision around the calculated mortality rates existed in the early stage of the COVID-19 pandemic.

Few reviews included treatment data. Those reviews described studies considered to be at a very low level of evidence: usually small, retrospective studies with very heterogeneous populations. Seven reviews analyzed laboratory parameters; those reviews could have been useful for clinicians who attend patients suspected of COVID-19 in emergency services worldwide, such as assessing which patients need to be reassessed more frequently.

All systematic reviews scored poorly on the AMSTAR 2 critical appraisal tool for systematic reviews. Most of the original studies included in the reviews were case series and case reports, impacting the quality of evidence. Such evidence has major implications for clinical practice and the use of these reviews in evidence-based practice and policy. Clinicians, patients, and policymakers can only have the highest confidence in systematic review findings if high-quality systematic review methodologies are employed. The urgent need for information during a pandemic does not justify poor quality reporting.

We acknowledge that there are numerous challenges associated with analyzing COVID-19 data during a pandemic [ 47 ]. High-quality evidence syntheses are needed for decision-making, but each type of evidence syntheses is associated with its inherent challenges.

The creation of classic systematic reviews requires considerable time and effort; with massive research output, they quickly become outdated, and preparing updated versions also requires considerable time. A recent study showed that updates of non-Cochrane systematic reviews are published a median of 5 years after the publication of the previous version [ 48 ].

Authors may register a review and then abandon it [ 49 ], but the existence of a public record that is not updated may lead other authors to believe that the review is still ongoing. A quarter of Cochrane review protocols remains unpublished as completed systematic reviews 8 years after protocol publication [ 50 ].

Rapid reviews can be used to summarize the evidence, but they involve methodological sacrifices and simplifications to produce information promptly, with inconsistent methodological approaches [ 51 ]. However, rapid reviews are justified in times of public health emergencies, and even Cochrane has resorted to publishing rapid reviews in response to the COVID-19 crisis [ 52 ]. Rapid reviews were eligible for inclusion in this overview, but only one of the 18 reviews included in this study was labeled as a rapid review.

Ideally, COVID-19 evidence would be continually summarized in a series of high-quality living systematic reviews, types of evidence synthesis defined as “ a systematic review which is continually updated, incorporating relevant new evidence as it becomes available ” [ 53 ]. However, conducting living systematic reviews requires considerable resources, calling into question the sustainability of such evidence synthesis over long periods [ 54 ].

Research reports about COVID-19 will contribute to research waste if they are poorly designed, poorly reported, or simply not necessary. In principle, systematic reviews should help reduce research waste as they usually provide recommendations for further research that is needed or may advise that sufficient evidence exists on a particular topic [ 55 ]. However, systematic reviews can also contribute to growing research waste when they are not needed, or poorly conducted and reported. Our present study clearly shows that most of the systematic reviews that were published early on in the COVID-19 pandemic could be categorized as research waste, as our confidence in their results is critically low.

Our study has some limitations. One is that for AMSTAR 2 assessment we relied on information available in publications; we did not attempt to contact study authors for clarifications or additional data. In three reviews, the methodological quality appraisal was challenging because they were published as letters, or labeled as rapid communications. As a result, various details about their review process were not included, leading to AMSTAR 2 questions being answered as “not reported”, resulting in low confidence scores. Full manuscripts might have provided additional information that could have led to higher confidence in the results. In other words, low scores could reflect incomplete reporting, not necessarily low-quality review methods. To make their review available more rapidly and more concisely, the authors may have omitted methodological details. A general issue during a crisis is that speed and completeness must be balanced. However, maintaining high standards requires proper resourcing and commitment to ensure that the users of systematic reviews can have high confidence in the results.

Furthermore, we used adjusted AMSTAR 2 scoring, as the tool was designed for critical appraisal of reviews of interventions. Some reviews may have received lower scores than actually warranted in spite of these adjustments.

Another limitation of our study may be the inclusion of multiple overlapping reviews, as some included reviews included the same primary studies. According to the Cochrane Handbook, including overlapping reviews may be appropriate when the review’s aim is “ to present and describe the current body of systematic review evidence on a topic ” [ 12 ], which was our aim. To avoid bias with summarizing evidence from overlapping reviews, we presented the forest plots without summary estimates. The forest plots serve to inform readers about the effect sizes for outcomes that were reported in each review.

Several authors from this study have contributed to one of the reviews identified [ 25 ]. To reduce the risk of any bias, two authors who did not co-author the review in question initially assessed its quality and limitations.

Finally, we note that the systematic reviews included in our overview may have had issues that our analysis did not identify because we did not analyze their primary studies to verify the accuracy of the data and information they presented. We give two examples to substantiate this possibility. Lovato et al. wrote a commentary on the review of Sun et al. [ 41 ], in which they criticized the authors’ conclusion that sore throat is rare in COVID-19 patients [ 56 ]. Lovato et al. highlighted that multiple studies included in Sun et al. did not accurately describe participants’ clinical presentations, warning that only three studies clearly reported data on sore throat [ 56 ].

In another example, Leung [ 57 ] warned about the review of Li, L.Q. et al. [ 29 ]: “ it is possible that this statistic was computed using overlapped samples, therefore some patients were double counted ”. Li et al. responded to Leung that it is uncertain whether the data overlapped, as they used data from published articles and did not have access to the original data; they also reported that they requested original data and that they plan to re-do their analyses once they receive them; they also urged readers to treat the data with caution [ 58 ]. This points to the evolving nature of evidence during a crisis.

Our study’s strength is that this overview adds to the current knowledge by providing a comprehensive summary of all the evidence synthesis about COVID-19 available early after the onset of the pandemic. This overview followed strict methodological criteria, including a comprehensive and sensitive search strategy and a standard tool for methodological appraisal of systematic reviews.

In conclusion, in this overview of systematic reviews, we analyzed evidence from the first 18 systematic reviews that were published after the emergence of COVID-19. However, confidence in the results of all the reviews was “critically low”. Thus, systematic reviews that were published early on in the pandemic could be categorized as research waste. Even during public health emergencies, studies and systematic reviews should adhere to established methodological standards to provide patients, clinicians, and decision-makers trustworthy evidence.

Availability of data and materials

All data collected and analyzed within this study are available from the corresponding author on reasonable request.

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Acknowledgments

We thank Catherine Henderson DPhil from Swanscoe Communications for pro bono medical writing and editing support. We acknowledge support from the Covidence Team, specifically Anneliese Arno. We thank the whole International Network of Coronavirus Disease 2019 (InterNetCOVID-19) for their commitment and involvement. Members of the InterNetCOVID-19 are listed in Additional file 6 . We thank Pavel Cerny and Roger Crosthwaite for guiding the team supervisor (IJBN) on human resources management.

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Israel Júnior Borges do Nascimento & Milena Soriano Marcolino

Medical College of Wisconsin, Milwaukee, WI, USA

Israel Júnior Borges do Nascimento

Helene Fuld Health Trust National Institute for Evidence-based Practice in Nursing and Healthcare, College of Nursing, The Ohio State University, Columbus, OH, USA

Dónal P. O’Mathúna

School of Nursing, Psychotherapy and Community Health, Dublin City University, Dublin, Ireland

Department of Anesthesiology, Intensive Care and Pain Medicine, University of Münster, Münster, Germany

Thilo Caspar von Groote

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Hebatullah Mohamed Abdulazeem

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Livia Puljak

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IJBN conceived the research idea and worked as a project coordinator. DPOM, TCVG, HMA, IW, AM, LP, VTC, IZG, TPP, ANA, SF, NLB and MSM were involved in data curation, formal analysis, investigation, methodology, and initial draft writing. All authors revised the manuscript critically for the content. The author(s) read and approved the final manuscript.

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Supplementary Information

Additional file 1: appendix 1..

Search strategies used in the study.

Additional file 2: Appendix 2.

Adjusted scoring of AMSTAR 2 used in this study for systematic reviews of studies that did not analyze interventions.

Additional file 3: Appendix 3.

List of excluded studies, with reasons.

Additional file 4: Appendix 4.

Table of overlapping studies, containing the list of primary studies included, their visual overlap in individual systematic reviews, and the number in how many reviews each primary study was included.

Additional file 5: Appendix 5.

A detailed explanation of AMSTAR scoring for each item in each review.

Additional file 6: Appendix 6.

List of members and affiliates of International Network of Coronavirus Disease 2019 (InterNetCOVID-19).

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Borges do Nascimento, I.J., O’Mathúna, D.P., von Groote, T.C. et al. Coronavirus disease (COVID-19) pandemic: an overview of systematic reviews. BMC Infect Dis 21 , 525 (2021). https://doi.org/10.1186/s12879-021-06214-4

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DOI : https://doi.org/10.1186/s12879-021-06214-4

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What you need to know

SARS-CoV-2 is genetically similar to SARS-CoV-1, but characteristics of SARS-CoV-2—eg, structural differences in its surface proteins and viral load kinetics—may help explain its enhanced rate of transmission

In the respiratory tract, peak SARS-CoV-2 load is observed at the time of symptom onset or in the first week of illness, with subsequent decline thereafter, indicating the highest infectiousness potential just before or within the first five days of symptom onset

Reverse transcription polymerase chain reaction (RT-PCR) tests can detect viral SARS-CoV-2 RNA in the upper respiratory tract for a mean of 17 days; however, detection of viral RNA does not necessarily equate to infectiousness, and viral culture from PCR positive upper respiratory tract samples has been rarely positive beyond nine days of illness

Symptomatic and pre-symptomatic transmission (1-2 days before symptom onset), is likely to play a greater role in the spread of SARS-CoV-2 than asymptomatic transmission

A wide range of virus-neutralising antibodies have been reported, and emerging evidence suggests that these may correlate with severity of illness but wane over time

Since the emergence of SARS-CoV-2 in December 2019, there has been an unparalleled global effort to characterise the virus and the clinical course of disease. Coronavirus disease 2019 (covid-19), caused by SARS-CoV-2, follows a biphasic pattern of illness that likely results from the combination of an early viral response phase and an inflammatory second phase. Most clinical presentations are mild, and the typical pattern of covid-19 more resembles an influenza-like illness—which includes fever, cough, malaise, myalgia, headache, and taste and smell disturbance—rather than severe pneumonia (although emerging evidence about long term consequences is yet to be understood in detail). 1 In this review, we provide a broad update on the emerging understanding of SARS-CoV-2 pathophysiology, including virology, transmission dynamics, and the immune response to the virus. Any of the mechanisms and assumptions discussed in the article and in our understanding of covid-19 may be revised as further evidence emerges.

What we know about the virus

SARS-CoV-2 is an enveloped β-coronavirus, with a genetic sequence very similar to SARS-CoV-1 (80%) and bat coronavirus RaTG13 (96.2%). 2 The viral envelope is coated by spike (S) glycoprotein, envelope (E), and membrane (M) proteins ( fig 1 ). Host cell binding and entry are mediated by the S protein. The first step in infection is virus binding to a host cell through its target receptor. The S1 sub-unit of the S protein contains the receptor binding domain that binds to the peptidase domain of angiotensin-converting enzyme 2 (ACE 2). In SARS-CoV-2 the S2 sub-unit is highly preserved and is considered a potential antiviral target. The virus structure and replication cycle are described in figure 1 .

(1) The virus binds to ACE 2 as the host target cell receptor in synergy with the host’s transmembrane serine protease 2 (cell surface protein), which is principally expressed in the airway epithelial cells and vascular endothelial cells. This leads to membrane fusion and releases the viral genome into the host cytoplasm (2). Stages (3-7) show the remaining steps of viral replication, leading to viral assembly, maturation, and virus release

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Coronaviruses have the capacity for proofreading during replication, and therefore mutation rates are lower than in other RNA viruses. As SARS-CoV-2 has spread globally it has, like other viruses, accumulated some mutations in the viral genome, which contains geographic signatures. Researchers have examined these mutations to study virus characterisation and understand epidemiology and transmission patterns. In general, the mutations have not been attributed to phenotypic changes affecting viral transmissibility or pathogenicity. The G614 variant in the S protein has been postulated to increase infectivity and transmissibility of the virus. 3 Higher viral loads were reported in clinical samples with virus containing G614 than previously circulating variant D614, although no association was made with severity of illness as measured by hospitalisation outcomes. 3 These findings have yet to be confirmed with regards to natural infection.

Why is SARS-CoV-2 more infectious than SARS-CoV-1?

SARS-CoV-2 has a higher reproductive number (R 0 ) than SARS-CoV-1, indicating much more efficient spread. 1 Several characteristics of SARS-CoV-2 may help explain this enhanced transmission. While both SARS-CoV-1 and SARS-CoV-2 preferentially interact with the angiotensin-converting enzyme 2 (ACE 2) receptor, SARS-CoV-2 has structural differences in its surface proteins that enable stronger binding to the ACE 2 receptor 4 and greater efficiency at invading host cells. 1 SARS-CoV-2 also has greater affinity (or bonding) for the upper respiratory tract and conjunctiva, 5 thus can infect the upper respiratory tract and can conduct airways more easily. 6

Viral load dynamics and duration of infectiousness

Viral load kinetics could also explain some of the differences between SARS-CoV-2 and SARS-CoV-1. In the respiratory tract, peak SARS-CoV-2 load is observed at the time of symptom onset or in the first week of illness, with subsequent decline thereafter, which indicates the highest infectiousness potential just before or within the first five days of symptom onset ( fig 2 ). 7 In contrast, in SARS-CoV-1 the highest viral loads were detected in the upper respiratory tract in the second week of illness, which explains its minimal contagiousness in the first week after symptom onset, enabling early case detection in the community. 7

After the initial exposure, patients typically develop symptoms within 5-6 days (incubation period). SARS-CoV-2 generates a diverse range of clinical manifestations, ranging from mild infection to severe disease accompanied by high mortality. In patients with mild infection, initial host immune response is capable of controlling the infection. In severe disease, excessive immune response leads to organ damage, intensive care admission, or death. The viral load peaks in the first week of infection, declines thereafter gradually, while the antibody response gradually increases and is often detectable by day 14 (figure adapted with permission from https://www.sciencedirect.com/science/article/pii/S009286742030475X ; https://www.thelancet.com/journals/lanres/article/PIIS2213-2600(20)30230-7/fulltext )

Quantitative reverse transcription polymerase chain reaction (qRT-PCR) technology can detect viral SARS-CoV-2 RNA in the upper respiratory tract for a mean of 17 days (maximum 83 days) after symptom onset. 7 However, detection of viral RNA by qRT-PCR does not necessarily equate to infectiousness, and viral culture from PCR positive upper respiratory tract samples has been rarely positive beyond nine days of illness. 5 This corresponds to what is known about transmission based on contact tracing studies, which is that transmission capacity is maximal in the first week of illness, and that transmission after this period has not been documented. 8 Severely ill or immune-compromised patients may have relatively prolonged virus shedding, and some patients may have intermittent RNA shedding; however, low level results close to the detection limit may not constitute infectious viral particles. While asymptomatic individuals (those with no symptoms throughout the infection) can transmit the infection, their relative degree of infectiousness seems to be limited. 9 10 11 People with mild symptoms (paucisymptomatic) and those whose symptom have not yet appeared still carry large amounts of virus in the upper respiratory tract, which might contribute to the easy and rapid spread of SARS-CoV-2. 7 Symptomatic and pre-symptomatic transmission (one to two days before symptom onset) is likely to play a greater role in the spread of SARS-CoV-2. 10 12 A combination of preventive measures, such as physical distancing and testing, tracing, and self-isolation, continue to be needed.

Route of transmission and transmission dynamics

Like other coronaviruses, the primary mechanism of transmission of SARS-CoV-2 is via infected respiratory droplets, with viral infection occurring by direct or indirect contact with nasal, conjunctival, or oral mucosa, when respiratory particles are inhaled or deposited on these mucous membranes. 6 Target host receptors are found mainly in the human respiratory tract epithelium, including the oropharynx and upper airway. The conjunctiva and gastrointestinal tracts are also susceptible to infection and may serve as transmission portals. 6

Transmission risk depends on factors such as contact pattern, environment, infectiousness of the host, and socioeconomic factors, as described elsewhere. 12 Most transmission occurs through close range contact (such as 15 minutes face to face and within 2 m), 13 and spread is especially efficient within households and through gatherings of family and friends. 12 Household secondary attack rates (the proportion of susceptible individuals who become infected within a group of susceptible contacts with a primary case) ranges from 4% to 35%. 12 Sleeping in the same room as, or being a spouse of an infected individual increases the risk of infection, but isolation of the infected person away from the family is related to lower risk of infection. 12 Other activities identified as high risk include dining in close proximity with the infected person, sharing food, and taking part in group activities 12 The risk of infection substantially increases in enclosed environments compared with outdoor settings. 12 For example, a systematic review of transmission clusters found that most superspreading events occurred indoors. 11 Aerosol transmission can still factor during prolonged stay in crowded, poorly ventilated indoor settings (meaning transmission could occur at a distance >2 m). 12 14 15 16 17

The role of faecal shedding in SARS-CoV-2 transmission and the extent of fomite (through inanimate surfaces) transmission also remain to be fully understood. Both SARS-CoV-2 and SARS-CoV-1 remain viable for many days on smooth surfaces (stainless steel, plastic, glass) and at lower temperature and humidity (eg, air conditioned environments). 18 19 Thus, transferring infection from contaminated surfaces to the mucosa of eyes, nose, and mouth via unwashed hands is a possible route of transmission. This route of transmission may contribute especially in facilities with communal areas, with increased likelihood of environmental contamination. However, both SARS-CoV-1 and SARS-CoV-2 are readily inactivated by commonly used disinfectants, emphasising the potential value of surface cleaning and handwashing. SARS-CoV-2 RNA has been found in stool samples and RNA shedding often persists for longer than in respiratory samples 7 ; however, virus isolation has rarely been successful from the stool. 5 7 No published reports describe faecal-oral transmission. In SARS-CoV-1, faecal-oral transmission was not considered to occur in most circumstances; but, one explosive outbreak was attributed to aerosolisation and spread of the virus across an apartment block via a faulty sewage system. 20 It remains to be seen if similar transmission may occur with SARS-CoV-2.

Pathogenesis

Viral entry and interaction with target cells.

SARS-CoV-2 binds to ACE 2, the host target cell receptor. 1 Active replication and release of the virus in the lung cells lead to non-specific symptoms such as fever, myalgia, headache, and respiratory symptoms. 1 In an experimental hamster model, the virus causes transient damage to the cells in the olfactory epithelium, leading to olfactory dysfunction, which may explain temporary loss of taste and smell commonly seen in covid-19. 21 The distribution of ACE 2 receptors in different tissues may explain the sites of infection and patient symptoms. For example, the ACE 2 receptor is found on the epithelium of other organs such as the intestine and endothelial cells in the kidney and blood vessels, which may explain gastrointestinal symptoms and cardiovascular complications. 22 Lymphocytic endotheliitis has been observed in postmortem pathology examination of the lung, heart, kidney, and liver as well as liver cell necrosis and myocardial infarction in patients who died of covid-19. 1 23 These findings indicate that the virus directly affects many organs, as was seen in SARS-CoV-1 and influenzae.

Much remains unknown. Are the pathological changes in the respiratory tract or endothelial dysfunction the result of direct viral infection, cytokine dysregulation, coagulopathy, or are they multifactorial? And does direct viral invasion or coagulopathy directly contribute to some of the ischaemic complications such as ischaemic infarcts? These and more, will require further work to elucidate.

Immune response and disease spectrum ( figure 2 )

After viral entry, the initial inflammatory response attracts virus-specific T cells to the site of infection, where the infected cells are eliminated before the virus spreads, leading to recovery in most people. 24 In patients who develop severe disease, SARS-CoV-2 elicits an aberrant host immune response. 24 25 For example, postmortem histology of lung tissues of patients who died of covid-19 have confirmed the inflammatory nature of the injury, with features of bilateral diffuse alveolar damage, hyaline-membrane formation, interstitial mononuclear inflammatory infiltrates, and desquamation consistent with acute respiratory distress syndrome (ARDS), and is similar to the lung pathology seen in severe Middle East respiratory syndrome (MERS) and severe acute respiratory syndrome (SARS). 26 27 A distinctive feature of covid-19 is the presence of mucus plugs with fibrinous exudate in the respiratory tract, which may explain the severity of covid-19 even in young adults. 28 This is potentially caused by the overproduction of pro-inflammatory cytokines that accumulate in the lungs, eventually damaging the lung parenchyma. 24

Some patients also experience septic shock and multi-organ dysfunction. 24 For example, the cardiovascular system is often involved early in covid-19 disease and is reflected in the release of highly sensitive troponin and natriuretic peptides. 29 Consistent with the clinical context of coagulopathy, focal intra-alveolar haemorrhage and presence of platelet-fibrin thrombi in small arterial vessels is also seen. 27 Cytokines normally mediate and regulate immunity, inflammation, and haematopoiesis; however, further exacerbation of immune reaction and accumulation of cytokines in other organs in some patients may cause extensive tissue damage, or a cytokine release syndrome (cytokine storm), resulting in capillary leak, thrombus formation, and organ dysfunction. 24 30

Mechanisms underlying the diverse clinical outcomes

Clinical outcomes are influenced by host factors such as older age, male sex, and underlying medical conditions, 1 as well as factors related to the virus (such as viral load kinetics), host-immune response, and potential cross-reactive immune memory from previous exposure to seasonal coronaviruses ( box 1 ).

Risk factors associated with the development of severe disease, admission to intensive care unit, and mortality

Underlying condition.

Hypertension

Cardiovascular disease

Chronic obstructive pulmonary disease

Presentation

Higher fever (≥39°C on admission)

Dyspnoea on admission

Higher qSOFA score

Laboratory markers

Neutrophilia/lymphopenia

Raised lactate and lactate dehydrogenase

Raised C reactive protein

Raised ferritin

Raised IL-6

Raised ACE2

D-dimer >1 μg/mL

Sex-related differences in immune response have been reported, revealing that men had higher plasma innate immune cytokines and chemokines at baseline than women. 31 In contrast, women had notably more robust T cell activation than men, and among male participants T cell activation declined with age, which was sustained among female patients. These findings suggest that adaptive immune response may be important in defining the clinical outcome as older age and male sex is associated with increased risk of severe disease and mortality.

Increased levels of pro-inflammatory cytokines correlate with severe pneumonia and increased ground glass opacities within the lungs. 30 32 In people with severe illness, increased plasma concentrations of inflammatory cytokines and biomarkers were observed compared with people with non-severe illness. 30 33 34

Emerging evidence suggests a correlation between viral dynamics, the severity of illness, and disease outcome. 7 Longitudinal characteristics of immune response show a correlation between the severity of illness, viral load, and IFN- α, IFN-γ, and TNF-α response. 34 In the same study many interferons, cytokines, and chemokines were elevated early in disease for patients who had severe disease and higher viral loads. This emphasises that viral load may drive these cytokines and the possible pathological roles associated with the host defence factors. This is in keeping with the pathogenesis of influenza, SARS, and MERS whereby prolonged viral shedding was also associated with severity of illness. 7 35

Given the substantial role of the immune response in determining clinical outcomes, several immunosuppressive therapies aimed at limiting immune-mediated damage are currently in various phases of development ( table 1 ).

Therapeutics currently under investigation

• View inline

Immune response to the virus and its role in protection

Covid-19 leads to an antibody response to a range of viral proteins, but the spike (S) protein and nucleocapsid are those most often used in serological diagnosis. Few antibodies are detectable in the first four days of illness, but patients progressively develop them, with most achieving a detectable response after four weeks. 36 A wide range of virus-neutralising antibodies have been reported, and emerging evidence suggests that these may correlate with severity but wane over time. 37 The duration and protectivity of antibody and T cell responses remain to be defined through studies with longer follow-up. CD-4 T cell responses to endemic human coronaviruses appear to manifest cross-reactivity with SARS-CoV-2, but their role in protection remains unclear. 38

Unanswered questions

Further understanding of the pathogenesis for SARS-CoV-2 will be vital in developing therapeutics, vaccines, and supportive care modalities in the treatment of covid-19. More data are needed to understand the determinants of healthy versus dysfunctional response and immune markers for protection and the severity of disease. Neutralising antibodies are potential correlates of protection, but other protective antibody mechanisms may exist. Similarly, the protective role of T cell immunity and duration of both antibody and T cell responses and the correlates of protection need to be defined. In addition, we need optimal testing systems and technologies to support and inform early detection and clinical management of infection. Greater understanding is needed regarding the long term consequences following acute illness and multisystem inflammatory disease, especially in children.

Education into practice

How would you describe SARS-CoV-2 transmission routes and ways to prevent infection?

How would you describe to a patient why cough, anosmia, and fever occur in covid-19?

Questions for future research

What is the role of the cytokine storm and how could it inform the development of therapeutics, vaccines, and supportive care modalities?

What is the window period when patients are most infectious?

Why do some patients develop severe disease while others, especially children, remain mildly symptomatic or do not develop symptoms?

What are the determinants of healthy versus dysfunctional response, and the biomarkers to define immune correlates of protection and disease severity for the effective triage of patients?

What is the protective role of T cell immunity and duration of both antibody and T cell responses, and how would you define the correlates of protection?

How patients were involved in the creation of this article

No patients were directly involved in the creation of this article.

How this article was created

We searched PubMed from 2000 to 18 September 2020, limited to publications in English. Our search strategy used a combination of key words including “COVID-19,” “SARS-CoV-2,” “SARS”, “MERS,” “Coronavirus,” “Novel Coronavirus,” “Pathogenesis,” “Transmission,” “Cytokine Release,” “immune response,” “antibody response.” These sources were supplemented with systematic reviews. We also reviewed technical documents produced by the Centers for Disease Control and Prevention and World Health Organization technical documents.

Author contributions: MC, KK, JK, MP drafted the first and subsequent versions of the manuscript and all authors provided critical feedback and contributed to the manuscript.

Competing interests The BMJ has judged that there are no disqualifying financial ties to commercial companies. The authors declare the following other interests: none.

Further details of The BMJ policy on financial interests are here: https://www.bmj.com/about-bmj/resources-authors/forms-policies-and-checklists/declaration-competing-interests

Provenance and peer review: commissioned; externally peer reviewed.

This article is made freely available for use in accordance with BMJ's website terms and conditions for the duration of the covid-19 pandemic or until otherwise determined by BMJ. You may use, download and print the article for any lawful, non-commercial purpose (including text and data mining) provided that all copyright notices and trade marks are retained.

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Coronavirus disease 2019 (COVID-19): A literature review

Affiliations.

• 1 Medical Research Unit, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Tropical Disease Centre, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Department of Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia. Electronic address: [email protected].
• 2 Division of Infectious Diseases, AichiCancer Center Hospital, Chikusa-ku Nagoya, Japan. Electronic address: [email protected].
• 3 Department of Family Medicine, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia. Electronic address: [email protected].
• 4 Department of Pulmonology and Respiratory Medicine, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia. Electronic address: [email protected].
• 5 School of Medicine, The University of Western Australia, Perth, Australia. Electronic address: [email protected].
• 6 Siem Reap Provincial Health Department, Ministry of Health, Siem Reap, Cambodia. Electronic address: [email protected].
• 7 Department of Microbiology and Parasitology, Faculty of Medicine and Health Sciences, Warmadewa University, Denpasar, Indonesia; Department of Medical Microbiology and Immunology, University of California, Davis, CA, USA. Electronic address: [email protected].
• 8 Medical Research Unit, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Tropical Disease Centre, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Department of Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Department of Clinical Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia. Electronic address: [email protected].
• 9 Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, MI 48109, USA. Electronic address: [email protected].
• 10 Medical Research Unit, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Tropical Disease Centre, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Department of Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia. Electronic address: [email protected].
• PMID: 32340833
• PMCID: PMC7142680
• DOI: 10.1016/j.jiph.2020.03.019

In early December 2019, an outbreak of coronavirus disease 2019 (COVID-19), caused by a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), occurred in Wuhan City, Hubei Province, China. On January 30, 2020 the World Health Organization declared the outbreak as a Public Health Emergency of International Concern. As of February 14, 2020, 49,053 laboratory-confirmed and 1,381 deaths have been reported globally. Perceived risk of acquiring disease has led many governments to institute a variety of control measures. We conducted a literature review of publicly available information to summarize knowledge about the pathogen and the current epidemic. In this literature review, the causative agent, pathogenesis and immune responses, epidemiology, diagnosis, treatment and management of the disease, control and preventions strategies are all reviewed.

Keywords: 2019-nCoV; COVID-19; Novel coronavirus; Outbreak; SARS-CoV-2.

Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.

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• COVID-19 pandemic and Internal Medicine Units in Italy: a precious effort on the front line. Montagnani A, Pieralli F, Gnerre P, Vertulli C, Manfellotto D; FADOI COVID-19 Observatory Group. Montagnani A, et al. Intern Emerg Med. 2020 Nov;15(8):1595-1597. doi: 10.1007/s11739-020-02454-5. Epub 2020 Jul 31. Intern Emerg Med. 2020. PMID: 32737837 Free PMC article. No abstract available.

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A Review of Coronavirus Disease-2019 (COVID-19)

Tanu singhal.

Department of Pediatrics and Infectious Disease, Kokilaben Dhirubhai Ambani Hospital and Medical Research Institute, Mumbai, India

There is a new public health crises threatening the world with the emergence and spread of 2019 novel coronavirus (2019-nCoV) or the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus originated in bats and was transmitted to humans through yet unknown intermediary animals in Wuhan, Hubei province, China in December 2019. There have been around 96,000 reported cases of coronavirus disease 2019 (COVID-2019) and 3300 reported deaths to date (05/03/2020). The disease is transmitted by inhalation or contact with infected droplets and the incubation period ranges from 2 to 14 d. The symptoms are usually fever, cough, sore throat, breathlessness, fatigue, malaise among others. The disease is mild in most people; in some (usually the elderly and those with comorbidities), it may progress to pneumonia, acute respiratory distress syndrome (ARDS) and multi organ dysfunction. Many people are asymptomatic. The case fatality rate is estimated to range from 2 to 3%. Diagnosis is by demonstration of the virus in respiratory secretions by special molecular tests. Common laboratory findings include normal/ low white cell counts with elevated C-reactive protein (CRP). The computerized tomographic chest scan is usually abnormal even in those with no symptoms or mild disease. Treatment is essentially supportive; role of antiviral agents is yet to be established. Prevention entails home isolation of suspected cases and those with mild illnesses and strict infection control measures at hospitals that include contact and droplet precautions. The virus spreads faster than its two ancestors the SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV), but has lower fatality. The global impact of this new epidemic is yet uncertain.

Introduction

The 2019 novel coronavirus (2019-nCoV) or the severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) as it is now called, is rapidly spreading from its origin in Wuhan City of Hubei Province of China to the rest of the world [ 1 ]. Till 05/03/2020 around 96,000 cases of coronavirus disease 2019 (COVID-19) and 3300 deaths have been reported [ 2 ]. India has reported 29 cases till date. Fortunately so far, children have been infrequently affected with no deaths. But the future course of this virus is unknown. This article gives a bird’s eye view about this new virus. Since knowledge about this virus is rapidly evolving, readers are urged to update themselves regularly.

Coronaviruses are enveloped positive sense RNA viruses ranging from 60 nm to 140 nm in diameter with spike like projections on its surface giving it a crown like appearance under the electron microscope; hence the name coronavirus [ 3 ]. Four corona viruses namely HKU1, NL63, 229E and OC43 have been in circulation in humans, and generally cause mild respiratory disease.

There have been two events in the past two decades wherein crossover of animal betacorona viruses to humans has resulted in severe disease. The first such instance was in 2002–2003 when a new coronavirus of the β genera and with origin in bats crossed over to humans via the intermediary host of palm civet cats in the Guangdong province of China. This virus, designated as severe acute respiratory syndrome coronavirus affected 8422 people mostly in China and Hong Kong and caused 916 deaths (mortality rate 11%) before being contained [ 4 ]. Almost a decade later in 2012, the Middle East respiratory syndrome coronavirus (MERS-CoV), also of bat origin, emerged in Saudi Arabia with dromedary camels as the intermediate host and affected 2494 people and caused 858 deaths (fatality rate 34%) [ 5 ].

Origin and Spread of COVID-19 [ 1 , 2 , 6 ]

In December 2019, adults in Wuhan, capital city of Hubei province and a major transportation hub of China started presenting to local hospitals with severe pneumonia of unknown cause. Many of the initial cases had a common exposure to the Huanan wholesale seafood market that also traded live animals. The surveillance system (put into place after the SARS outbreak) was activated and respiratory samples of patients were sent to reference labs for etiologic investigations. On December 31st 2019, China notified the outbreak to the World Health Organization and on 1st January the Huanan sea food market was closed. On 7th January the virus was identified as a coronavirus that had >95% homology with the bat coronavirus and > 70% similarity with the SARS- CoV. Environmental samples from the Huanan sea food market also tested positive, signifying that the virus originated from there [ 7 ]. The number of cases started increasing exponentially, some of which did not have exposure to the live animal market, suggestive of the fact that human-to-human transmission was occurring [ 8 ]. The first fatal case was reported on 11th Jan 2020. The massive migration of Chinese during the Chinese New Year fuelled the epidemic. Cases in other provinces of China, other countries (Thailand, Japan and South Korea in quick succession) were reported in people who were returning from Wuhan. Transmission to healthcare workers caring for patients was described on 20th Jan, 2020. By 23rd January, the 11 million population of Wuhan was placed under lock down with restrictions of entry and exit from the region. Soon this lock down was extended to other cities of Hubei province. Cases of COVID-19 in countries outside China were reported in those with no history of travel to China suggesting that local human-to-human transmission was occurring in these countries [ 9 ]. Airports in different countries including India put in screening mechanisms to detect symptomatic people returning from China and placed them in isolation and testing them for COVID-19. Soon it was apparent that the infection could be transmitted from asymptomatic people and also before onset of symptoms. Therefore, countries including India who evacuated their citizens from Wuhan through special flights or had travellers returning from China, placed all people symptomatic or otherwise in isolation for 14 d and tested them for the virus.

Cases continued to increase exponentially and modelling studies reported an epidemic doubling time of 1.8 d [ 10 ]. In fact on the 12th of February, China changed its definition of confirmed cases to include patients with negative/ pending molecular tests but with clinical, radiologic and epidemiologic features of COVID-19 leading to an increase in cases by 15,000 in a single day [ 6 ]. As of 05/03/2020 96,000 cases worldwide (80,000 in China) and 87 other countries and 1 international conveyance (696, in the cruise ship Diamond Princess parked off the coast of Japan) have been reported [ 2 ]. It is important to note that while the number of new cases has reduced in China lately, they have increased exponentially in other countries including South Korea, Italy and Iran. Of those infected, 20% are in critical condition, 25% have recovered, and 3310 (3013 in China and 297 in other countries) have died [ 2 ]. India, which had reported only 3 cases till 2/3/2020, has also seen a sudden spurt in cases. By 5/3/2020, 29 cases had been reported; mostly in Delhi, Jaipur and Agra in Italian tourists and their contacts. One case was reported in an Indian who traveled back from Vienna and exposed a large number of school children in a birthday party at a city hotel. Many of the contacts of these cases have been quarantined.

These numbers are possibly an underestimate of the infected and dead due to limitations of surveillance and testing. Though the SARS-CoV-2 originated from bats, the intermediary animal through which it crossed over to humans is uncertain. Pangolins and snakes are the current suspects.

Epidemiology and Pathogenesis [ 10 , 11 ]

All ages are susceptible. Infection is transmitted through large droplets generated during coughing and sneezing by symptomatic patients but can also occur from asymptomatic people and before onset of symptoms [ 9 ]. Studies have shown higher viral loads in the nasal cavity as compared to the throat with no difference in viral burden between symptomatic and asymptomatic people [ 12 ]. Patients can be infectious for as long as the symptoms last and even on clinical recovery. Some people may act as super spreaders; a UK citizen who attended a conference in Singapore infected 11 other people while staying in a resort in the French Alps and upon return to the UK [ 6 ]. These infected droplets can spread 1–2 m and deposit on surfaces. The virus can remain viable on surfaces for days in favourable atmospheric conditions but are destroyed in less than a minute by common disinfectants like sodium hypochlorite, hydrogen peroxide etc. [ 13 ]. Infection is acquired either by inhalation of these droplets or touching surfaces contaminated by them and then touching the nose, mouth and eyes. The virus is also present in the stool and contamination of the water supply and subsequent transmission via aerosolization/feco oral route is also hypothesized [ 6 ]. As per current information, transplacental transmission from pregnant women to their fetus has not been described [ 14 ]. However, neonatal disease due to post natal transmission is described [ 14 ]. The incubation period varies from 2 to 14 d [median 5 d]. Studies have identified angiotensin receptor 2 (ACE 2 ) as the receptor through which the virus enters the respiratory mucosa [ 11 ].

The basic case reproduction rate (BCR) is estimated to range from 2 to 6.47 in various modelling studies [ 11 ]. In comparison, the BCR of SARS was 2 and 1.3 for pandemic flu H1N1 2009 [ 2 ].

Clinical Features [ 8 , 15 – 18 ]

The clinical features of COVID-19 are varied, ranging from asymptomatic state to acute respiratory distress syndrome and multi organ dysfunction. The common clinical features include fever (not in all), cough, sore throat, headache, fatigue, headache, myalgia and breathlessness. Conjunctivitis has also been described. Thus, they are indistinguishable from other respiratory infections. In a subset of patients, by the end of the first week the disease can progress to pneumonia, respiratory failure and death. This progression is associated with extreme rise in inflammatory cytokines including IL2, IL7, IL10, GCSF, IP10, MCP1, MIP1A, and TNFα [ 15 ]. The median time from onset of symptoms to dyspnea was 5 d, hospitalization 7 d and acute respiratory distress syndrome (ARDS) 8 d. The need for intensive care admission was in 25–30% of affected patients in published series. Complications witnessed included acute lung injury, ARDS, shock and acute kidney injury. Recovery started in the 2nd or 3rd wk. The median duration of hospital stay in those who recovered was 10 d. Adverse outcomes and death are more common in the elderly and those with underlying co-morbidities (50–75% of fatal cases). Fatality rate in hospitalized adult patients ranged from 4 to 11%. The overall case fatality rate is estimated to range between 2 and 3% [ 2 ].

Interestingly, disease in patients outside Hubei province has been reported to be milder than those from Wuhan [ 17 ]. Similarly, the severity and case fatality rate in patients outside China has been reported to be milder [ 6 ]. This may either be due to selection bias wherein the cases reporting from Wuhan included only the severe cases or due to predisposition of the Asian population to the virus due to higher expression of ACE 2 receptors on the respiratory mucosa [ 11 ].

Disease in neonates, infants and children has been also reported to be significantly milder than their adult counterparts. In a series of 34 children admitted to a hospital in Shenzhen, China between January 19th and February 7th, there were 14 males and 20 females. The median age was 8 y 11 mo and in 28 children the infection was linked to a family member and 26 children had history of travel/residence to Hubei province in China. All the patients were either asymptomatic (9%) or had mild disease. No severe or critical cases were seen. The most common symptoms were fever (50%) and cough (38%). All patients recovered with symptomatic therapy and there were no deaths. One case of severe pneumonia and multiorgan dysfunction in a child has also been reported [ 19 ]. Similarly the neonatal cases that have been reported have been mild [ 20 ].

Diagnosis [ 21 ]

A suspect case is defined as one with fever, sore throat and cough who has history of travel to China or other areas of persistent local transmission or contact with patients with similar travel history or those with confirmed COVID-19 infection. However cases may be asymptomatic or even without fever. A confirmed case is a suspect case with a positive molecular test.

Specific diagnosis is by specific molecular tests on respiratory samples (throat swab/ nasopharyngeal swab/ sputum/ endotracheal aspirates and bronchoalveolar lavage). Virus may also be detected in the stool and in severe cases, the blood. It must be remembered that the multiplex PCR panels currently available do not include the COVID-19. Commercial tests are also not available at present. In a suspect case in India, the appropriate sample has to be sent to designated reference labs in India or the National Institute of Virology in Pune. As the epidemic progresses, commercial tests will become available.

Other laboratory investigations are usually non specific. The white cell count is usually normal or low. There may be lymphopenia; a lymphocyte count <1000 has been associated with severe disease. The platelet count is usually normal or mildly low. The CRP and ESR are generally elevated but procalcitonin levels are usually normal. A high procalcitonin level may indicate a bacterial co-infection. The ALT/AST, prothrombin time, creatinine, D-dimer, CPK and LDH may be elevated and high levels are associated with severe disease.

The chest X-ray (CXR) usually shows bilateral infiltrates but may be normal in early disease. The CT is more sensitive and specific. CT imaging generally shows infiltrates, ground glass opacities and sub segmental consolidation. It is also abnormal in asymptomatic patients/ patients with no clinical evidence of lower respiratory tract involvement. In fact, abnormal CT scans have been used to diagnose COVID-19 in suspect cases with negative molecular diagnosis; many of these patients had positive molecular tests on repeat testing [ 22 ].

Differential Diagnosis [ 21 ]

The differential diagnosis includes all types of respiratory viral infections [influenza, parainfluenza, respiratory syncytial virus (RSV), adenovirus, human metapneumovirus, non COVID-19 coronavirus], atypical organisms (mycoplasma, chlamydia) and bacterial infections. It is not possible to differentiate COVID-19 from these infections clinically or through routine lab tests. Therefore travel history becomes important. However, as the epidemic spreads, the travel history will become irrelevant.

Treatment [ 21 , 23 ]

Treatment is essentially supportive and symptomatic.

The first step is to ensure adequate isolation (discussed later) to prevent transmission to other contacts, patients and healthcare workers. Mild illness should be managed at home with counseling about danger signs. The usual principles are maintaining hydration and nutrition and controlling fever and cough. Routine use of antibiotics and antivirals such as oseltamivir should be avoided in confirmed cases. In hypoxic patients, provision of oxygen through nasal prongs, face mask, high flow nasal cannula (HFNC) or non-invasive ventilation is indicated. Mechanical ventilation and even extra corporeal membrane oxygen support may be needed. Renal replacement therapy may be needed in some. Antibiotics and antifungals are required if co-infections are suspected or proven. The role of corticosteroids is unproven; while current international consensus and WHO advocate against their use, Chinese guidelines do recommend short term therapy with low-to-moderate dose corticosteroids in COVID-19 ARDS [ 24 , 25 ]. Detailed guidelines for critical care management for COVID-19 have been published by the WHO [ 26 ]. There is, as of now, no approved treatment for COVID-19. Antiviral drugs such as ribavirin, lopinavir-ritonavir have been used based on the experience with SARS and MERS. In a historical control study in patients with SARS, patients treated with lopinavir-ritonavir with ribavirin had better outcomes as compared to those given ribavirin alone [ 15 ].

In the case series of 99 hospitalized patients with COVID-19 infection from Wuhan, oxygen was given to 76%, non-invasive ventilation in 13%, mechanical ventilation in 4%, extracorporeal membrane oxygenation (ECMO) in 3%, continuous renal replacement therapy (CRRT) in 9%, antibiotics in 71%, antifungals in 15%, glucocorticoids in 19% and intravenous immunoglobulin therapy in 27% [ 15 ]. Antiviral therapy consisting of oseltamivir, ganciclovir and lopinavir-ritonavir was given to 75% of the patients. The duration of non-invasive ventilation was 4–22 d [median 9 d] and mechanical ventilation for 3–20 d [median 17 d]. In the case series of children discussed earlier, all children recovered with basic treatment and did not need intensive care [ 17 ].

There is anecdotal experience with use of remdeswir, a broad spectrum anti RNA drug developed for Ebola in management of COVID-19 [ 27 ]. More evidence is needed before these drugs are recommended. Other drugs proposed for therapy are arbidol (an antiviral drug available in Russia and China), intravenous immunoglobulin, interferons, chloroquine and plasma of patients recovered from COVID-19 [ 21 , 28 , 29 ]. Additionally, recommendations about using traditional Chinese herbs find place in the Chinese guidelines [ 21 ].

Prevention [ 21 , 30 ]

Since at this time there are no approved treatments for this infection, prevention is crucial. Several properties of this virus make prevention difficult namely, non-specific features of the disease, the infectivity even before onset of symptoms in the incubation period, transmission from asymptomatic people, long incubation period, tropism for mucosal surfaces such as the conjunctiva, prolonged duration of the illness and transmission even after clinical recovery.

Isolation of confirmed or suspected cases with mild illness at home is recommended. The ventilation at home should be good with sunlight to allow for destruction of virus. Patients should be asked to wear a simple surgical mask and practice cough hygiene. Caregivers should be asked to wear a surgical mask when in the same room as patient and use hand hygiene every 15–20 min.

The greatest risk in COVID-19 is transmission to healthcare workers. In the SARS outbreak of 2002, 21% of those affected were healthcare workers [ 31 ]. Till date, almost 1500 healthcare workers in China have been infected with 6 deaths. The doctor who first warned about the virus has died too. It is important to protect healthcare workers to ensure continuity of care and to prevent transmission of infection to other patients. While COVID-19 transmits as a droplet pathogen and is placed in Category B of infectious agents (highly pathogenic H5N1 and SARS), by the China National Health Commission, infection control measures recommended are those for category A agents (cholera, plague). Patients should be placed in separate rooms or cohorted together. Negative pressure rooms are not generally needed. The rooms and surfaces and equipment should undergo regular decontamination preferably with sodium hypochlorite. Healthcare workers should be provided with fit tested N95 respirators and protective suits and goggles. Airborne transmission precautions should be taken during aerosol generating procedures such as intubation, suction and tracheostomies. All contacts including healthcare workers should be monitored for development of symptoms of COVID-19. Patients can be discharged from isolation once they are afebrile for atleast 3 d and have two consecutive negative molecular tests at 1 d sampling interval. This recommendation is different from pandemic flu where patients were asked to resume work/school once afebrile for 24 h or by day 7 of illness. Negative molecular tests were not a prerequisite for discharge.

At the community level, people should be asked to avoid crowded areas and postpone non-essential travel to places with ongoing transmission. They should be asked to practice cough hygiene by coughing in sleeve/ tissue rather than hands and practice hand hygiene frequently every 15–20 min. Patients with respiratory symptoms should be asked to use surgical masks. The use of mask by healthy people in public places has not shown to protect against respiratory viral infections and is currently not recommended by WHO. However, in China, the public has been asked to wear masks in public and especially in crowded places and large scale gatherings are prohibited (entertainment parks etc). China is also considering introducing legislation to prohibit selling and trading of wild animals [ 32 ].

The international response has been dramatic. Initially, there were massive travel restrictions to China and people returning from China/ evacuated from China are being evaluated for clinical symptoms, isolated and tested for COVID-19 for 2 wks even if asymptomatic. However, now with rapid world wide spread of the virus these travel restrictions have extended to other countries. Whether these efforts will lead to slowing of viral spread is not known.

A candidate vaccine is under development.

Practice Points from an Indian Perspective

At the time of writing this article, the risk of coronavirus in India is extremely low. But that may change in the next few weeks. Hence the following is recommended:

• Healthcare providers should take travel history of all patients with respiratory symptoms, and any international travel in the past 2 wks as well as contact with sick people who have travelled internationally.
• They should set up a system of triage of patients with respiratory illness in the outpatient department and give them a simple surgical mask to wear. They should use surgical masks themselves while examining such patients and practice hand hygiene frequently.
• Suspected cases should be referred to government designated centres for isolation and testing (in Mumbai, at this time, it is Kasturba hospital). Commercial kits for testing are not yet available in India.
• Patients admitted with severe pneumonia and acute respiratory distress syndrome should be evaluated for travel history and placed under contact and droplet isolation. Regular decontamination of surfaces should be done. They should be tested for etiology using multiplex PCR panels if logistics permit and if no pathogen is identified, refer the samples for testing for SARS-CoV-2.
• All clinicians should keep themselves updated about recent developments including global spread of the disease.
• Non-essential international travel should be avoided at this time.
• People should stop spreading myths and false information about the disease and try to allay panic and anxiety of the public.

Conclusions

This new virus outbreak has challenged the economic, medical and public health infrastructure of China and to some extent, of other countries especially, its neighbours. Time alone will tell how the virus will impact our lives here in India. More so, future outbreaks of viruses and pathogens of zoonotic origin are likely to continue. Therefore, apart from curbing this outbreak, efforts should be made to devise comprehensive measures to prevent future outbreaks of zoonotic origin.

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Peer-reviewed

Research Article

The impact of the COVID-19 pandemic on higher education: Assessment of student performance in computer science

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Supervision, Validation, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Department of Computer Science, Lublin University of Technology, Lublin, Poland, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

Roles Conceptualization, Formal analysis, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation Department of Computer Science, Lublin University of Technology, Lublin, Poland

Roles Data curation, Software

• Małgorzata Charytanowicz, 
• Magdalena Zoła, 
• Waldemar Suszyński

• Published: August 14, 2024
• https://doi.org/10.1371/journal.pone.0305763
• Reader Comments

The COVID-19 pandemic had radically changed higher education. The sudden transition to online teaching and learning exposed, however, some benefits by enhancing educational flexibility and digitization. The long-term effects of these changes are currently unknown, but a key question concerns their effect on student learning outcomes. This study aims to analyze the impact of the emergence of new models and teaching approaches on the academic performance of Computer Science students in the years 2019–2023. The COVID-19 pandemic created a natural experiment for comparisons in performance during in-person versus synchronous online and hybrid learning mode. We tracked changes in student achievements across the first two years of their engineering studies, using both basic (descriptive statistics, t-Student tests, Mann-Whitney test) and advanced statistical methods (Analysis of variance). The inquiry was conducted on 787 students of the Lublin University of Technology (Poland). Our findings indicated that first semester student scores were significantly higher when taught through online (13.77±2.77) and hybrid (13.7±2.86) approaches than through traditional in-person means as practiced before the pandemic (11.37±3.9, p-value < 0.05). Conversely, third semester student scores were significantly lower when taught through online (12.01±3.14) and hybrid (12.04±3.19) approaches than through traditional in-person means, after the pandemic (13.23±3.01, p-value < 0.05). However, the difference did not exceed 10% of a total score of 20 points. With regard to the statistical data, most of the questions were assessed as being difficult or appropriate, with adequate discrimination index, regardless of the learning mode. Based on the results, we conclude that we did not find clear evidence that pandemic disruption and online learning caused knowledge deficiencies. This critical situation increased students’ academic motivation. Moreover, we conclude that we have developed an effective digital platform for teaching and learning, as well as for a secure and fair student learning outcomes assessment.

Citation: Charytanowicz M, Zoła M, Suszyński W (2024) The impact of the COVID-19 pandemic on higher education: Assessment of student performance in computer science. PLoS ONE 19(8): e0305763. https://doi.org/10.1371/journal.pone.0305763

Editor: Prabhat Mittal, Satyawati College (Eve.), University of Delhi, INDIA

Received: October 15, 2023; Accepted: June 4, 2024; Published: August 14, 2024

Copyright: © 2024 Charytanowicz et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are available at the following link: https://zenodo.org/records/11583297 .

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

The COVID-19 pandemic brought with it a number of health, economic and social consequences. Indeed, the spread of the SARS-CoV-2 virus turned out to be so dangerous that many countries implemented new regulations in the educational field to limit physical contact. The pandemic-induced school shutdowns and sudden transition to remote teaching and learning at all levels of education. This change-over generated a number of technical and social problems [ 1 – 6 ]. These problems had also affected the academic community, although online or blended learning methods were implemented before the COVID-19 pandemic [ 7 ].

On March 12, 2020, a state of epidemic emergency was declared in Poland, and a week later–a state of pandemic. In consequence, the Minister of Science and Higher Education issued a regulation on the temporary suspension of the functioning of education institutes, lasting from March 12 till 25 2020 [ 8 , 9 ]. On March 25, 2020, the education system, including higher education, was switched to online teaching and learning, as necessitated by the need to maintain social distancing measures. Universities had to adapt to the circumstances almost overnight. However, many universities were not fully prepared with regard to technical capabilities, educational resources and the skills of the teaching staff in organizing distance education [ 10 – 12 ]. Before the COVID-19 pandemic, the applicable regulations of the Ministry of Science and Higher Education did not encourage the authorities of most universities to invest in technologies for conducting fully remote studies. Poland was, however, not an exception in this respect. Many old, prestigious universities in Europe were also reserved about remote learning, and the virtual learning environment was mainly used as a teaching aid.

Fortunately, the information revolution had by this time developed more flexible approaches to learning with the form of Information and Communication Technology (ICT). Indeed, it is one of the leading factors that affect current teaching methodology [ 13 – 18 ]. E-learning systems, their accessibility and functionality, have provided new possibilities to acquire knowledge and to ease the burden of learning. As an outcome, remote teaching and learning are often seen as promising solutions that offer high flexibility and a learner-centered approach that enables students to learn at their own pace [ 19 , 20 ]. Thus, the role of the teacher in the classroom has transformed from that of being the font of knowledge, to an instructional manager identifying relevant resources and creating collaborative learning opportunities. Moreover, online assessments have become increasingly important and now represent one of the most critical aspects of the educational process. Unfortunately, the role of ICT in higher education is still somewhat controversial.

The extreme situation caused by the COVID-19 pandemic provided an opportunity to revise our approach both to traditional and online learning, yet also posing challenges for the future of education systems. The main question of our research was whether the sudden transition to online teaching and learning caused by the COVID-19 pandemic had a negative impact on students academic performance and upon the reliability of the assessment process. We believe that our study can help to reduce the controversies related to remote learning and teaching.

2. Related works

Before the year 2020, the principal recipients of remote education were adults participating in professional development courses [ 21 ]. The COVID-19 pandemic outbreak, however, resulted in increased interest in methods of education that do not require physical meeting between students and teachers. The closure of educational institutions to mitigate the spread of COVID-19 compelled schools and universities to find alternative ways of continuing their operations. This led to the widespread adoption of online learning (e-learning).

The use of e-learning platforms has enabled the transformation of the traditional model of education in which the lecturer transmitted knowledge, into a model of supervised self-education. A separate line of research has been dedicated to the impact of remote education on university students, who are predominantly young adults, and, as such, are less subject to parental supervision. Topics under study include student attitudes towards distance learning [ 22 , 23 ], the technologies and learning platforms utilized [ 24 – 26 ], and the impact of network quality on the smoothness of classes [ 22 , 27 ].

A relatively well researched aspect of e-learning is the analysis of its advantages and disadvantages in comparison to traditional learning [ 28 – 30 ], including its application during the COVID-19 pandemic [ 31 – 34 ]. Undoubtedly, remote education has its benefits, among others, flexibility, speed, time savings [ 35 , 36 ], as well as better use of the infrastructure and organizational savings for the institution [ 37 ]. Distance learning in the form of e-learning also comes with drawbacks, for example, limited interpersonal contacts [ 38 ], lack of immediate feedback [ 39 , 40 ], and problems with self-discipline and adaptability [ 41 – 43 ]. Considering its strengths and weaknesses, e-learning can be viewed as either a replacement or augmentation of traditional approaches to education.

An integral part of remote education is the verification of its results. The topic was covered in literature in the pre-COVID era [ 44 – 46 ], but much less so during the pandemic [ 47 , 48 ]. Our work focuses on the analysis of student performance under the e-learning setup during COVID-19 related confinement and afterwards. The differentiating characteristic of this paper is the fact that it covers a longer period of time, unlike some other research focusing only on a single academic semester [ 49 ].

The COVID-19 pandemic has provided the opportunity to advance usage of online platforms and digital media, as well as to create new education strategies. It should be noted that most students (and instructors) adapted successfully to online teaching and learning [ 50 , 51 ]. However, certain studies [ 52 – 54 ] have indicated negative student feedback. In the year 2023, education has returned to more traditional teaching/learning approaches after more than two years of online learning.

The outbreak of COVID-19 presented a serious challenge to academic education by enforcing a drastic change in the teaching methods. For this reason, we formulated the following research questions:

• How had the COVID-19 pandemic change applied teaching and learning strategies?
• Did the COVID-19 pandemic have a disruptive effect on the academic performance of students resulting in knowledge deficiency?
• How did the change from in-person to online learning affect the reliability of student assessment?

The rest of the paper is structured as follows. Section 3 presents the context of the study, materials and methods. Section 4 explains the results obtained. Sections 5 and 6 conclude our work and describe limitations and future scope.

3. Materials and methods

3.1. design and context.

The research was conducted in the Department of Computer Science of the Lublin University of Technology in Poland, the largest public technical university in the Lublin voivodship. This was a cross-sectional study carried out among students who were enrolled in the first semester of engineering studies in the academic years 2019/2020, 2020/2021 and 2021/2022 (from October to July). Because of the COVID-19 pandemic, the courses of interest in this study were conducted in different delivery formats (in-person, synchronous online and hybrid).

Traditional in-person course delivery format included lectures and laboratories. The former involved, primarily, oral presentations given to a group of students. A teacher-centered approach to learning was applied with discussion and multimedia presentation, as well as whiteboard or chalkboard visual aids to emphasize important points in the lecture. Moreover, a Learning Management System (Moodle LMS) was incorporated within the lectures to develop, organize, deliver and manage didactic materials and assess the effectiveness of education via tests, surveys or assignments. This tool was also employed to provide discussion forums. The faculty used the activity Quiz as a student self-assessment tool, as well as to determine knowledge and skills.

With regard to laboratory work, practical classes were conducted in programming laboratories for the selected courses. In such a teaching/learning format, we found that most students preferred working alone or conducting discussions with their partners or their neighbors.

All students used online manuals or didactic materials delivered by Moodle LMS. Final exams were held at the University via Moodle LMS through in-person proctoring, as this approach allowed the introduction of a live person to monitor the activity of students in a testing environment.

In the synchronous online course format, students obtained theoretical and practical education entirely online via Microsoft Teams by way of video meetings and Moodle LMS. Meetings in Teams include audio, video and screen sharing. All lectures were delivered synchronously using MS Teams. Practical sessions were conducted through online synchronous video meetings in small student groups. Interaction occurred via the discussion board, while MS Teams was also employed to enable scheduled online consultations. Supporting materials (videos, presentations, tasks to do, quizzes, and other didactic materials) were provided to the students through the Moodle LMS. Final exams were conducted under controlled conditions via Moodle LMS through online live proctoring by accepting screen, video and audio sharing.

The hybrid course delivery format combined in-person and online strategies. Students obtained theoretical education entirely online as synchronous sessions by way of MS Teams and Moodle LMS, whilst practical education was obtained through the traditional in-person format, in small student groups. Final exams were held at the University via Moodle LMS through in-person proctoring.

We analyzed exam scores across the first two years of the engineering studies using anonymous data from the Moodle. The Research Ethics Committee of Lublin University of Technology approved the study (Ethical Approval Reference: 3/2023).

3.2. Course selection

The following criteria were used to select the courses:

• the courses covered algorithms and programming,
• the courses had unchanged objectives and learning outcomes during the investigated period,
• the courses were conducted by the same instructors using to the same tools and methods.

Two compulsory courses met these criteria: 1 –Introduction to Computer Science and 2 –Numerical Analysis Algorithms. Both courses were conducted in the Polish language and they provided fundamental knowledge for all areas of Computer Science learning and skills development. Enrolled students were obligated to complete 30 lesson hours of theory and 30 lesson hours of practical experience within a course length of 15 weeks. In the full-time option, four hours of classes were given each course week, and were distributed into two two-hour sessions. Herein, the first consisted of a master class lecture and the second consisted of an interactive problem-based learning laboratory. In the part-time option, the number of in-person teaching hours was reduced to half and classes were held, on average, twice a month, on Saturday and Sunday.

The Introduction to Computer Science course is taught in the first year and is covered in the first semester. Students who successfully completed the course gained five credits, according to the European Credit Transfer and Accumulation System (ECTS). The intention of the offered course is to provide students with knowledge of standard algorithms and data structures, and to provide them with the skills to analyze both the theoretical complexity of algorithms and their practical behaviors. The course covers the following topics:

• Introduction to algorithms and problem-solving techniques.
• Basic programming concepts, types, sequential data structures.
• Programming in Python.
• Searching and sorting algorithms.
• Examples of algorithms, algorithmic strategies.
• Testing and documenting programming code.
• Asymptotic notation and complexity analysis.
• Analyzing program code for correctness, efficiency, and errors.
• Automata theory and formal languages. Turing machine.
• Classes P and NP.

The knowledge and skills to implement and solve algorithmic problems using the mentioned algorithms are developed using Python.

The Numerical Analysis Algorithms course is taught in the second year and is covered in the third semester. Successful completion awards students with five credits, according to ECTS. The primary objective of the course is to develop basic understanding of numerical algorithms, as well as the skills to implement algorithms to solve computer-based mathematical problems. The course covers the following topics:

• Basic numerics, floating-point representation, convergence.
• Horner’s scheme.
• The theory of interpolation: Lagrange polynomial, Hermite interpolation, Neville’s iterative formula.
• Least square approximation.
• Numerical integration: Newton-Cotes formulas, Gaussian quadrature.
• Direct methods for solving systems of linear equations: Gaussian elimination, LU factorization, Cholesky decomposition.
• Householder method.
• Solving nonlinear equations and systems of nonlinear equations: Bisection method, fixed-point iteration, Newton’s method.
• Runge-Kutta methods for ordinary differential equations.
• Characteristic polynomial and eigenvalues.

The knowledge and skills to implement and solve algorithmic problems using the mentioned algorithms were developed using C++ due to its object-oriented programming with high performance, efficient memory management, low-level access to hardware and a rich standard library, including mathematical functions commonly used in numerical algorithms. These allow students to write efficient and customizable numerical algorithms. Objective C++ was one of the courses of the first year of studies.

3.3. The study participants

Study participants were selected from Computer Science students who were enrolled in the two mentioned compulsory courses: Introduction to Computer Science (ICS) (first semester) and Numerical Analysis Algorithms (NAA) (third semester). The first group of students began their studies in the academic year 2019/2020 in a traditional in-person course delivery format that was interrupted because of the confinement. They then continued their studies utilizing the synchronous online format. The second group consisted of students who began their studies in academic year 2020/2021 in the synchronous online format and continued these activities in a hybrid format. The third group of students began their studies in academic year 2021/2022 in a hybrid format that returned to an in-person format in the year 2022/2023. Online learning was supported by Moodle and MS Teams.

Only students enrolled in either the ICS and NAA courses participated in our research. Students who interrupted their studies and did not complete the courses were excluded. Thus, the study group included students who were enrolled in both courses and took both final exams. A total of 787 participants were selected. Table 1 summarizes the study participant groups according to education strategy.

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https://doi.org/10.1371/journal.pone.0305763.t001

Males constituted 87.5% of the total study participants, while females constituted 12.5%. Regarding nationality, the majority, i.e. 85.5%, came from Poland, while 14.5% came from other countries, mainly Ukraine.

3.4. Online exam quizzes

In this study, the Moodle platform provided by the Computer Science Department from the Lublin University of Technology was applied to conduct the final exam process. Comparative analysis of student academic performance was anchored on the results obtained in their final exams. Final exams were carried through the Moodle platform using Quiz activity . All exams comprised questions of various types, including Multiple Choice , Short Answer , Numerical and Essay as follows:

• Multiple choice questions were employed for evaluating both theoretical and practical contents. For our purpose, the option Multiple answers are allowed was used. Multiple answers questions enable one or more answers to be chosen by providing check boxes next to the answers. We used a negative grade percentage for wrong answers, so that simply ticking all choices did not necessarily generate a full grade. If the sum of partial grades was negative, then the total grade for this question would be zero [ 55 ].
• Short answer or numerical questions were used to evaluate theoretical and practical contents. In a short answer question, the student types in a word or phrase in response to a question. This must exactly match one of the acceptable answers. Numerical questions resembled short-answer questions. Here, the difference was that numerical answers were allowed to have an accepted error for number.
• Essay questions were used to evaluate practical contents, mainly programming and coding skills. We employed essay-type questions to provide the option of answering by entering text online. The option Require the student to enter text was chosen. The Response format option was set to Plain text , monospaced font to improve the readability of code by ensuring consistent and clear alignment. This is particularly helpful for maintaining an organized layout. The essay questions had to be marked manually by the course instructor.

The number of multiple choice questions and short answer / numerical questions was comparable. One question was an essay question. Questions were created and stored separately in a Question bank and were organized into 10 categories according to the implemented curricula and learning outcomes. Each category consisted of at least 50 questions. Quiz settings were as follows:

• Quizzes included 20 questions worth 20 points. There were two categories of questions: theoretical and practical.
• Students were allowed to have one attempt at each quiz. The time limit option was set to 60 minutes.
• Students were not allowed to open other windows or programs while taking these quizzes.
• A password was required. The option Block concurrent connections was checked.
• The Choose Sequential navigation method was employed to compel the student to progress through the questions in order and not return to a previous question or skip to a later one.
• The timeframe when the students were able to see feedback was set to the option After the quiz is closed and the option Whether correct was checked.
• Employed questions were assessed for quality and modified for re-use in the next academic year.

Students were tested using the same evaluation methods and types of questions in in-person, synchronous online and hybrid groups. The Moodle platform collected assessment data and generated report statistics. The data containing students’ exam results (points) were collected and exported from the Moodle platform as.xlsx files.

3.5. Quiz report statistics

Quiz statistics provided test statistics and quiz structure analysis. The test statistics gave information on how students performed on a quiz, and employed descriptive statistics: average grade, median grade, standard deviation of grades, skewness and kurtosis. A detailed analysis of each question was given in quiz structure analysis, and applied the following measures: facility index, discrimination index and discriminative efficiency. Discriminative efficiency is a measure similar to discrimination index [ 55 ].

Facility index.

In this work, facility index of a question was determined by the average score divided by the maximum score and represented as a percentage. A higher value indicated an easier question. The interpretation of its values is given in Table 2 [ 55 ].

https://doi.org/10.1371/journal.pone.0305763.t002

Discrimination index.

Discrimination index is the correlation between the score for this question and the score for the whole quiz represented as a percentage. If the score for the question and the score for the test are well correlated, the question can be categorized as a question with good discrimination. The maximum discrimination requires a facility index in the range 30%–70%, although this is not tantamount to high discrimination index. Discrimination index values should be interpreted according to Table 3 [ 55 ].

https://doi.org/10.1371/journal.pone.0305763.t003

A negative value of a discrimination index would mean that the best students got this question wrong more often than the worst students. A discrimination index of zero would mean it was a poor discriminator between good and bad students. Discrimination index is considered excellent when the value is higher than 40%, and considered good when it ranges from 20% to 40%.

Discriminative efficiency.

The discriminative efficiency estimates how good the discrimination index is relative to the difficulty of the question. This attempts to discriminate between students of different ability, and the higher the value, the better is the question at discriminating between students of different abilities [ 55 ]. Values between 30%–50% provide adequate discrimination, while those above 50% provide very good discrimination.

3.6. Statistical analysis

Data collected was tabulated, and analysis was carried out by applying simple percentage analysis, as well as descriptive analysis, using mean, standard deviation and inferential analysis such as t-Student tests and ANOVA [ 56 , 57 ]. We performed non-parametric alternatives such as a Mann-Whitney U test and the Kruskal-Wallis test to compare samples that cannot be assumed to be normally distributed [ 58 , 59 ]. Statistical significance was set at p<0.05. Data analysis was performed using the Statistica Package, Version 13 (TIBCO Software Inc.).

Participants’ profile

Our study included 787 Computer Science students, aged 18 to 22 years. The participant background characteristics revealed that most students were male (87.5%) and native (Polish; 85.5%). Furthermore, most of the students were enrolled in full-time studies (85.5%) ( Table 4 ).

https://doi.org/10.1371/journal.pone.0305763.t004

The percentages of the students who began their studies in the academic years 2019/2020, 2020/2021 and 2021/2022 were comparable, around 30%. An important aspect of the analysis was the availability of data from the pre-pandemic period that was relevant for our investigations.

Comparison of in-person, synchronous online and hybrid learning

The comparison of in-person, synchronous online, and hybrid teaching methods in student learning outcomes based on background characteristics is presented in Tables 5 and 6 .

https://doi.org/10.1371/journal.pone.0305763.t005

https://doi.org/10.1371/journal.pone.0305763.t006

The findings indicated that for the first semester course Introduction to Computer Science, the relation between learning outcomes and student gender was insignificant (p = 0.427). Moreover, the relation between learning outcomes and study option was also insignificant (p = 0.223). However, there was statistically significant difference between learning outcomes and residency status (p < 0.001). The findings indicated that during in-person and online studies, native students had significantly higher learning outcomes than did non-native students (p < 0.001). In addition, full-time students of online studies had significantly higher learning outcomes (p = 0.002) than did part-time students.

Regarding the learning outcomes of the students as obtained in the third semester course Numerical Analysis Algorithms, gender and study options were also insignificant (p = 0.834; p = 0.157) in relation to learning outcomes. In contrast, residency status was significant (p < 0.001). The findings indicate that native students had significantly higher learning outcomes than did non-native students (p < 0.001). Moreover, full-time students of online studies had significantly higher learning outcomes as compared to part-time students (p = 0.011).

The comparison of teaching methods in participant performance based on different semesters (courses) is presented in Table 7 .

https://doi.org/10.1371/journal.pone.0305763.t007

The differences in mean scores related to the first semester course Introduction to Computer Science, during online and hybrid studies, were significantly higher compared to in-person studies (LSD post-hoc, p < 0.001). However, mean scores related to the third semester course Numerical Analysis Algorithms, during online and hybrid studies, were significantly lower in comparison to in-person studies (LSD post-hoc, p < 0.001). Switching to traditional in-person studies in the academic year 2022/2023 did not degrade student performance.

Quiz quality assessment

Tables 8 and 9 reveal the facility index, discrimination index and discriminative efficiency values from the final exams held from 2019/2020 to 2022/2023.

https://doi.org/10.1371/journal.pone.0305763.t008

https://doi.org/10.1371/journal.pone.0305763.t009

The lowest mean facility index was 47% ± 25%, while the highest mean facility index was 59% ± 20%. Moreover, the mean discrimination index was located within the range between 31% and 37% and the mean discriminative efficiency was found within the range between 43% and 54%. The results indicate, with regard to facility index, that most of the questions were moderately difficult, yet about right for the average student, and demonstrated adequate discrimination—regardless of the course delivery format.

5. Discussion and conclusions

In our study, we compared the learning outcomes of Computer Science students who were taught through synchronous online and hybrid systems, to those who learned in the traditional in-person system, and this revealed significantly higher learning outcomes when taught through online and hybrid systems versus in-person. It is worth noting that student scores showed an increasing trend in the years 2019–2023. Despite this, the significant difference in the results of the students’ final examination was not too large–as it did not exceed 10% of the maximal score.

A comparison between the student groups demonstrates that utilizing synchronous online learning can result in more enhanced educational opportunities for students. However, our findings indicated that native students had significantly higher learning outcomes than did non-native students. The reason could be that the study courses were held in Polish, which is a difficult language for non-native students to learn and utilize.

Several research studies have shown that online learning and the combination of online and in-person learning systems have positive and powerful roles in enhancing the effectiveness of education [ 19 , 29 , 41 , 47 , 60 ]. However, along with enhanced accessibility and flexibility, pure online learning also has several disadvantages, notably, the lack of interpersonal contacts and student satisfaction. In the hybrid form, however, flexibility and accessibility are enhanced, while human connection occurs.

Our results indicated that synchronous online learning could be appreciated as a successful method of conducting Computer Science education and can be used as a tool supporting traditional in-person methods. Although this approach is a little less flexible for teachers and students, and requires reliable technology, in comparison to asynchronous learning, this allows for more real time engagement and feedback [ 61 ].

As the effective measurement of knowledge acquired is an important component of Computer Science education, the use of the Moodle quizzes activity as a continuous assessment of students was analyzed according to statistical data such as the facility index, discrimination index and discriminative efficiency. Out of the exam tests conducted from the academic year 2019/2020 to 2022/2023, the mean facility index scores ranged from 47% to 59% and the mean discrimination index ranged from 31% to 37%. The statistic results indicated that, regarding facility index, most of the questions were moderately difficult and about right for the average student regardless of the course delivery format, and that a consistent and adequate level of discrimination indices was maintained. In addition, the similar results obtained in our study no matter the year, with three different groups of students, also confirmed the validity and reliability of the designed exam tests.

Although online learning requires extensive self-discipline, it allows universities to integrate new technologies into their offer, and hence, effectively facilitate the student learning process. After the COVID-19 pandemic, there has been a quick transition back to in-person teaching, but still there are many proffered activities being in an online format. At present, many students state that they prefer to learn through hybrid learning methods. Furthermore, several studies have shown that e-learning methods are used widely by students outside of their formal curricula for continuing their professional education [ 62 ]. This indicates that students and professionals appreciate and take advantage of self-paced learning environments in which they control their learning pace, information flow, selection of learning activities, as well as their time management. Thus, the digital transformation of the educational process has become a necessity to meet shifting student demands and seems to be one of the leading factors that affect current teaching methodology.

It is worth noting that the extreme situation caused by the COVID-19 pandemic provided an opportunity to revise our approach, both to traditional and online learning, but also posed challenges for the future of education systems. In conclusion, the results of the analysis allow us to answer the questions formulated before in the following way.

• The COVID-19 confinement caused online education, which previously was mainly used as an addition to traditional learning methods, to become the mainstream, in particular, in Computer Science.
• The COVID-19 pandemic did not have a disruptive effect that resulted in knowledge deficiency with regard to the academic performance of Computer Science students. In contrast, this situation increased student academic motivation. Indeed, students demonstrated higher exam scores during subsequent two academic years.
• Despite the change from in-person to online learning, the reliability of student assessment remained at similar levels.

6. Limitations and future works

Our context is algorithms and programming in the first two years of the engineering studies program. While we believe that the long period under study is an advantage of this work, its limitation is the fact that it focuses only on the students of Computer Science. We based our research on the data comprising the performance of students in only two courses. Moreover, only the exam scores from the 1 st and 3 rd semesters were included in the study. The courses of other semesters were not assessed because they did not meet the required assumptions regarding the course selection. Another limitation of our study was that students could share information about the content of the exam. However, we randomly assigned students to subcategory sets to avoid sharing information. In the future it is worth considering extending the analysis to students of other fields, as well as take into account student performance in more courses.

Acknowledgments

The authors thank Mr Jack Dunster for linguistic improvement of the text.

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Effect estimates for self-reported symptoms compared with no symptoms with 95% CIs. Plus signs indicate that those factors were sequentially added to the model.

a Medical factors include body mass index, smoking status, diabetes, hypertension, coronary heart disease, heart failure, stroke or transient ischemic attack, and estimated glomerular filtration rate.

eFigure. Flowchart of Study Participants Who Completed the C4R Questionnaire, Submitted a Dried Blood Spot for Evaluation and Received Two Doses of Either Pfizer-Biontech or Moderna SARS Cov-2 Vaccines

• Error in Figure and Discussion JAMA Network Open Correction November 28, 2022

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Hermann EA , Lee B , Balte PP, et al. Association of Symptoms After COVID-19 Vaccination With Anti–SARS-CoV-2 Antibody Response in the Framingham Heart Study. JAMA Netw Open. 2022;5(10):e2237908. doi:10.1001/jamanetworkopen.2022.37908

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Association of Symptoms After COVID-19 Vaccination With Anti–SARS-CoV-2 Antibody Response in the Framingham Heart Study

• 1 Division of General Medicine, Department of Medicine, Columbia University, New York, New York
• 2 Department of Pediatrics, Larner College of Medicine at the University of Vermont, Burlington
• 3 Department of Medicine, Boston University School of Medicine, Boston, Massachusetts
• 4 Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
• 5 Department of Medicine, Larner College of Medicine at the University of Vermont, Burlington
• 6 Department of Microbiology and Molecular Genetics, Larner College of Medicine, University of Vermont, Burlington
• Correction Error in Figure and Discussion JAMA Network Open

SARS-CoV-2 messenger RNA (mRNA) vaccines (BNT162b2 [Pfizer-BioNTech] and mRNA-1273 [Moderna]) are associated with local and systemic symptoms; however, whether postvaccination symptoms are associated with vaccine-induced antibody response is unknown. Previous studies 1 - 3 of COVID-19 vaccine reactogenicity and immunogenicity were limited to convenience samples that may not be generalizable. We studied the association of self-reported postvaccination symptoms with anti–SARS-CoV-2 antibody response among Framingham Heart Study (FHS) participants contributing to the Collaborative Cohort of Cohorts for COVID-19 Research (C4R) study. 4

The FHS is an ongoing, prospective cohort study evaluating cardiovascular disease risk factors. In February 2021, participants were invited to self-administer C4R questions on COVID-19 vaccination (and associated symptoms) and submit a dried blood spot to test for anti–SARS-CoV-2 antibodies (eFigure in the Supplement ). This report includes participants who received 2 doses of mRNA vaccine at least 2 weeks before blood spot collection. Postvaccination symptoms were categorized as systemic symptoms (fever, chills, muscle pain, nausea, vomiting, headache, and/or moderate to severe fatigue) or local symptoms (injection site pain and/or rash). IgG antibodies to SARS-CoV-2 spike subunit were measured using microsphere immunoassay (Luminex), chosen for its successful use in population-based serosurveys. Results were reported as median fluorescence intensity (MFI), with batch-specific reactive antibody response MFI cutoffs. 5 Associations between postvaccination symptoms and antibody response were assessed by χ 2 test and multivariable linear regression, with complete case analyses adjusted for batch, time since vaccination, and sociodemographic and clinical characteristics. A 2-sided P  < .05 was considered statistically significant. Protocols were approved by institutional review boards of participating institutions and the National Heart, Lung, and Blood Institute. Written informed consent was obtained from all participants. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline.

Of 3200 FHS participants eligible to participate in C4R, 928 (29%) completed the C4R questionnaire and blood spot collection and reported 2 doses of BNT162b2 (414 [45%]) or mRNA-1273 (514 [55%]) vaccines (eFigure in the Supplement ). Respondents’ mean (SD) age was 65 (12) years, 360 (39%) were men and 568 (61%) were women, 893 (96%) were non-Hispanic White, and 84 (9%) self-reported prior COVID-19 infection. After either vaccine dose, 446 participants (48%) reported systemic symptoms, 109 (12%) reported local symptoms only, and 373 (40%) reported no symptoms. In bivariate analysis, symptoms were associated with younger age, female sex, prior infection, and the mRNA-1273 vaccine ( Table ). Antibody reactivity was observed in 365 asymptomatic participants (98%), 108 participants (99%) with only local symptoms, and 444 participants (99%) with systemic symptoms ( P  = .08). In adjusted models, systemic symptoms were associated with greater antibody response, although associations were attenuated with sequential adjustment for potential confounders ( Figure ). Similar results were obtained with exclusion of participants with prior COVID-19 infection.

In a sample of twice-vaccinated, older, community-dwelling US adults, self-reported systemic symptoms after SARS-CoV-2 mRNA vaccination were associated with greater antibody response vs local-only or no symptoms, although associations were attenuated with sequential adjustment for potential confounders. These results agree with a previous report 6 in US health care workers that showed higher postvaccination antibody measurements among those with significant symptoms after an mRNA vaccine. This report identifies age, sex, and Moderna vaccine as factors associated with both vaccine reactogenicity and immunogenicity, consistent with prior observations. 3 , 6 No association was observed between symptoms after vaccination and race or ethnicity, body mass index, or comorbidities. In this generalizable cohort, nearly all participants exhibited a positive antibody response to complete mRNA vaccine series. Nonetheless, systemic symptoms remained associated with greater antibody response in multivariable-adjusted models, highlighting unexplained interpersonal variability. Further research on biological mechanisms underlying heterogeneity in vaccine response is needed. Limitations of this report include an older, predominantly non-Hispanic White, professional cohort; potential recall bias; and use of MFI, which is not standardized against neutralizing antibody titers. In conclusion, these findings support reframing postvaccination symptoms as signals of vaccine effectiveness and reinforce guidelines for vaccine boosters in older adults.

Accepted for Publication: September 7, 2022.

Published: October 21, 2022. doi:10.1001/jamanetworkopen.2022.37908

Correction: This article was corrected on November 28, 2022, to fix some incorrect row headers in the Figure and to add some missing information regarding attenuation of the reported associations in the first sentence of the Discussion.

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2022 Hermann EA et al. JAMA Network Open .

Corresponding Author: Emilia A. Hermann, MD, MPH, Division of General Medicine, Department of Medicine, Columbia University Medical Center, 630 W 168th St, PH 9 East, Room 105, New York, NY 10032 ( [email protected] ).

Author Contributions: Drs Hermann and Balte had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Hermann, Lee, Kirkpatrick, Oelsner.

Acquisition, analysis, or interpretation of data: Hermann, Balte, Xanthakis, Kirkpatrick, Cushman, Oelsner.

Drafting of the manuscript: Hermann, Lee, Kirkpatrick, Oelsner.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Hermann, Balte, Oelsner.

Obtained funding: Cushman, Oelsner.

Administrative, technical, or material support: Kirkpatrick, Cushman, Oelsner.

Supervision: Kirkpatrick, Oelsner.

Conflict of Interest Disclosures: Dr Balte reported receiving grants from the National Institutes of Health during the conduct of the study. Dr Oelsner reported receiving grants from the National Heart, Lung, and Blood Institute during the conduct of the study and outside the submitted work. No other disclosures were reported.

Funding/Support: This research was funded in part by agreement 1OT2HL156812 from the National Institutes of Health.

Role of the Funder/Sponsor: The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the National Institutes of Health.

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The Covid-19 Pandemic Has Impacted the Research Productivity of Women More than Men

22 Pages Posted: 15 Aug 2024 Publication Status: Under Review

Lilian Treasure

Michael Okpara University of Agriculture

Hellen Namawejje

Makerere University

Olalekan Adekola

York Saint John University

Annet Mulema

International Development Research Centre (IDRC)

Angela Abasilim

Federal University of Technology Akure

Ngozi Oguguah

Nigerian Institute for Oceanography and Marine Research

Onyekachi Ikeagwu

Ebonyi State University

Victor Nweze

University of Nigeria

The impacts of the COVID-19 pandemic crosscut every sector of society, including the work of researchers. This study investigates the determinants of research productivity of African men and women researchers before and during the COVID-19 pandemic. A cross-sectional quantitative research design, using a questionnaire via Qualtrics, was used to interview 311 researchers (170 men and 141 women) from diverse academic disciplines in African universities and research institutions. The Maximum Likelihood Estimation (MLE) method of the Stochastic frontier model was used to estimate the parameters that predict research productivity by gender. The study found a difference in the productivity of single and married men and women at different probability levels. Single women researchers and their married counterparts had varied levels of research productivity, but men's productivity was unaffected regardless of marital status. An increase in income levels positively influenced research productivity among men; however, there is no significant difference for women. The results also showed that the workload of women researchers in Africa increased during the COVID-19 pandemic. The mental health status of men researchers in Africa was significant and positively related to their research productivity compared to women whose mental health status showed no statistical significance. The study concluded that some key demographic indicators need to be factored into providing an enabling environment that fosters equality of research productivity during a pandemic. Mental health services should be a priority area for investment for women researchers to increase their research productivity.

Keywords: COVID-19, gender, resource access, Africa, research productivity

Suggested Citation: Suggested Citation

Michael Okpara University of Agriculture ( email )

Umuahia-Ikot Ekpene Road PMB 7267 Umudike, 440001 Nigeria

Makerere University ( email )

P.O Box 7062 P.O BOX 7062 Kampala, 256 Uganda

Olalekan Adekola (Contact Author)

York saint john university ( email ).

Lord Mayors Walk York, YO31 7EX United Kingdom

International Development Research Centre (IDRC) ( email )

Federal university of technology akure ( email ), nigerian institute for oceanography and marine research ( email ), ebonyi state university ( email ).

Dept. of Public Administration,Ebonyi State Univer Ashi Polytechnic Anyiin,Logo LGA,Benue State Abakaliki, 053 Nigeria

University of Nigeria ( email )

184 ogui Road Enugu Department of Science Education, Chemistry option Nsukka, DE 410001 Nigeria

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About 400 Million People Worldwide Have Had Long Covid, Researchers Say

The condition has put significant strain on patients and society — at a global economic cost of about $1 trillion a year, a new report estimates.

By Pam Belluck

Pam Belluck has been reporting about long Covid since the condition first emerged.

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• Respiratory signs and symptoms
• Risk factors

With the rapid spread of the novel coronavirus (COVID-19), a sustained global pandemic has emerged. Globally, the cumulative death toll is in the millions. The rising number of COVID-19 infections and deaths has severely impacted the lives of people worldwide, healthcare systems, and economic development. We conducted a retrospective analysis of the characteristics of COVID-19 patients. This analysis includes clinical features upon initial hospital admission, relevant laboratory test results, and imaging findings. We aimed to identify risk factors for severe illness and to construct a predictive model for assessing the risk of severe COVID-19. We collected and analyzed electronic medical records of confirmed COVID-19 patients admitted to the Affiliated Hospital of Jiangsu University (Zhenjiang, China) between December 18, 2022, and February 28, 2023. According to the WHO diagnostic criteria for the novel coronavirus, we divided the patients into two groups: severe and non-severe, and compared their clinical, laboratory, and imaging data. Logistic regression analysis, the least absolute shrinkage and selection operator (LASSO) regression, and receiver operating characteristic (ROC) curve analysis were used to identify the relevant risk factors for severe COVID-19 patients. Patients were divided into a training cohort and a validation cohort. A nomogram model was constructed using the “rms” package in R software. Among the 346 patients, the severe group exhibited significantly higher respiratory rates, breathlessness, altered consciousness, neutrophil-to-lymphocyte ratio (NLR), and lactate dehydrogenase (LDH) levels compared to the non-severe group. Imaging findings indicated that the severe group had a higher proportion of bilateral pulmonary inflammation and ground-glass opacities compared to the non-severe group. NLR and LDH were identified as independent risk factors for severe patients. The diagnostic performance was maximized when NLR, respiratory rate (RR), and LDH were combined. Based on the statistical analysis results, we developed a COVID-19 severity risk prediction model. The total score is calculated by adding up the scores for each of the twelve independent variables. By mapping the total score to the lowest scale, we can estimate the risk of COVID-19 severity. In addition, the calibration plots and DCA analysis showed that the nomogram had better discrimination power for predicting the severity of COVID-19. Our results showed that the development and validation of the predictive nomogram had good predictive value for severe COVID-19.

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Introduction.

Since December 2019, an outbreak of unidentified viral pneumonia occurred in Wuhan. The causative virus was identified as a novel coronavirus distinct from the six known coronaviruses 1 . Compared to other influenza viruses, COVID-19 spreads faster, has a wider reach, and presents more severe symptoms and outcomes. Individuals infected with COVID-19 present a wide range of symptoms, including fever, fatigue, sore throat, dry cough, and more. Among these, fever, dry cough, and pulmonary imaging changes are often common clinical manifestations in COVID-19 patients. The severity of infection can vary, with some people being asymptomatic or non-severe, others developing severe symptoms, and in some cases, it may progress rapidly to cause complications and even be life-threatening. The rising number of COVID-19 infections and deaths has severely impacted the lives of people worldwide, healthcare systems, and economic development. To alleviate the burden on the healthcare system, while providing more precise treatment and minimizing the occurrence of severe cases and fatalities, healthcare professionals need to identify risk factors for severe illness in COVID-19 patients at an early stage and engage in timely and effective disease management. Developing predictive models that incorporate multiple variables or features to assess the risk of severe illness in individuals infected with or post-infection by COVID-19 can assist healthcare providers in managing patients systematically while allocating limited medical resources. Several studies have already developed diagnostic and predictive models for COVID-19. For instance, there are COVID-19 diagnostic prediction models based on symptoms like loss of smell and taste 2 , as well as diagnostic models utilizing high-resolution computer tomography scans with deep learning techniques 3 . Machine learning methods have been employed to classify COVID-19 using CT images 4 . However, these predictive models exhibit varying degrees of inadequacy in terms of discriminative power and accuracy 5 . Due to factors such as ethnicity, region, and other unassessed variables, these models unavoidably possess limitations in terms of their applicability.

With an increasing number of predictive models being developed, inflammatory markers are considered one of the key biological indicators for assessing disease severity and play a vital role in diagnosing and evaluating inflammatory conditions 6 , 7 , 8 , 9 . Research suggests that the Neutrophil-to-Lymphocyte Ratio (NLR) can be used for the diagnosis and assessment of the severity of COVID-19 in patients 10 . Elevated levels of LDH have been significantly associated with the severity and mortality rates of COVID-19 11 . In chest CT scans, severe COVID-19 patients often exhibit bilateral lung involvement, while non-severe cases are more likely to display ground-glass opacities 12 .

Hence, combining patient-specific disease characteristics, laboratory test results, and imaging findings to identify risk factors for severe illness and construct a predictive model for the severity of COVID-19 is of paramount clinical significance. This approach aids in the early identification of severe COVID-19 patients and allows for more proactive treatment strategies.

Materials and methods

Participants.

Confirmed cases of COVID-19 admitted to the Affiliated Hospital of Jiangsu University (Zhenjiang, China) between December 18, 2022, and February 28, 2023, were selected as study subjects according to the following inclusion and exclusion criteria. All eligible patients met the following: (i) Real-time fluorescent RT-PCR detection of the novel coronavirus nucleic acid was positive at Zhenjiang Disease Control and Prevention Center, various levels of hospitals in Zhenjiang, and Jiangsu University Affiliated Hospital, (ii) ≥ 18 years old, (iii) Positive patients with radiological examination results, (iv) Patients who have not received treatment for novel coronavirus infection before their visit, (v) The latest peripheral blood sample results were collected from fasting patients before treatment. All eligible patients should exclude the following: (i) Accompanied by acute infections in other parts (acute pancreatitis, acute cholecystitis, liver abscess, etc.), (ii) Infection of the lungs with other known pathogens, (iii) Pregnant, (iv) Recently used antiplatelet, anticoagulant drugs, immunosuppressants, or other conditions that researchers believe may affect the study results, (v) Patients with missing baseline data or those transferred to other designated hospitals during hospitalization.

According to the inclusion and exclusion criteria, a total of 346 patients were included in this study. Based on the WHO diagnostic criteria, all study subjects were further divided into non-severe and severe groups (Fig.  1 ).

Flowchart of patient selection for this study.

This study was approved and registered by the Ethics Committee of the Affiliated Hospital of Jiangsu University (Approval number: KY2023K1005). In this retrospective study, all participants provided informed consent. We protected the confidentiality of patient information by recognizing and minimizing data collection. The collected data were anonymized— to the greatest degree to ensure the confidentiality of patient information intact.

Research method

Baseline characteristics, laboratory data, and radiological results for each eligible patient were obtained from the electronic medical records system of Jiangsu University Affiliated Hospital. Electronic medical records for each patient were extracted and analyzed by two independent researchers using standardized data collection forms. The present study was approved by The Ethical Review Committee of Jiangsu University Affiliated Hospital (Zhenjiang, China). Approval number: KY2023K1005. All patients provided informed consent. All experiments were performed in accordance with relevant guidelines and regulations.

Clinical baseline data mainly included the following information for each patient: (i) General information: age (years), gender (male/female). (ii) Smoking history (yes/no), alcohol consumption history (yes/no). (iii) Past medical history: presence or absence of comorbidities such as hypertension, diabetes, heart diseases, nerve system disease, chronic lung disease, liver and kidney disease, and cancer. Laboratory data include complete blood count, biochemical parameters, myocardial enzyme profile, and coagulation function. The latest peripheral blood samples were collected from patients with an empty stomach in the early morning before diagnosis and any treatment. Blood cell analysis was performed using the Sysmex XN3000 automated hematology analyzer (Sysmex Corporation, Japan). Biochemical parameters and blood myocardial enzyme spectrum were measured using the Beckman AU5800 fully automated biochemistry analyzer (Beckman Coulter, Inc.). The coagulation function was analyzed using the automated coagulation analyzer Sekisui CP3000 (Sekisui Medical Corporation, Japan). Chest imaging is done using computed tomography (SOMATOM Definition, Germany). The normal ranges for all indicators were recorded according to the manufacturer’s instructions.

The following parameters were calculated for each group: NLR (neutrophil-to-lymphocyte ratio), PLR (platelet-to-lymphocyte ratio), MLR (monocyte-to-lymphocyte ratio), LMR (lymphocyte-to-monocyte ratio), MRR (monocyte-to-red blood cell ratio), NRR (neutrophil-to-red blood cell ratio), LRR (lymphocyte-to-red blood cell ratio), SII (systemic immune-inflammation index), and SIRI (systemic immune response index).NLR = ANC(× 10 9 /L)/ALC(× 10 9 /L); PLR = PLT(× 10 9 /L)/ALC(× 10 9 /L); MLR = AMC(× 10 9 /L)/ALC(× 10 9 /L); LMR = ALC(× 10 9 /L)/AMC(× 10 9 /L); MRR = AMC(× 10 9 /L)/RBC(× 10 9 /L); NRR = ANC(× 10 9 /L)/RBC(× 10 9 /L); LRR = ALC(× 10 9 /L)/RBC(× 10 9 /L); SII = PLT(× 10 9 /L) × ANC(× 10 9 /L)/ALC(× 10 9 /L); SIRI = ANC(× 10 9 /L) × AMC(× 10 9 /L)/ALC(× 10 9 /L).

Ethical approval and consent to participate

The present study was approved by The Ethical Review Committee of Jiangsu University Affiliated Hospital (Zhenjiang, China). Approval number: KY2023K1005. All patients provided informed consent.

Statistical analysis

Statistical analysis was performed using IBM SPSS statistical software 25.0 (IBM, USA) and R software (version 4.2.2). The Kolmogorov‑Smirnov test was used to evaluate the distribution characteristics of the data. Count data were expressed as percentages (%). Intergroup comparisons were performed using the chi-square test or Fisher’s exact test. If the data followed a normal distribution, they were expressed as mean ± standard deviation (x̄ ± s). For non-normally distributed continuous data, logarithmic transformation was applied, and the distribution characteristics were evaluated again. If the data followed a normal distribution after transformation, they were expressed as median (interquartile range) [M (P25, P75)]. The process of taking the logarithm of variables can transform the data into a relatively uniform scale, thereby avoiding the effects of magnitude differences and reducing the correlation between variables, which can better reveal the true relationship between variables. The data after taking the logarithm still retains some characteristics of the original data, such as the central trend of the data and the relative size relationship. However, taking the logarithm will reduce the volatility of the data and make the data more stable, which is conducive to subsequent data analysis and model establishment. For the measurement data, two independent sample t-tests were used for the between-group comparison. LASSO regression analysis was employed to determine the basic variables associated with the risk of severe COVID-19. For risk factors with p  < 0.05 in the univariate logistic regression analysis, stepwise backward-conditional logistic regression analysis was performed to select independent risk factors associated with non-severe and severe COVID-19. The likelihood ratio test was used to analyze the overall effectiveness of the model. The Hosmer–Lemeshow goodness-of-fit test was used to evaluate the fit of the model. ROC curves were used to evaluate the predictive value of individual or combined markers for the severity of COVID-19. The patients were divided into the training and validation cohorts with a ratio of 7:3 using the R function “createDataPartition” to ensure that outcome events were distributed randomly between the two cohorts. The training cohort was used to construct the model. The validation cohort was used to validate the results obtained using the training cohort. Welch’s two-sample t-test and Pearson’s chi-square test were used to analyze the data distribution characteristics of the training cohort and the validation cohort. A nomogram model was constructed using the “rms” package (version 6.7–1) in R software. Each patient’s clinical and laboratory data were plotted in the nomogram, and the corresponding scores for each variable were obtained. The scores for all variables were summed to obtain a total score, and the vertical line corresponding to the final row of numbers represented the predicted probability, indicating the risk of severe COVID-19 in patients. Calibration was evaluated using the calibration curve. Calibration curves of this model were plotted using R software, and calibration curve analysis can be viewed as a visual Hosmer–Lemeshow test. The data analysis phase flowchart is shown in Fig.  2 .

Flowchart of the data analysis phase.

Baseline characteristics of the study participants

Baseline general information.

A total of 346 patients with positive nucleic acid testing for the novel coronavirus were included in this study. General data on the patient is shown in Table 1 . Among them, 123 cases (35.5%) were classified as severe, and 223 cases (64.5%) as non-severe. The average age of the patients in the severe group was 78.6 ± 10.9 years, while in the non-severe group, it was 73.0 ± 13.9 years. The average age in the severe group was significantly higher than in the non-severe group ( p  < 0.05). There were significant differences in the gender distribution between the two groups. In the severe group, there were 94 males (76.4%) and 29 females (23.6%), while in the non-severe group, there were 143 males (64.1%) and 80 females (35.9%). The proportion of male patients in the severe group was significantly higher than in the non-severe group ( p  < 0.05). The heart rate was 83.13 ± 12.2 breaths per minute in the non-severe group and 92.8 ± 14.0 breaths per minute in the severe group. The respiratory rate in the severe group was significantly higher than in the non-severe group ( p  < 0.05). There were also significant differences in oxygen saturation between the two groups. The oxygenation index in the non-severe group was 96.5 ± 1.5%, while in the severe group, it was 88.4 ± 6.8%. The oxygenation index in the severe group was significantly lower than in the non-severe group ( p  < 0.05).

Initial symptoms

The patient’s symptoms and chest imaging findings are shown in Table 2 . Among the 346 patients with COVID-19 upon admission, the most common initial symptom was a cough, reported by 296 patients, accounting for 85.5% of the cases. Fever was reported by 249 patients, accounting for 72.0% of the cases. Compared to the non-severe group, the severe group had a higher proportion of patients with symptoms such as wheezing, respiratory distress, and altered consciousness, and these differences were statistically significant ( p  < 0.05). The non-severe group had a significantly higher proportion of patients with fatigue as their initial symptom than the severe group ( p  < 0.05). The two groups had no significant differences in other initial symptoms ( p  ≥ 0.05). In terms of radiology, there were 192 cases (55.5%) with ground glass opacities (GGO) in the patients and 167 cases (48.3%) with subpleural lesions. Both of these are common radiological features in COVID-19 patients. Compared with the non-severe group, the severe group had a higher proportion of bilateral lung inflammation, which was statistically significant ( P  < 0.05).

Hematological and inflammatory marker data of the two patient groups

The hematological and immunological marker data were compared between the severe and non-severe groups of patients with COVID-19. The results of the laboratory examinations are presented in Tables 3 , 4 and 5 . Among them, Table 4 shows the results of the Kolmogorov–Smirnov test with normal distribution of inflammation index in patients. The results showed that the inflammatory index of the patients did not follow the normal distribution. There were no statistically significant differences between the two groups in terms of platelet (PLT) and erythrocyte sedimentation rate (ESR) levels ( p  ≥ 0.05). However, the severe group exhibited significantly higher levels of white blood cell count (WBC), absolute lymphocyte count (ALC), absolute neutrophil count (ANC), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), lymphocyte-to-red blood cell ratio (LRR), monocyte-to-red blood cell ratio (MRR), neutrophil-to-red blood cell ratio (NRR), systemic immune-inflammation index (SII), and systemic immune response index (SIRI) compared to the non-severe group. Conversely, the severe group had significantly lower lymphocyte-to-monocyte ratio (LMR) levels than the non-severe group. These differences were all statistically significant ( p  < 0.05).

Peripheral blood biomarker data of the two patient groups

We compare hematological and biochemical parameters between severe and non-severe COVID-19 patients:

The analysis results, as shown in Table 5 , indicate that the levels of total bilirubin (TBIL), direct bilirubin (DBIL), aspartate aminotransferase (AST), blood urea nitrogen (BUN), and creatinine (Cre) were significantly higher in the severe group compared to the non-severe group, with all differences being statistically significant ( p  < 0.05). However, there were no significant differences in indirect bilirubin (IBIL), alanine aminotransferase (ALT), triglycerides (TG), and total cholesterol (TC) between the two groups, with no statistical significance ( p  ≥ 0.05) .

We compare coagulation function parameters between severe and non-severe COVID-19 patients:

The analysis results, as presented in Table 6 , reveal that the levels of international normalized ratio (INR) and D-dimer (DD) were significantly higher in the severe group compared to the non-severe group, with both differences being statistically significant ( p  < 0.05). However, there were no significant differences in prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time (TT), and fibrinogen (FIB) levels between the two groups, with no statistical significance ( p  ≥ 0.05).

We compare cardiac enzyme profile parameters between severe and non-severe COVID-19 patients:

The results indicate that the levels of creatine kinase (CK), creatine kinase-MB (CK-MB), and lactate dehydrogenase (LDH) were significantly higher in the severe group compared to the non-severe group, with all differences being statistically significant ( p  < 0.05).

Selection of risk prediction factors for COVID-19

Lasso regression analysis for covid-19.

In the process of building the regression model, a large number of independent variables can lead to inflated coefficients, potentially causing overfitting. To efficiently extract important variables, LASSO regression was used for the regularization and selection of variables. The degree of complexity adjustment in LASSO regression was controlled by the parameter λ, where a larger λ value indicates a stronger penalty on the variables. The selection of variable combinations depends on the adjustment of λ.

Figure  3 presents the LASSO regression path plot obtained through the R software for variable selection. The changes in each variable’s trajectory were shown, with the logarithm of λ on the x-axis and the regression coefficients of the variables on the y-axis. As λ increases, the regression coefficients of the variables gradually shrunk toward zero. A non-zero coefficient suggested a greater contribution of the variable to the outcome, making it more likely to be retained.

LASSO regression path plot: LASSO regression path plot for variable selection obtained by R software.

Figure  4 displayed the tenfold cross-validation results of the LASSO regression, showing the relationship between the logarithm of λ (log (λ)), the mean squared error (MSE), and the number of variables in the model. When cross-validation is performed, the function will automatically divide the original data set into 10 parts. The function will use 9 of these data sets to train the model and use the remaining 1 data set to test the training results and give the error. This process will be repeated 10 times. In each cross-validation, the function will try to substitute different λ to build the model, so that the model error under different λ is obtained. The dotted line in the middle of the value of positive and negative standard deviation of the logarithm (lambda) range. On the left side of the dotted line the model error logs the most hours of harmonic parameters (lambda) value. For the clinical prediction model, we tend to choose a higher precision of the model. Model error of the hour is the optimal value, when lambda is 0.012, get excellent performance with the least variable number of models.

Tenfold cross-validation results of LASSO regression: show the relationship between log (λ), mean square error (MSE), and the number of variables in the model.

In the end, 26 variables were selected as predictive factors for severe COVID-19, categorized as Age, Height, Day, RR, Heart rate, Oxygen, Mechanical Ventilation, Organ Failure, Fatigue, Eosinophilic Granulocyte%, Basophilic Granulocyte%, ALC, RDW, MPV, CRP, PCT, TBIL, ADA, UA, TG, TC, APO-B, CK, FIB, DD, and LDH.

Multivariable logistic regression analysis of severe and non-severe cases of COVID-19

Based on the LASSO regression, a single-factor logistic regression analysis was conducted on the identified risk factors. Figure  5 shows that risk factors with a significance level of P  < 0.05 were selected, and a backward stepwise method was used to construct the logistic regression prediction model for severe COVID-19. The predictive model achieved an overall accuracy of 80.9%, with an accuracy of 91.0% for non-severe cases and 62.6% for severe cases, indicating a high level of accuracy. The likelihood ratio test demonstrated the effectiveness of the included independent variables in constructing the model ( P  < 0.001), indicating the significance of the model construction. The Hosmer–Lemeshow goodness-of-fit test indicated a good fit of the model to the prediction results, with no significant difference between the predicted probabilities and the actual probabilities ( P  = 0.118). The results, as shown in Table 7 , indicate that increasing age, accelerated respiratory rate, elevated ADA, LDH, and NLR levels were associated with an increased risk of severe COVID-19, with statistically significant differences ( P  < 0.05). In conclusion, age, respiratory rate, ADA level, LDH level, and NLR level are independent predictive factors for severe COVID-19.

A logistic regression prediction model for severe COVID-19.

ROC curve analysis of biomarkers for severe and non-severe cases of COVID-19

ROC curve analysis was performed to evaluate the discriminative ability of Age, RR, LDH, and NLR for distinguishing between non-severe and severe cases of COVID-19. The results, as shown in Table 8 . The results showed that LDH, RR, and NLR exhibited the expected diagnostic value for severe COVID-19, with LDH demonstrating higher diagnostic efficiency. The AUC value for LDH was 0.809 (95% CI 0.761–0.856), with a sensitivity of 78.9% and specificity of 73.5%. The AUC value for RR was 0.772 (95% CI 0.716–0.827), with a sensitivity of 60.2% and specificity of 86.4%, indicating higher specificity in diagnosing severe COVID-19. NLR had lower diagnostic efficiency compared to LDH and RR, with an AUC value of 0.710 (95% CI 0.652–0.767), sensitivity of 61.8%, and specificity of 76.2%. Due to their good performance in ROC curve analysis, LDH, RR, and NLR were selected for further analysis. The AUC values for LDH combined with NLR or RR were 0.817 and 0.814, respectively, which were higher than the diagnostic efficiency of NLR alone (AUC, 0.710) and RR alone (AUC, 0.772), and the sensitivity was also improved (sensitivity of 74.8% and 78.9% respectively). The combined analysis demonstrated that the diagnostic efficiency of the LDH + NLR + RR combined index was higher than that of single indices, with an AUC value of 0.823 (95% CI 0.777–0.869).

As shown in Fig.  5 for the risk factors with a significance level of P  < 0.05, a logistic regression prediction model for severe COVID-19 was constructed by using a reverse step-by-step method.

Construction and validation of the nomogram

The patients were divided into the training and validation cohorts with a ratio of 7:3 using the R function “createDataPartition” to ensure that outcome events were distributed randomly between the two cohorts. The training cohort was used to construct the model. The validation cohort was used to validate the results obtained using the training cohort. A total of 346 COVID-19 patients were randomly divided into a training cohort and a validation cohort at a ratio of 7:3. The general data and clinical characteristics of these patients are summarized in Table 9 . In the training and validation cohorts, the mean age of COVID-19 patients was 74 and 76 years, respectively. The respiratory rate was 21.1 ± 3.8 and 21.1 ± 3.5 breaths per minute, and the heart rate was 86 ± 14 and 87 ± 13 beats per minute. There were 212 patients using mechanical ventilation in the training cohort and 90 in the validation cohort. In the training and validation cohorts, the mean LDH was 267 ± 236 and 271 ± 164, ADA 14.8 ± 6.0 and 15.3 ± 12.7, SII 2,016 ± 2,514 and 2,445 ± 4,977, and NLR 11 ± 12 and 11 ± 15, respectively. In the training and validation cohorts, there were 135 and 57 patients with GGO, 115 and 52 patients with subpleural lesions, and 85 and 36 patients with bilateral lung inflammation in the lung imaging examination. The training and validation cohorts were comparable in terms of general data and clinical characteristics ( P  > 0.05).

As shown in Fig.  6 , significant and independent predictors were identified based on regression analysis and clinical considerations to construct a predictive nomogram model. The nomogram model included twelve variables (age, creatinine, respiratory rate, heart rate, mechanical ventilation, lymphocyte count, GGO, subpleural lesions, ADA, LDH, NLR, and SII). The severity of COVID-19 could be estimated by summing the scores of each independent variable and predicting the total score on the lowest scale.

A nomogram to predict the severity of COVID-19.

Nomogram validation and evaluation

In this study, ROC analysis, DCA analysis, and calibration plots were used to test the predictive efficiency of the probability of COVID-19, and the results showed that the nomogram had good prediction efficiency. In the ROC curve, the Y-axis is called the sensitivity, which also becomes the true positive rate. Higher values on the Y-axis represent higher model accuracy. The X-axis is 1-specificity, also known as the false positive rate, and the closer the intersection point between the curve and the X-axis is to 0, the higher the accuracy of the model. The area under the curve (AUC) ranges from 0.5 to 1, and the closer the AUC is to 1, the better the diagnostic effect of the model in predicting the outcome. As shown in Fig.  7 , the AUC of the ROC curve was 0.981 for the training cohort and 0.907 for the internal validation cohort. The AUC values of the two cohorts reflected the good diagnostic effect of the nomogram. In the calibration curve, the X-axis represents the predicted probability of an event and the Y-axis represents the actual probability of an event. The thick gray line represents the ideal reference line when the predicted probability matches the actual probability, while the dashed and solid lines represent the calibration curve for the entire cohort and the model curve built through internal validation. A higher degree of fit between the two indicates a better predictive performance of the nomogram model. As shown in Fig.  8 , the curve representing the risk of severe COVID-19 disease estimated by the model is in good agreement with the probability curve observed in internal validation, indicating that the nomogram performs better in predicting the probability of COVID-19. DCA assessed the clinical validity of the model. Based on the classification results, the X-axis represents the boundary of the expected likelihood value, and the Y-axis represents the normalized net benefit at this boundary. Gray and black reference lines indicate the “all intervention” and “no intervention” hypotheses, respectively. In the threshold probability range of 0.1 to 0.7, DCA curves lie above the two baselines “none” and “all,” indicating that the performance of the model is acceptable in this range. As shown in Fig.  9 , this nomogram has clinical utility. In conclusion, the calibration plot and DCA analysis showed that the nomogram had a good predictive effect on the severity of COVID-19.

ROC curves for the nomogram. ( A ): Training group; ( B ): Validation group.

Calibration curve for predicting the probability of COVID-19 severity. ( A ): Training group; ( B ): Validation group.

Decision curve analysis in the prediction of COVID-19 severity. ( A ): Training group; ( B ): Validation group.

The results of this study indicate that in the severe group, respiratory rate, breathlessness, altered consciousness, NLR, and LDH levels were significantly higher compared to the non-severe group. Imaging findings suggest that in the severe group, there was a higher proportion of bilateral pulmonary inflammation and ground-glass opacities. NLR and LDH were identified as independent risk factors for severe patients. The diagnostic efficiency was maximized when NLR, RR, and LDH were combined. In this study, we developed a COVID-19 severity risk prediction model. It includes twelve variables to predict the risk of severe COVID-19. The total score is calculated by adding up the scores for each of the fourteen independent variables. By mapping the total score to the lowest scale, we can estimate the probability of severe COVID-19 risk.

Regarding COVID-19 diagnostic and survival prognosis models, the National Health Commission of the People’s Republic of China has reported one of the initial validated survival models, which includes ten independent predictive factors (chest imaging abnormalities, age, hemoptysis, dyspnea, altered consciousness, comorbidity count, cancer history, NLR, LDH, DBIL, and TBIL) 13 . However, this prognosis model only includes 59 cases of severe and fatal patients and has not yet been externally validated in different patient populations and healthcare settings in Western societies. Among the expanding list of other models, researchers from the UK reported one of the largest models. They collected observational data from 57,824 COVID patients across 260 hospitals in England, Scotland, and Wales. Their severity score includes eight variables (age, gender, comorbidity count, respiratory rate, peripheral blood oxygen saturation, consciousness level, BUN, and CRP) 14 . Another approach from Spanish researchers focuses on prognosis features directly related to the pathophysiology of COVID-19 rather than patient characteristics, constructing a model for the mortality of severe patients based on peripheral oxygenation levels during hospitalization, ANC, PLT, LDH, and CRP 15 . Although some common variables are shared among these models, there is significant variation in predictive outcomes. Other studies have also reported models based on deep learning algorithms, using CT images to predict the severity of COVID-19, showing high accuracy 16 , 17 . However, these models are challenging to construct due to their complex algorithms. Consequently, these models may not be suitable for all institutions and healthcare professionals.

Therefore, we are attempting to develop a novel predictive model for the risk of severe COVID-19. This predictive model relies solely on clinical manifestations, laboratory indicators, and imaging features. These are readily obtainable and identifiable in a clinical setting. Ultimately, based on the observed results, we aim to use a risk score to predict the risk of severe illness in COVID-19 patients.

First, we analyzed the clinical characteristics of the patients in the study. The most common clinical presentations were fever, cough, and phlegm production. Among patients in the severe group, there was a higher prevalence of increased respiratory rate, breathlessness, and altered consciousness as first symptoms compared to the non-severe group, and this difference was statistically significant. Additionally, the severe group had significantly older individuals when compared to the non-severe group. In both groups, the prevalence of severe cases was higher in males than in females, which is consistent with previous research, further underscoring the significant association between age, gender, and disease severity 18 , 19 , 20 , 21 . Smoking history has been considered a risk factor for severe COVID-19 22 . Similarly, Mehra et al. demonstrated a higher in-hospital mortality rate among current smokers in COVID-19 patients 23 . However, in our study, no significant difference was observed between the severe and non-severe groups, in contrast to some prior studies. Variations in inclusion criteria or sample size differences between study populations might explain the disparities between our findings and those of previous studies. Our results indicated a significantly higher likelihood of severe illness in patients with comorbidities such as hypertension, diabetes, and heart diseases, which aligns with previous research, supporting the notion that patients with underlying conditions are more likely to progress to severe illness. While previous studies have shown that chest CT scans of non-severe COVID-19 patients often display ground-glass opacities, our study found a higher proportion of ground-glass opacities in the severe group. This difference may be due to the relatively small sample size in our study, potentially introducing some bias. Further research with larger sample sizes is needed to validate these findings.

After discussing the clinical characteristics of COVID-19, we analyzed the immunological features of peripheral blood in COVID-19 patients. Compared to non-severe patients, severe patients had elevated white blood cell and neutrophil counts upon admission, while lymphocyte counts were significantly reduced. This is in line with results from other related studies 12 , 20 , 24 , 25 and is believed to be an effect of the virus on T cells through ACE2 receptor infection 26 . In comparison to non-severe patients, severe patients had a higher NLR, and this difference was statistically significant. NLR is particularly useful. It is associated with systemic inflammatory status and disease activity. Additionally, NLR has prognostic value in cardiovascular diseases 27 , autoimmune diseases 28 , tumors 28 , and other infectious diseases 29 . Some scholars have indicated that NLR is an early marker of infection in COVID-19 patients 30 , as virus-induced inflammation raises NLR levels. Elevated NLR further promotes the progression of COVID-19. Some studies have also identified the role of NLR in distinguishing COVID-19 severity and predicting mortality 10 , 20 , 31 , 32 , 33 , and our study’s results are consistent with these findings. In our multifactorial logistic regression model, NLR emerged as a crucial predictive factor for the severity of COVID-19 in patients. Our data showed a significant increase in LDH levels among severe patients. Some studies have suggested that elevated serum LDH levels are an independent predictive factor for severe cases 11 , which aligns with our findings in the current study. To summarize, our research further validates the use of NLR and LDH in predicting COVID-19 severity. In this study, through the observation of ROC curves, we noted that NLR, RR, and LDH have the potential to distinguish between severe and non-severe COVID-19 patients. Particularly, combined LDH and NLR testing exhibits high specificity. The predictive efficiency is maximized when NLR, RR, and LDH are combined.

In summary, we hypothesize that these clinical characteristics, laboratory indicators, and imaging findings combined may be more useful for clinicians as practical tools in assessing the severity and prognosis of COVID-19 patients. Therefore, we used twelve variables, including age, RR, HR, mechanical ventilation, ALC, ADA, LDH, NLR, SII, GGO in chest CT, subpleural lesions, and bilateral pulmonary inflammation, to construct the COVID-19 severity prediction model. Finally, by adding the scores of each of the twelve independent variables, we calculated a total score. By mapping the total score to the lowest scale, we were able to estimate the severity risk of COVID-19 patients. Nomograms are a reliable tool for creating statistical prediction models, resulting in simple and intuitive charts that quantify the risk of clinical events. ROC, calibration curve, and DCA analysis were used to validate the nomogram model, which could be used to judge the prediction effect of the nomogram. Therefore, compared to other clinical prediction models, the model we have established is faster, simpler, and more practical.

Finally, it should be acknowledged that this study has some limitations. First, this is a single-center retrospective study. The study population was relatively small, which inevitably led to some bias. In the future, we can conduct multicenter studies to expand the scope of the study population and validate the results of this study. Secondly, being a retrospective study, data were collected based on electronic records from the hospital, and the accuracy and reliability may vary across different hospitals. We can increase the researcher’s follow-up data collection scope, join more hospitals in data collection, and sorting, and the right, as far as possible, improve the accuracy and reliability of the data. Thirdly, we cannot exclude the potential influence of certain treatments received before admission on age, respiratory rate, heart rate, mechanical ventilation use, organ failure comorbidity, absolute lymphocyte count, ADA, LDH, NLR, SII, and chest CT outcomes. Despite these limitations. Despite these limitations, this COVID-19 severity risk prediction model offers the advantage of combined prediction, allowing for a more comprehensive and systematic assessment of the severity of COVID-19 patients. In this regard, we can carry out early medical history tracking when collecting patients’ data in the later stage, understand the basic situation of patients before admission in detail, reduce some unnecessary influencing factors as much as possible, and make the research results more accurate and reliable.

In conclusion, the utilization of 12 patient features at the time of their visit can be used to generate a single variable, and the risk score from the line chart helps predict an individual’s risk of severity in COVID-19. We also confirmed during the model-building process that the combined use of NLR, RR, and LDH can enhance the predictive efficiency of COVID-19. Using the severity prediction model and assessing relevant parameters aids in identifying severe COVID-19 patients. Early medical intervention and support for these high-risk patients may help reduce the severity and mortality rates of this disease.

This study found significant differences in RR, NLR, and LDH between severe and non-severe COVID-19 patients and demonstrated an enhanced predictive efficiency when combining NLR, RR, and LDH. A nomogram model was constructed by integrating patients’ clinical characteristics, laboratory tests, and imaging findings. The calibration plot and DCA analysis showed that the nomogram had better clinical benefit and utility in predicting the severity of COVID-19. It may assist healthcare providers in the early identification of severe cases and the timely implementation of effective treatments.

Data availability

We feel great thanks for your professional review work on our manuscript. The full data set used in this study is available on reasonable request from the corresponding author at [email protected]. As for the conditions of data use, we want the demander to indicate the way and purpose of data use. At the same time, we may require the demander to keep the data strictly confidential, and only use it for relevant research. The final decision to give the data was made after review by the corresponding author.

Abbreviations

The novel coronavirus

Receiver operating characteristic

• Neutrophil-to-lymphocyte ratio

Lactate dehydrogenase

Respiratory rate

Standard error

Confidence interval

Ground glass opacities

Platelet-to-lymphocyte ratio

Monocyte-to-lymphocyte ratio

Lymphocyte-to-monocyte ratio

Monocyte-to-red blood cell ratio

Neutrophil-to-red blood cell ratio

Lymphocyte-to-red blood cell ratio

Systemic immune-inflammation index

Systemic immune response index

White blood cell count

Absolute lymphocyte count

Absolute neutrophil count

Erythrocyte sedimentation rate

C-reactive protein

Total bilirubin

Direct bilirubin

Indirect bilirubin

Alanine aminotransferase

Aspartate transaminase

Blood urea nitrogen

Triglycerides

Total cholesterol

Prothrombin time

Prothrombin international normalization ratio

Activated partial thromboplastin time

Thrombin time

Creatine kinase

Creatine kinase-MB

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The present study was supported by a grant from the National Natural Science Foundation of China (No. 81370119). The present study was supported by China International Medical Foundation (Z-2014-08-2209). The present study was supported by Zhenjiang Science and Technology Innovation Fund (SH2023082).

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These authors contributed equally: Fen-Hong Qian and Yu Cao.

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Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Jiangsu University, No.438, Jiefang Road, Jingkou District, Zhenjiang, Jiangsu, China

Fen-Hong Qian, Yu Cao, Yu-Xue Liu, Jing Huang & Rong-Hao Zhu

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F.H.Q. and Y.C. contributed significantly to the conceptualization and design of this work. F.H.Q. and Y.C. contributed equally to this manuscript. Y.C. and Y.X.L. carried out the data collection and data analysis. Y.C. pre-processed the data and participated in drafting and revising the manuscript. F.H.Q. critically revised the manuscript for important content and finally approved the manuscript for publication. J.H. and R.H.Z. confirmed the authenticity of all original data. All authors have read and approved the final version of the manuscript.

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Qian, FH., Cao, Y., Liu, YX. et al. A predictive model to explore risk factors for severe COVID-19. Sci Rep 14 , 18197 (2024). https://doi.org/10.1038/s41598-024-68946-y

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DOI : https://doi.org/10.1038/s41598-024-68946-y

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