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Research at The Open University

Research that transforms lives.

We use our research to transform lives across the four nations and globally.

We take a challenge-led and open approach to research through our Open Societal Challenges (OSC).

These Challenges are driving real change across the themes of Sustainability, Tackling Inequalities and Living Well.

We need the brightest minds to address the most pressing needs of our time and we are building a more diverse next generation of research leaders to continue to support this and to broaden our horizons.

Research news and articles

OU receives £300k grant to explore legacies of colonialism in Malaysia

The Open University has recently secured almost £300,000 funding from the British Academy for a project that will explore coloniality and precarity engagements amongst urban-poor resilient Malaysian Indian women.

Literature review published on the wellbeing and mobility of female doctorate holders

An Open University led research review has identified inequity during the early career period for female doctoral graduates inside and outside academia.

£1.5 million grant for Centre for Electronic Imaging collaboration

The Open University has received funding in excess of £1.5 million from Teledyne e2v for phase five of a collaboration agreement that sponsors blue skies OU research in advanced detector technology for space imaging.

OU receives funding to understand anti-Catholicism prejudice

The Open University has received £340,000 funding from the Leverhulme Trust to look into anti-Catholicism in the UK and Ireland since 1945.

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Capturing the secrets of the Universe

Is justice affected by people's ethinicity?

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Discover Research at The Open University

Open Research Online (ORO) is the Open Access repository of research outputs from The Open University's research community.

The service is publicly accessible and can be browsed and searched freely. It is home to thousands of peer reviewed publications.

Browse ORO or search using the box below.

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Open Research Online - ORO

Discover research at the open university.

Browse by OU author

Alternatively, browse by Year Thesis Unit Research Group Student Research

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🇺🇦    make metadata, not war

A comprehensive bibliographic database of the world’s scholarly literature

The world’s largest collection of open access research papers, machine access to our vast unique full text corpus, core features, indexing the world’s repositories.

We serve the global network of repositories and journals

Comprehensive data coverage

We provide both metadata and full text access to our comprehensive collection through our APIs and Datasets

Powerful services

We create powerful services for researchers, universities, and industry

Cutting-edge solutions

We research and develop innovative data-driven and AI solutions

Committed to the POSI

Cost-free PIDs for your repository

OAI identifiers are unique identifiers minted cost-free by repositories. Ensure that your repository is correctly configured, enabling the CORE OAI Resolver to redirect your identifiers to your repository landing pages.

OAI IDs provide a cost-free option for assigning Persistent Identifiers (PIDs) to your repository records. Learn more.

Who we serve?

Enabling others to create new tools and innovate using a global comprehensive collection of research papers.

“ Our partnership with CORE will provide Turnitin with vast amounts of metadata and full texts that we can ... ” Show more

Gareth Malcolm, Content Partner Manager at Turnitin

Academic institutions.

Making research more discoverable, improving metadata quality, helping to meet and monitor open access compliance.

“ CORE’s role in providing a unified search of repository content is a great tool for the researcher and ex... ” Show more

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Researchers & general public.

Tools to find, discover and explore the wealth of open access research. Free for everyone, forever.

“ With millions of research papers available across thousands of different systems, CORE provides an invalu... ” Show more

Jon Tennant, Rogue Paleontologist and Founder of the Open Science MOOC

Helping funders to analyse, audit and monitor open research and accelerate towards open science.

“ Aggregation plays an increasingly essential role in maximising the long-term benefits of open access, hel... ” Show more

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Our services, access to raw data.

Create new and innovative solutions.

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Find relevant research and make your research more visible.

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Manage how your research content is exposed to the world.

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Our partnership with CORE will provide Turnitin with vast amounts of metadata and full texts that we can utilise in our plagiarism detection software.

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Executive Director of the Confederation of Open Access Repositories (COAR)

CORE has significantly assisted the academic institutions participating in our global network with their key mission, which is their scientific content exposure. In addition, CORE has helped our content administrators to showcase the real benefits of repositories via its added value services.

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Aggregation plays an increasingly essential role in maximising the long-term benefits of open access, helping to turn the promise of a 'research commons' into a reality. The aggregation services that CORE provides therefore make a very valuable contribution to the evolving open access environment in the UK.

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Open Educational Resources at The Open University

Furthering our understanding of OER and informal learning

You are here.

Projects led by The Open University (OU) around open educational resources (OER) aim to tackle a range of questions from 'What is the impact on teaching and learning of OER?' to 'How can we help researchers and practitioners in the OER field to contribute to the evidence of OER effectiveness?' Research around OER is broad and can be institutionally driven or set by external funders. 

The Institute of Educational Technology (IET) leads research into open learning at the OU.  It has a research focus on understanding why openness is important to the future of learning, identifying activities that need to be supported and showing how evidence of impact can influence policies and decisions.

IET and Open Media and Informal Learning (OMIL) work closely specifically around the research and delivery of open educational resources (OER) and within that, the use of OpenLearn Create as a platform for large-scale OER projects that incorporate social media, assessment and user collaboration.

Institutional research into OER

The OU is constantly striving to find out more about our informal learners.  We undertake surveys and review statistical data to uderstand the demographics of our many thousands of informal learners, their motivations and expectations on all our open platforms and media channels.  Data gathered in this way is important both to improve our widening participation offering and for research purposes.

Open Research Online

The Open University's Open Research Online  is an open access respository of OU research publications and other activities by OU staff. Below is a selection of OER papers:

View the ORO RSS feed of OER papers

• May 24 roundup
• Adios JIME!
• Final Open programme role
• Say hello to PEE – your Personal Engagement Environment
• April 24 round up
• The price of process
• March 24 round up

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Articles and conference papers

How to make your article open access.

Articles, conference papers and other peer-reviewed short-form research outputs are some of the items that are most commonly published open access. 

This page includes a step-by-step guide to publishing these outputs open access, along with the resources to help you do so.

On this page

Key information – read this first, step-by-step guide, funders with open access policies on journal articles and conference papers.

Back to top

Follow these steps to publish your article or conference paper open access.

1. Identify where to publish

Use 'Think. Check. Submit' to assess the relevant journals and choose which is most suitable.

2. Check any policy requirements

If you receive funding for your research, you will need to comply with your funder’s requirements for open access. (You may also find that they will give you the funding for open access publishing.)

If you don’t receive funding, there will still be policies you need to be aware of.

Whether or not you are funded, you will need to comply with the University's publication policy. You may also be subject to the REF, so check its policy too.

3. Use Sherpa Romeo to assess the open access routes available to you

Sherpa Romeo is an online service that helps you to check which open access route or model your chosen journal will allow.

4. Submit your manuscript

Send your article or conference paper to the publisher. Remember to use your Oxford email (ox.ac.uk) during the submission process.

Tip: If you have an Oxford-registered ORCID this process might be simplified, because logging in using your ORCID often auto-populates the form.

5. Pay any fees

When your article is accepted, you may have to pay a fee to the publisher (an ‘article processing charge’). This will depend on their open access route/model:

• If the journal is diamond/platinum, there will be no charge.
• If the journal is fully gold you may have to pay a charge (you can find out on Sherpa Romeo).
• If the journal is hybrid you will have to pay a charge to make your work open in the journal itself. But you can also make your work open access (at no cost) using the 'green' open access deposit method via the Oxford University Research Archive (ORA).

6. Congratulations! Your work is now open access

Depending on the journal's open access allowances, either:

• The journal itself has made the work open (gold/platinum/paid hybrid), or
• The hybrid journal is paywalled but you have made the work open access by depositing using the 'green route'.

The Open University of Sri Lanka (OUSL) is the only National University which offers a wide range of academic programmes of study through the Open and Distance Learning (ODL) mode.  

The flexibility and ease of access of this system has enabled the University to cater to the needs of prospective students from all parts of the country, regardless of their level of formal education, socio-economic background or age. It offers Certificate, Diploma, Degree and Postgraduate Degree programmes in a wide range of disciplines through its five Faculties, namely, Education, Engineering Technology, Health Sciences, Humanities & Social Science and Natural Sciences and the Postgraduate Institute of English (PGIE).

The Open University takes special interest in fostering research and scholarship, and is a pioneer in research in Open and Distance Learning. 

About the Event

The International Research Conference of the Open University of Sri Lanka 2024 (IRC-OUSL 2024) will be held on 07 th and 08 th of November 2024 as a hybrid conference.

IRC-OUSL 2024 offers a unique forum for academics and researchers both within Sri Lanka and overseas to present and discuss their research findings in a wide range of disciplines. There will be five parallel technical sessions and six panel discussions on 07 th and 09 th November 2024.

The Organizing Committee of IRC-OUSL 2024 is pleased to invite academics and researchers to submit their research findings to be presented at the conference.

Abstracts can be submitted under the following sub-themes:

Open and distance learning.

• Quality Assurance for Higher Education
• English Language Teaching
• Engineering and technology
• Health Sciences
• Life Sciences – Biological Sciences, Agriculture, Forestry
• Physical Sciences
• Environmental Sciences
• Humanities and Social Sciences

Striving for a Sustainable Future through Innovation

Important dates, call for abstracts.

• 25 th March 2024 - 31 st May 2024

Finding of the Novel research may be submitted for oral presentation at the IRC-OUSL 2024. Submissions should include the extended abstract (for reviewing and publishing)

• Guideline for Abstracts/Extended Abstracts Preparation
• Template for Abstracts
• Template for Extended Abstracts
• Guidelines for Oral Presentation
• Microsoft CMT conference management sysytem - Guideline for Authors

Completed submissions can be made through the Microsoft CMT website

The Author declaration form and  Checklist for Authors should be filled after the initial submission of the Extended abstract, through the online system. The completed Author Declaration Form should be either scanned and emailed to [email protected]  or posted to the Director Research, Research Unit, The Open University of Sri Lanka, PO Box 21, Nugegoda, Sri Lanka .

• Author Declaration
• Author Checklist

Notification of Acceptance of Abstracts

• 01 st July 2024

Once accepted, the corresponding authors will be notified by  01 st  July 2024 . The abstract together with reviewer comments will be made available to the authors.

Early Bird Registration

• 01 st July 2024 - 15 th August 2024
• OUSL Academic/Administrative/Academic support staff and Students: Free of Charge

Regular Registration

• 01 st September 2024 - 30 th September 2024

Submission of Camera Ready Copy

• 15 th September 2024 - 30 th September 2024

The deadline of receiving camera–ready copy of the Final Revised abstracts including suggested changes to the abstract will be  30th Sep 2024 . No revised abstracts will be accepted after the deadline. These abstracts will be published in the conference proceedings.

IRC-OUSL 2024

• 07 th and 08 th November 2024

IRC-OUSL 2024 will be held on 07 th , 08 th  November 2024.

Once accepted, the corresponding authors will be notified by 01 st July 2024 . The abstract together with reviewer comments will be made available to the authors.

The deadline of receiving camera–ready copy of the Final Revised abstracts including suggested changes to the abstract will be 30th Sep 2024 . No revised abstracts will be accepted after the deadline. These abstracts will be published in the conference proceedings.

learning opportunities to anyone, anywhere, and at any time.

Contact the Organizers

Director Research Prof Shyama R. Weerakoon Tel: +94 (11) 288 1510,  Ext:1135

Email: [email protected]

• More Details (https://ou.ac.lk/research/)

Feel free to ask a question or simply leave a comment.

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Our open access publishing is key to delivering on our mission

Open access (OA) is a key part of how Oxford University Press (OUP) supports our mission to achieve the widest possible dissemination of high-quality research.  We publish rigorously peer-reviewed, world-leading, trusted open access research, upholding the highest standards of publication ethics and integrity.

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OUP believes that the move to open access and open research needs to be equitable and inclusive for all. We want to ensure that authors can publish in their journal of choice. As part of our Developing Countries Initiative , corresponding authors based in qualifying countries publishing in any of OUP’s fully open access journals are eligible for a full waiver of their open access charge.

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Publishing your book on an OA basis makes your work freely available online, with no barriers to access. OUP applies the same peer review and editorial development processes to all books whether published open access or under a customer sales model.

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*Data source: Altmetric. Comparing number of policy and patent document mentions, relative to number of articles published, to Cambridge University Press, Elsevier, Frontiers, Hindawi, Institute of Physics Publishing, MDPI, PLOS, Sage, Springer Nature, Taylor & Francis, and Wiley.

**Data source: Dimensions. Comparing the mean lifetime citation rate of open access articles to those published by Cambridge University Press, Elsevier, Frontiers, Hindawi, Institute of Physics Publishing, MDPI, PLOS, Sage, Springer Nature, Taylor & Francis, and Wiley.

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The SpringerOpen portfolio has grown tremendously since its launch in 2010, so that we now offer researchers from all areas of science, technology, medicine, the humanities and social sciences a place to publish open access in journals. Publishing with SpringerOpen makes your work freely available online for everyone, immediately upon publication, and our high-level peer-review and production processes guarantee the quality and reliability of the work. Open access books are published by our Springer imprint.

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Apollo 11 and 50 years of research on Moon rocks

President Kennedy’s famous “We choose to go to the Moon” speech to a crowd of 40,000 people on 12th September 1962 resonated widely with the American population and dampened some of the disquiet about the cost and value of the Moon-landing effort. It also started the space race with the Soviet Union. To quote Kennedy, “We set sail on this new sea because there is new knowledge to be gained, and new rights to be won, and they must be won and used for the progress of all people. For space science, like nuclear science and all technology, has no conscience of its own.”

When Apollo 11 successfully landed on the Moon on 20th July 1969 (20:17 GMT), history was made. Arguably this was the most important event for humankind and certainly one of the most important events of the 20th century. Astronauts Neil Armstrong and Buzz Aldrin, who spent 21.5 hours on the Moon, including 2.5 hours for an extra-vehicular activity (EVA) at the lunar surface, and the command module pilot Michael Collins became national and international heroes, and their exploits effectively ended the space race.

Perhaps their most important task was to collect samples of the lunar surface. Apollo 11 returned 22kg of lunar samples to Earth.

During the EVA, Armstrong and Aldrin performed several experiments including a soil mechanics investigation to study the properties of the lunar soil, a solar wind composition experiment, a passive seismic experiment to detect lunar ‘moonquakes’, and a laser ranging retroreflector to very precisely measure the distance between the Earth and Moon. But perhaps their most important task was to collect samples of the lunar surface. Apollo 11 returned 22kg of lunar samples to Earth.

The Apollo 11 landing was the first of six successful landings on the Moon – a phase of exploration which ended on 14th December 1972 when the lunar module of Apollo 17 lifted off from the lunar surface. In this short period of time, 12 men walked on the lunar surface, and with the help of wheeled transport in the later missions they collected 2,200 samples (rocks and soils) weighing 382kg.

To this day, those samples are continuing to be used to provide important clues into the origin and evolution of the Moon. Planetary scientists at The Open University are at the forefront of that work, and for the 50th anniversary have been working with NASA to produce a virtual microscope collection of over 550 rocks collected during the Apollo missions . These detailed images are a starting point for many investigations worldwide and show how important the Apollo samples still are to the scientific community. Indeed, NASA lunar curators often use virtual microscope images when allocating samples for new investigations.

At The Open University, virtual microscope images are being used as part of a search for water on the Moon. The conventional view has been that the Moon is devoid of water, but in 2008, a journal article led by Dr Alberto Saal from the Brown University.

Data from these apatites and melt inclusions suggest the presence of a water reservoir in the Moon similar to that of certain regions in the Earth’s interior.

USA reported first direct detection of ‘water’ (measured as hydroxyl, OH) in lunar volcanic glasses. Almost coincidentally, remote detection of ‘water’ (present either as OH or H2O) was reported by scientists working with data collected by the NASA’s Moon Mineralogy Mapper spectrometer which was on board the first Indian Space Research Organisation orbiter mission to the Moon (Chandrayaan-1).

Over the past decade, our team at The Open University has been undertaking cutting-edge laboratory research to look for water (and other associated volatiles such as C, N, O, S and Cl) in Apollo samples and on lunar meteorites. We find it in one of the accessory minerals found in lunar rocks – the mineral apatite, and inclusions of melt trapped during the early stages of magma cooling. To be precise, we don’t find ‘water’, the molecule H2O. Rather, we find hydrogen in the form of a hydroxyl anion, OH.

Data from these apatites and melt inclusions suggest the presence of a water reservoir in the Moon similar to that of certain regions in the Earth’s interior. Furthermore, the isotopic composition of water in the lunar samples points towards water in the Moon having a common origin with that of the Earth, and the majority of this water seemed to have been delivered by asteroidal material as opposed to cometary sources. Nevertheless, this field of research remain very active as many new questions have arisen through recent findings. Therefore, understanding the origin and evolution of the Moon is very much a work in progress, and samples brought back by Apollo missions continue to play a vital role in this endeavour.

This article was originally published in the Summer 2019 edition of The Geographer - the newsletter of the Royal Scottish Geographical Society.

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Explore Moon rocks collected from the first Moon landing

Use our Virtual Microscope to examine a selection of Moon rocks collected by Neil Armstrong and Buzz Aldrin, from the Apollo 11 mission in 1969.

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Moons of our Solar System

In this free course, Moons of our Solar System, explore the many moons of our Solar System. Find out what makes them special. Should we send humans to our Moon again?

As the only planetary body everyone is familiar with seeing in the sky, the Moon has long been an object of fascination and speculation. This free course will teach you about the nearest planetary body to Earth: the missions to the Moon, the basic facts of its composition, the cratering on its surface, and the ancient eruptions that flooded many...

Microgravity: living on the International Space Station

This free badged open course, Microgravity: living on the International Space Station, will help you to consider microgravity environments in more detail than before! You will make direct comparisons of some important physical values between the International Space Station (ISS), the Earth and the Moon.

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• Originally published: Monday, 8 July 2019
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• 30 May 2024

Japan’s push to make all research open access is taking shape

• Dalmeet Singh Chawla 0

Dalmeet Singh Chawla is a freelance science journalist based in London.

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Japan plans to make all publicly funded research available to read in institutional repositories. Credit: Toru Yamanaka/AFP via Getty

The Japanese government is pushing ahead with a plan to make Japan’s publicly funded research output free to read. In June, the science ministry will assign funding to universities to build the infrastructure needed to make research papers free to read on a national scale. The move follows the ministry’s announcement in February that researchers who receive government funding will be required to make their papers freely available to read on the institutional repositories from January 2025.

The Japanese plan “is expected to enhance the long-term traceability of research information, facilitate secondary research and promote collaboration”, says Kazuki Ide, a health-sciences and public-policy scholar at Osaka University in Suita, Japan, who has written about open access in Japan .

The nation is one of the first Asian countries to make notable advances towards making more research open access (OA) and among first countries in the world to forge a nationwide plan for OA.

The plan follows in the footsteps of the influential Plan S, introduced six years ago by a group of research funders in the United States and Europe known as cOAlition S , to accelerate the move to OA publishing . The United States also implemented an OA mandate in 2022 that requires all research funded by US taxpayers to be freely available from 2026.

Institutional repositories

When the Ministry of Education, Culture, Sports, Science and Technology (MEXT) announced Japan’s pivot to OA in February, it also said that it would invest ¥10 billion (around US$63 million) to standardize institutional repositories — websites dedicated to hosting scientific papers, their underlying data and other materials — ensuring that there will be a mechanism for making research in Japan open.

Among the roughly 800 universities in Japan, more than 750 already have an institutional repository, says Shimasaki Seiichi, director of the Office for Nuclear Fuel Cycles and Decommissioning at MEXT in Tokyo, who was involved with drawing up the plan. Each university will host the research produced by its academics, but the underlying software will be the same.

In 2022, Japan also launched its own national preprint server, Jxiv , but its use remains limited with only the few hundred preprint articles posted on the platform to date. Ide says that publishing as preprints is not yet habitual among many researchers in Japan, noting that only around one in five respondents to his 2023 survey 1 on Jxiv were even aware that it existed.

Japan’s move to greater access to its research is focusing on ‘green OA’ — in which authors make the author-accepted, but unfinalized, versions of papers available in the digital repositories, says Seiichi.

Seiichi says that gold OA — in which the final copyedited and polished version of a paper is made freely available on the journal site — is not feasible on a wide scale. That’s because the cost to make every paper free to read would be too high for universities. Publishers levy an article-processing charge (APC) if the paper is made free to read, rather than being paywalled, a fee that covers a publisher's costs.

APCs are increasing at an average rate of 4.3% per year, notes Johan Rooryck, a scholar of French linguistics at Leiden University in the Netherlands, and executive director of cOAlition S.

Rooryck says that Japan’s green OA strategy should be applauded. “It's definitely something that one should do,” he says. “Especially for all the content that is still behind the paywall.”

Kathleen Shearer, executive director of the Confederation of Open Access Repositories in Montreal, Canada, says that the Japanese plan is “equitable”.

“It doesn’t matter where you publish, whether you have APCs or not, you are still able to comply with an open-access policy,” she says.

She adds that the policy will mean that Japan has a unified record of all research produced by its academics because all institutional repositories are hosted on the same national server. “Japan is way ahead of the rest of us,” Shearer says. “More countries are moving in this direction but Japan really was one of the first.”

Focusing on institutional repositories will have another benefit: it will not discriminate against research published in Japanese, Shearer says. “A big part of their scholarly ecosystem is represented in Japanese.”

Japanese research is no longer world class — here’s why

The plan to move to OA and support Japanese universities’ repositories comes as Japan grapples with its declining standing in international research.

In a report released last October, MEXT found that Japan’s world-class research status is declining . For instance, Japan’s share in the top 10% of most-cited papers has dipped from 6% to 2%, placing it 13th on the list of nations, despite Japan having the fifth-highest research output.

In March, Japan also vowed to triple its number of doctorate holders by 2040, after another report found that the country’s number of PhD graduates is also declining, making it an outlier among the major economies.

doi: https://doi.org/10.1038/d41586-024-01493-8

Ide, K. & Nakayama, J.-I. Genes Cells 28 , 333–337 (2023).

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RV indicates rhinovirus; RT-PCR, reverse transcription polymerase chain reaction; and wearables, wearable biometric monitoring sensors.

A, H1N1 influenza. B, Rhinovirus. ID indicates identification.

A, Receiver operating characteristic (ROC) curves for the best-performing models of infection status for the H1N1 influenza, rhinovirus, and combined virus challenges. B, Confusion matrices for a sample of models in A. C, Mean (SD) accuracy of leave-one-out, cross-validated (LOOCV) models in A. NI-B indicates noninfected vs infected, both; NI-C, noninfected vs infected, clinical; and NI-D, noninfected vs infected, data driven.

A, H1N1 influenza. B, Rhinovirus. C, Both viruses combined. Mild-moderate, mild to moderate; NI-C, noninfected vs infected, clinical; NI-mild, noninfected vs infected, mild; NI-mild-moderate, noninfected vs infected, mild to moderate; NI-moderate, noninfected vs infected, moderate; and wearables, wearable biometric monitoring sensors.

A, Receiver operating characteristic (ROC) curves for the best-performing models of infection status for the H1N1 influenza, rhinovirus, and combined virus challenges. B, Confusion matrices for a sample of models in A. C, Mean (SD) accuracy of leave-one-out, cross-validated (LOOCV) models in A. Mild-moderate indicates mild to moderate; NI-C, noninfected vs infected, clinical; NI-mild, noninfected vs infected, mild; NI-mild-moderate, noninfected vs infected, mild to moderate; and NI-moderate, noninfected vs infected, moderate.

eTable 1. Features Used in Random Forest Models

eFigure 1. Feature Sets for Every Model

eFigure 2. Confusion Matrices for Best Performing Model Across Viral Challenges and Infection Status Comparisons

eFigure 3. Confusion Matrices for Best Performing Model Across Viral Challenges and Infection Severity Comparisons

eFigure 4. Relative Feature Importance for Best Performing Model for Each Viral Challenge, Infection Status Grouping, and Infection Severity Grouping

eTable 2. Racial Demographics, Sex, and Median Age Across Infection Severity Groups for H1N1 Influenza Viral Challenge and Rhinovirus Viral Challenge

eTable 3. Mean Accuracy, Precision, Sensitivity, F1-Score, AUC of Every Infection Status Model Tested Across Viral Challenges, Number of Hours Post-Inoculation, and Infection Severity Comparisons

eTable 4. Mean Accuracy, Precision, Sensitivity, Specificity, F1-Score, AUC of Every Infection Severity Model Tested Across Individual Viral Challenges, Number of Hours Post-Inoculation, and Infection Severity Comparisons

eTable 5. Mean Accuracy, Precision, Sensitivity, Specificity, F1-Score, AUC of Every Infection Severity Model Tested Across Combined Viral Challenges, Number of Hours Post-Inoculation, and Infection Severity Comparisons

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Grzesiak E , Bent B , McClain MT, et al. Assessment of the Feasibility of Using Noninvasive Wearable Biometric Monitoring Sensors to Detect Influenza and the Common Cold Before Symptom Onset. JAMA Netw Open. 2021;4(9):e2128534. doi:10.1001/jamanetworkopen.2021.28534

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Assessment of the Feasibility of Using Noninvasive Wearable Biometric Monitoring Sensors to Detect Influenza and the Common Cold Before Symptom Onset

• 1 Biomedical Engineering Department, Duke University, Durham, North Carolina
• 2 Duke Center for Applied Genomics and Precision Medicine, Duke University Medical Center, Durham, North Carolina
• 3 Durham Veterans Affairs Medical Center, Durham, North Carolina
• 4 Department of Medicine, Duke Global Health Institute, Durham, North Carolina
• 5 Department of Infectious Disease, Imperial College London, London, United Kingdom
• 6 Department of Pediatrics, University of Virginia School of Medicine, Charlottesville
• 7 Department of Psychiatry, Duke University School of Medicine, Durham, North Carolina
• 8 Department of Medicine, Duke University School of Medicine, Durham, North Carolina
• 9 Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor
• 10 Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina

Question   Can noninvasive, wrist-worn wearable devices detect acute viral respiratory infection and predict infection severity before symptom onset?

Findings   In a cohort study of 31 participants inoculated with H1N1 and 18 participants with rhinovirus, infection detection and severity prediction models trained using data on wearable devices were able to distinguish between infection and noninfection with 92% accuracy for H1N1 and 88% accuracy for rhinovirus and were able to distinguish between mild and moderate infection 24 hours prior to symptom onset with 90% accuracy for H1N1 and 89% accuracy for rhinovirus.

Meaning   This study suggests that the use of wearable devices to identify individuals with presymptomatic acute viral respiratory infection is feasible; because wearable devices are common in the general population, using them for infection screening may help limit the spread of contagion.

Importance   Currently, there are no presymptomatic screening methods to identify individuals infected with a respiratory virus to prevent disease spread and to predict their trajectory for resource allocation.

Objective   To evaluate the feasibility of using noninvasive, wrist-worn wearable biometric monitoring sensors to detect presymptomatic viral infection after exposure and predict infection severity in patients exposed to H1N1 influenza or human rhinovirus.

Design, Setting, and Participants   The cohort H1N1 viral challenge study was conducted during 2018; data were collected from September 11, 2017, to May 4, 2018. The cohort rhinovirus challenge study was conducted during 2015; data were collected from September 14 to 21, 2015. A total of 39 adult participants were recruited for the H1N1 challenge study, and 24 adult participants were recruited for the rhinovirus challenge study. Exclusion criteria for both challenges included chronic respiratory illness and high levels of serum antibodies. Participants in the H1N1 challenge study were isolated in a clinic for a minimum of 8 days after inoculation. The rhinovirus challenge took place on a college campus, and participants were not isolated.

Exposures   Participants in the H1N1 challenge study were inoculated via intranasal drops of diluted influenza A/California/03/09 (H1N1) virus with a mean count of 10 6 using the median tissue culture infectious dose (TCID50) assay. Participants in the rhinovirus challenge study were inoculated via intranasal drops of diluted human rhinovirus strain type 16 with a count of 100 using the TCID50 assay.

Main Outcomes and Measures   The primary outcome measures included cross-validated performance metrics of random forest models to screen for presymptomatic infection and predict infection severity, including accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC).

Results   A total of 31 participants with H1N1 (24 men [77.4%]; mean [SD] age, 34.7 [12.3] years) and 18 participants with rhinovirus (11 men [61.1%]; mean [SD] age, 21.7 [3.1] years) were included in the analysis after data preprocessing. Separate H1N1 and rhinovirus detection models, using only data on wearble devices as input, were able to distinguish between infection and noninfection with accuracies of up to 92% for H1N1 (90% precision, 90% sensitivity, 93% specificity, and 90% F1 score, 0.85 [95% CI, 0.70-1.00] AUC) and 88% for rhinovirus (100% precision, 78% sensitivity, 100% specificity, 88% F1 score, and 0.96 [95% CI, 0.85-1.00] AUC). The infection severity prediction model was able to distinguish between mild and moderate infection 24 hours prior to symptom onset with an accuracy of 90% for H1N1 (88% precision, 88% sensitivity, 92% specificity, 88% F1 score, and 0.88 [95% CI, 0.72-1.00] AUC) and 89% for rhinovirus (100% precision, 75% sensitivity, 100% specificity, 86% F1 score, and 0.95 [95% CI, 0.79-1.00] AUC).

Conclusions and Relevance   This cohort study suggests that the use of a noninvasive, wrist-worn wearable device to predict an individual’s response to viral exposure prior to symptoms is feasible. Harnessing this technology would support early interventions to limit presymptomatic spread of viral respiratory infections, which is timely in the era of COVID-19.

Approximately 9% of the world is infected with influenza annually, resulting in 3 million to 5 million severe cases and 300 000 to 500 000 deaths per year. 1 Adults are infected with approximately 4 to 6 common colds per year, and children are infected with approximately 6 to 8 common colds per year, with more than half of infections caused by human rhinoviruses (RVs). 2 , 3 Given the highly infectious nature of respiratory viruses and their variable incubation periods, infections are often transmitted unwittingly in a manner that results in community spread, especially as no presymptomatic screening methods currently exist to identify respiratory viral diseases. 4 , 5 With the increasing emergence of novel viruses, such as SARS-CoV-2, 6 it is critical to quickly identify and isolate contagious carriers of a virus, including presymptomatic and asymptomatic individuals, at the population level to minimize viral spread and associated severe health outcomes.

Wearable biometric monitoring sensors (hereafter referred to as wearables ) have been shown to be useful in detecting infections before symptoms occur. 7 - 9 Low-cost and accessible technologies that record physiologic measurements can empower underserved groups with new digital biomarkers. 8 , 10 - 12 Digital biomarkers are digitally collected data that are transformed into indicators of health and disease. 13 , 14 For example, resting heart rate, heart rate variability, accelerometry, electrodermal skin activity, and skin temperature can indicate a person’s infection status 8 , 9 , 15 - 27 or predict if and when a person will become infected after exposure. 7 Therefore, detecting abnormal biosignals using wearables could be the first step in identifying infections before symptom onset. 8

Here, we developed digital biomarker models for early detection of infection and severity prediction after pathogen exposure but before symptoms develop ( Figure 1 ). Our results highlight the opportunity for the identification of early presymptomatic or asymptomatic infection that may support individual treatment decisions and public health interventions to limit the spread of viral infections.

A total of 39 participants (12 women and 27 men; aged 18-55 years; mean [SD] age, 36.2 [11.8] years; 2 [5.1%] Black, 6 [15.4%] Asian, 25 [64.1%] White, 2 [5.1%] ≥2 race categories [1 (2.6%) White and Caribbean; 1 (2.6%) mixed/other category], and 4 [10.3%] did not fall into any of the ethnic groups listed, so they identified as “all other ethnic groups”) were recruited for the H1N1 influenza challenge study. Data were collected from September 11, 2017, to May 4, 2018. The influenza challenge study was reviewed and approved by the institutional review board at Duke University and the London-Fulham Research Ethics Committee. Written informed consent was obtained from all participants. A total of 24 participants (8 women and 16 men; aged 20-34 years; mean [SD] age, 22 [3.1] years; and 1 [4.2%] Black, 6 [25.0%] Asian, and 15 [62.5%] White, including 3 [12.5%] Hispanic or Latinx, 1 [4.2%] White and Black mixed, and 1 [4.2%] unknown) were recruited for the RV challenge study. Data were collected from September 14 to 21, 2015. The RV challenge study was reviewed and approved by the institutional review board at Duke University and the University of Virginia. Written informed consent was obtained from all participants. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis ( TRIPOD ) reporting guidelines.

Exclusion criteria for the influenza challenge included current pregnancy, breastfeeding, or smoking; history of chronic respiratory, allergy, or other significant illness; recent upper respiratory tract infection; nose abnormalities; or immunocompromised status. Participants were screened for high levels of serum antibodies against the challenge strain by hemagglutination inhibition assay (titers >1:10 excluded). 7 Exclusion criteria for the RV challenge included pregnancy; chronic respiratory illness; high blood pressure; history of tobacco, drug, or alcohol use; and serum antibody titers more than 1:4.

Participants in the H1N1 challenge study wore the E4 wristband (Empatica Inc) 1 day before and 11 days after the inoculation on the morning of day 2, before clinical discharge. The E4 wristband measures heart rate, skin temperature, electrodermal activity, and movement. Participants were inoculated via intranasal drops of the diluted influenza A/California/03/09 (H1N1) virus with a mean count of 10 6 using the median tissue culture infectious dose (TCID50) assay in 1-mL phosphate-buffered saline and were isolated for at least 8 days after inoculation after negative results of a nasal lavage polymerase chain reaction test. 7 We defined symptoms as either observable events (fever, stuffy nose, runny nose, sneezing, coughing, shortness of breath, hoarseness, diarrhea, and wheezy chest) or unobservable events (muscle soreness, fatigue, headache, ear pain, throat discomfort, chest pain, chills, malaise, and itchy eyes). 28 Viral shedding was quantified by nasal lavage polymerase chain reaction each morning, and symptoms were self-reported twice daily.

Participants in the RV challenge study wore the E4 wristband for 4 days before and 5 days after inoculation, which occurred in the afternoon (1-5 pm ) via intranasal drops of diluted human RV strain type 16 with a count of 100 using the TCID50 assay in 1 mL of lactated Ringer solution. Participants underwent daily nasal lavage, and the symptoms were reported as previously described. Participants lived on a college campus and were not isolated.

We grouped individuals by infection similarity ( Figure 2 ) using data-driven methods based on infection severity (asymptomatic or noninfected [AON], mild, or moderate signs of infection) and trajectory (early, middle, or late signs of infection). Multivariate functional clustering (bayesian information criteria loss function) was done on 3 daily aggregate measurements: observable symptoms, unobservable symptoms, and viral shedding. 29 , 30 Clinical infection groups were determined by previous definitions of symptomatic (modified Jackson symptom score >5 within first 5 days of inoculation) and viral shedders (>2 days of shedding). 31 - 34 Participants who were positive in one criterion but not the other were excluded from further analysis in the clinical groupings. For both infection groupings, we defined symptom onset as the first day of a 2-day period in which the symptom score was at least 2 points. 32

Mean (SD) and median values of heart rate, skin temperature, and accelerometry were calculated every minute from baseline to 60 hours after inoculation. If several preinoculation days were present, then the baseline was defined as the mean value of each wearable metric at the same time of day. A total of 8 and 3 participants were removed from the H1N1 and RV analyses, respectively, owing to lack of sufficient data caused by nonwear, miswear, or device errors, which were detected following the methods of She et al. 7

Resting heart rate and temperature were defined by a 5-minute median accelerometer cutoff determined from the baseline day’s data. 35 For each 12-hour interval, several interbeat interval features were calculated using the 5-minute rolling mean with baseline subtraction: mean heart rate variability, median heart rate variability, number of successive N-N intervals that differ by more than 50 milliseconds, percentage of N-N intervals that differ by more than 50 milliseconds, SD of N-N intervals, and root mean square of successive R-R interval differences (eTable 1 in the Supplement ). 35 To account for circadian effects, model features were calculated as the difference between preinoculation and postinoculation summary metrics occurring at the same 1-hour clock time of day (eTable 1 and eFigure 1A in the Supplement ). 36

Models predicting infection further in time after inoculation included progressively more features (9 features added for each 12-hour block; eFigure 1B in the Supplement ). Performance relative to symptom onset was calculated by differencing the time after inoculation from the median symptom onset of each viral challenge. The resulting feature set consisted of 40 features calculated from 9 delta summary wearable metrics generated from five 12-hour intervals. Forward stepwise selection simultaneously tuned models and performed feature selection to prevent overfitting. 37

Bootstrapped binary or multiclass random forest classifiers were built using Python Scikit-learn and validated using leave-one-person-out cross-validation (trees = 1000). 12 , 37 , 38 This procedure was repeated for every 12-hour period feature set that was added to a model (eFigure 1B in the Supplement ).

Evaluation metrics of the models included accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC). 39 The primary metric of model success was accuracy. For multiclass models, the weighted mean value for each metric was recorded. For binary models, receiver operating characteristic curves were derived from the predicted class probabilities of an input sample, and the resulting AUC and 95% CI were reported.

The data were generated as part of 2 large challenge studies involving nasal lavage inoculation of human volunteers with either influenza (H1N1) or human RV. For the influenza prediction models, 31 participants were included in the analysis after data preprocessing (7 women and 24 men; aged 18-55 years; mean [SD] age, 34.7 [12.3] years; and 5 [16.1%] Asian, 21 [67.7%] White, 1 [3.2%] mixed/other category, and 4 [12.9%] did not fall into any of the ethnic groups listed, so they identified as “all other ethnic groups”). For the RV prediction models, 18 participants were included in the analysis after preprocessing (7 women and 11 men; aged 20-33 years; mean [SD] age, 21.7 [3.1] years; and 2 [11.1%] Asian, 2 [11.1%] Black, and 14 [77.8%] White, including 3 [16.7%] Hispanic or Latinx). The primary demographic difference between the 2 viral challenges was that the H1N1 group contained a wider age range and a higher mean age of participants (eTable 2A and B in the Supplement ).

Functional clustering indicated that there were 3 distinct classes of infection status that, on visual inspection, roughly equated to (1) AON, (2) mild, and (3) moderate ( Figure 2 ). Based on this clustering, we defined the data-driven “infected” group as the combined mild and moderate classes and the “noninfected” group as the AON class. All clinically driven labels of infected vs noninfected were perfectly replicated by the data-driven groupings for the RV challenge but not for the H1N1 challenge. 40

We developed 25 binary, random forest classification models to predict infection vs noninfection using features derived from wearables. Each model covered a different time period after inoculation or used a different definition of infected vs noninfected. For infected participants in the H1N1 challenge, the median symptom onset after inoculation was 48 hours (range, 9-96 hours). At 36 hours after inoculation, models predicting the data-driven groupings from the H1N1 challenge reached an accuracy of 89% (87% precision, 100% sensitivity, 63% specificity, 93% F1 score, and 0.84 [95% CI, 0.60-1.00] AUC). Because 7 participants were either symptomatic nonshedders (n = 6) or AON shedders (n = 1), the clinically driven H1N1 infection groupings had 7 fewer observations than the data-driven groupings. Models predicting the clinically driven groupings for H1N1 reached an accuracy of 79% (72% precision, 80% sensitivity, 79% specificity, 76% F1 score, and 0.68 [95% CI, 0.46-0.89] AUC) within 12 hours after inoculation and an accuracy of 92% (90% precision, 90% sensitivity, 93% specificity, 90% F1 score, and 0.85 [95% CI, 0.70-1.00] AUC) within 24 hours after inoculation. Regardless of whether the data-driven or clinically driven grouping method was used, we could assess whether or not a participant was infected with H1N1 between 24 and 36 hours before symptom onset ( Figure 3 A-C; Figure 4 A; and eFigure 2 and eTable 3 in the Supplement ).

The median symptom onset for RV was 36 hours after inoculation (range, 24-36 hours). The models predicting whether or not a participant was infected with RV achieved an early accuracy of 78% (78% precision, 78% sensitivity, 78% specificity, 78% F1 score, and 0.77 [95% CI, 0.54-0.99] AUC) at 12 hours after inoculation. This time point corresponded to 24 hours prior to symptom onset. Model performance peaked at the time of symptom onset, which was 36 hours after inoculation, with an accuracy of 88% at the same time as symptom onset (100% precision, 78% sensitivity, 100% specificity, 88% F1 score, and 0.96 [95% CI, 0.85-1.00] AUC) ( Figure 3 A-C; Figure 4 B; and eFigure 2 and eTable 3 in the Supplement ).

When both viral challenges were combined, models predicting the data-driven infection groupings reached an early accuracy of 78% (81% precision, 83% sensitivity, 68% specificity, 82% F1 score, and 0.66 [95% CI, 0.50-0.82] AUC) at 12 hours after inoculation. The models predicting clinically driven infection groupings reached an accuracy of 76% (76% precision, 68% sensitivity, 83% specificity, 72% F1 score, and 0.75 [95% CI, 0.60-0.90] AUC) at 24 hours after inoculation ( Figure 3 A-C; Figure 4 C; and eFigure 2 and eTable 3 in the Supplement ).

Infection severity was defined as (1) AON, (2) mild, or (3) moderate based on the data-driven functional clustering results ( Figure 2 ). We developed 66 binary and multiclass random forest models to predict class membership using features derived from the wearables for different time periods after inoculation. After automated feature selection, all 41 of the single viral challenge models included only 1 to 3 of the 9 to 45 possible features per model (eFigure 4A and B in the Supplement ). Interbeat interval features were retained in every model, and resting heart rate features were present in almost half (47.4% [9 of 19]) of the models (eTable 1 in the Supplement ).

At 12 hours after inoculation, the binary classification model predicting the future development of AON vs moderate H1N1 achieved 83% accuracy (78% precision, 88% sensitivity, 80% specificity, 82% F1 score, and0.88 [95% CI, 0.71-1.00] AUC). For RV, the model predicting the future development of AON vs moderate infection reached 92% accuracy (80% precision, 100% sensitivity, 89% specificity, 89% F1 score, and 1.00 [95% CI, 1.00-1.00] AUC). For both viruses combined, the model predicting the future development of AON vs moderate infection peaked at 84% accuracy (77% precision, 83% sensitivity, 84% specificity, 80% F1 score, and 0.78 [95% CI, 0.61-0.94] AUC) at 12 hours after inoculation ( Figure 4 A-C; Figure 5 A-C; and eFigure 3, eTable 4, and eTable 5 in the Supplement ).

Of the binary classification models for both viral challenge studies, we found that the AON vs moderate models achieved the highest accuracy and AUC toward predicting infection severity prior to symptom onset. This finding was expected given that these were the 2 most divergent classes of infection severity. At 12 hours after inoculation, the model predicting mild vs moderate H1N1 distinguished between the 2 symptomatic groups with 81% accuracy (75% precision, 75% sensitivity, 85% specificity, 75% F1 score, and 0.86 [95% CI, 0.69-1.00] AUC). By 24 hours after inoculation, this model achieved 90% accuracy (88% precision, 88% sensitivity, 92% specificity, 88% F1 score, and 0.88 [95% CI, 0.72-1.00] AUC). After excluding H1N1 challenge participants in the mild and moderate classes who did not have an infection per the clinically driven definition, the model predicting mild vs moderate H1N1 achieved 100% accuracy (100% precision, 100% sensitivity, 100% specificity, 100% F1 score, and 1.00 [95% CI, 1.00-1.00] AUC). By 24 hours after inoculation, the infection severity prediction model was able to distinguish between mild and moderate infection with an accuracy of 89% for RV (100% precision, 75% sensitivity, 100% specificity, 86% F1 score, and 0.95 [95% CI, 0.79-1.00] AUC). The model predicting mild vs moderate illness for both viruses combined distinguished between the 2 symptomatic groups with an accuracy of 86% (90% precision, 75% sensitivity, 94% specificity, 82% F1 score, and 0.91 [95% CI, 0.80-1.00] AUC). After excluding H1N1 challenge participants in the mild and moderate classes who did not have an infection per the clinically driven definition, the model predicting mild vs moderate illness for both viruses combined reached an accuracy of 94% (100% precision, 89% sensitivity, 100% specificity, 94% F1 score, and 0.94 [95% CI, 0.82-1.00] AUC) ( Figure 4 B; Figure 5 A and C; eFigure 3, eTable 4, and eTable 5 in the Supplement ). Receiving operator characteristic curves for both viral challenge studies ( Figure 5 A) demonstrated that the model predicting development of AON vs moderate illness and the model predicting development of mild vs moderate illness yielded higher discriminative ability than the model predicting AON vs mild illness.

The multiclass models were built to predict both infection status and infection severity per the data-driven definitions. We found that the highest performing multiclass models (predicting development of AON vs mild vs moderate illness) reached 77% accuracy for H1N1 (24 hours after inoculation; 76% precision, 77% sensitivity, 88% specificity, and 76% F1 score) and 82% accuracy for RV (36 hours after inoculation; 85% precision, 82% sensitivity, 88% specificity, and 82% F1 score) ( Figure 4 B; Figure 5 B and C; eFigure 2 and eTable 4 in the Supplement ).

The aim of this work was to evaluate a novel and scalable approach to identify whether or not a person will develop an infection after virus exposure and to predict eventual disease severity using noninvasive, wrist-worn wearables. The approach was tested using 2 viral challenge studies with influenza H1N1, human RV, or both viruses combined. This study shows that it is feasible to use wearable data to predict infection status and infection severity 12 to 36 hours before symptom onset, with most of our models reaching greater than 80% accuracy. Presymptomatic detection of respiratory viral infection and infection severity prediction may enable better medical resource allocation, early quarantine, and more effective prophylactic measures. Our results show that an accuracy plateau occurred in the 12- to 24-hour period after inoculation for 24 of 25 infection detection models (96.0%) and for 64 of 66 infection severity models (97.0%). This finding indicates that the most critical of the physiologic changes that occur in response to viral inoculation and that predict pending illness severity occurred within 12 to 24 hours after exposure.

Two factors associated with model accuracy are (1) knowledge of the exact time and dosage of inoculation and (2) the high-fidelity measurements of the research-grade wearable that enable intricate feature engineering, neither of which are possible in existing observational studies using consumer-grade devices. Because the outcome labeling is robust and accurate, there is a significant reduction in noise that would be present in an observational study. 41 The participants in both studies experienced clinically mild disease, so the physiologic changes in patients with severe disease outcomes would likely be even more extreme and therefore easier to detect. The timing of the models’ detection and severity prediction is particularly relevant to current work aimed at early detection of COVID-19 from smartwatches, as presymptomatic and asymptomatic spread are significant contributors to the SARS-CoV-2 pandemic. 9 , 20 - 24 , 26 , 42 - 45 The most important features for predicting infection severity were resting heart rate and mean heart rate variability. Thus, our model could be extensible to commercial wearables, which are used by 21% of US adults, for population-level detection of respiratory viral infections. 46 , 47

Several factors may be associated with the higher accuracy of the RV severity models compared with the H1N1 severity models, including the longer RV baseline period (4 days vs 1 day) and the morning vs afternoon inoculation time that may include circadian effects. This possibility was addressed in part by calculating the differences between baseline and postinoculation only from measurements taken at the same times of day. The same influenza challenge data were recently used to predict viral shedding timing, with an AUC of 0.758 using heart rate during sleep as a model feature. 7 Nighttime and early morning biometric measurements are potentially more useful than daytime measurements owing to their increased consistency, which should be explored further in future studies. 8 , 36

This study has some limitations. It focuses on 2 common respiratory viruses in a fairly small population. Expanding the data set to include larger and more diverse populations and other types of viruses will be necessary to demonstrate the broad applicability of these findings. Inclusion of negative control groups (ie, participants with no pathogen exposure and those with conditions that masquerade as infections [eg, asthma or allergies]) would further improve the work.

This study suggests that routine physiologic monitoring using common wearable devices may identify impending viral infection before symptoms develop. The ability to identify individuals during this critical early phase, when many may be spreading the virus without knowing it, and when therapies (if available) and public health interventions are most likely to be efficacious, may have a wide-ranging effect. In the midst of the global SARS-CoV-2 pandemic, the need for novel approaches like this has never been more apparent, and future work to validate these findings in individuals with other respiratory infections, such as COVID-19, may be critical given the highly variable and potentially severe or even fatal presentation of SARS-CoV-2 infection. The ability to detect infection early, predict how an infection will change over time, and determine when health changes occur that require clinical care may improve resource allocation and save lives.

Accepted for Publication: July 13, 2021.

Published: September 29, 2021. doi:10.1001/jamanetworkopen.2021.28534

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2021 Grzesiak E et al. JAMA Network Open .

Corresponding Author: Jessilyn Dunn, PhD, Biomedical Engineering Department, Duke University, 2424 Erwin Rd, Durham, NC 27705 ( [email protected] ).

Author Contributions: Ms Grzesiak and Dr Dunn had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: McClain, Woods, Tsalik, Nicholson, Burke, Chiu, Doraiswamy, Ginsburg, Dunn.

Acquisition, analysis, or interpretation of data: Grzesiak, Bent, McClain, Woods, Tsalik, Veldman, Burke, Gardener, Bergstrom, Turner, Chiu, Doraiswamy, Hero, Henao, Dunn.

Drafting of the manuscript: Grzesiak, Bent, Dunn.

Critical revision of the manuscript for important intellectual content: Grzesiak, McClain, Woods, Tsalik, Nicholson, Veldman, Burke, Gardener, Bergstrom, Turner, Chiu, Doraiswamy, Hero, Henao, Ginsburg, Dunn.

Statistical analysis: Grzesiak, Bent, Hero, Dunn.

Obtained funding: McClain, Woods, Chiu.

Administrative, technical, or material support: Veldman, Burke, Gardener, Turner, Henao, Dunn.

Supervision: McClain, Woods, Burke, Turner, Chiu, Ginsburg, Dunn.

Conflict of Interest Disclosures: Dr McClain reported receiving grants from Defense Advanced Research Projects Agency (DARPA) during the conduct of the study; in addition, Dr McClain had a patent for molecular signatures of acute respiratory infections pending. Dr Tsalik reported receiving personal fees from and being the cofounder of Predigen Inc outside the submitted work. Dr Burke reported receiving grants from DARPA during the conduct of the study and serving as a consultant for Predigen Inc outside the submitted work. Dr Turner reported receiving grants from Duke University during the conduct of the study. Dr Chiu reported receiving grants from DARPA during the conduct of the study and grants from Wellcome Trust, Medical Research Council, and the European Commission outside the submitted work. Dr Doraiswamy reported receiving grants from DARPA and nonfinancial support from Lumos Labs during the conduct of the study and receiving grants from Salix, Avanir, Avid, the National Institutes of Health, Cure Alzheimer’s Fund, Karen L. Wrenn Trust, Steve Aoki Foundation, the Office of Naval Research, and the Department of Defense and personal fees from Clearview, Verily, Vitakey, Transposon, Neuroglee, Brain Forum, and Apollo outside the submitted work; in addition, Dr Doraiswamy had a patent for infection detection using wearables pending, a patent for diagnosis of Alzheimer disease pending, a patent for treatment of Alzheimer disease pending, and a patent for infection detection through cognitive variability pending. Dr Ginsburg reported being the founder of Predigen Inc outside the submitted work. No other disclosures were reported.

Funding/Support: Dr Chiu is supported by the Biomedical Research Centre award to the Imperial College Healthcare National Health Service (NHS) Trust. Infrastructure support was provided by the National Institute for Health Research (NIHR) Imperial Biomedical Research Centre and the NIHR Imperial Clinical Research Facility. The Influenza A/California/04/09 challenge virus was donated by ITS Innovation, London, UK. The influenza viral challenge was supported by grant N66001-17-2-4014 from Prometheus Program of the DARPA and the rhinovirus study was supported by grant D17AP00005 from the Biochronicity Program of DARPA.

Role of the Funder/Sponsor: The funding sources 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 expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care.

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Recent UW Law Faculty Scholarship: Standing for Elections in State Courts; White-on-Black Crime: Revisiting the Convict Leasing Narrative; Function Versus Consequence in Restraint of Trade Analysis; and Judicial Biography of Australian Justice, Sir Gerard Brennan Book Review

Here is the latest faculty scholarship appearing in the  University of Wisconsin Law School Legal Studies Research Papers series  found on SSRN.

• Standing for Elections in State Courts Univ. Ill. L. Rev. (Forthcoming, 2024) by MIRIAM SEIFTER , UW Law School, and Adam B. Sopko, State Democracy Research Initiative

Election-related litigation is soaring. Litigants regularly challenge every aspect of an election cycle, from who can vote to how votes are cast and counted to the certification of results. Courts have thrown out many of these lawsuits on standing grounds. Given the requirements of traditional federal standing doctrine—including the requirement of individualized injury rather than generalized grievances—these dismissals are at least plausible in federal court.

But most election-related lawsuits today are filed in state court, where standing doctrine is and should be different. State courts are not bound by Article III of the U.S. Constitution, have constitutional commitments to democracy and open courts, and typically have more flexible justiciability doctrines. This Essay urges state courts to build on that foundation through a presumptively permissive approach to election standing. State courts fulfill their judicial role by redressing rather than avoiding threats to state-level democracy. And deciding election-related lawsuits on the merits serves important functions of stability, certainty, and finality, as well as confidence in election outcomes.

To be sure, the surge in election-related litigation is suboptimal, and some share of the lawsuits are meritless or brought in bad faith. Our argument is simply that standing doctrine is not the best tool for rejecting these lawsuits. The Essay describes other techniques that courts can use to deal with abusive or burdensome litigation without undermining the openness that is foundational to state judicial systems.

• White-on-Black Crime: Revisiting the Convict Leasing Narrative 2024 Wis. L. Rev. (Forthcoming, 2024) by ION MEYN , UW Law School

Between 1880 and 1915, the Southern criminal legal system enslaved and re-enslaved legally emancipated Black persons. Under the conventional account of this period, the law facilitated and legitimatized these practices, however odious and racially discriminatory. This view—one that critiques as it accepts the legality of the system—provides an explanation for a significant number of cases in which a Black person was convicted and sent to forced labor.

And yet, there is growing evidence that many convictions were not facilitated by law but rather the result of criminal conspiracies to traffic Black victims. County-level arrest data indicates “convictions” occurred in lockstep with the labor demands of businesses that contracted with local state actors. Numerous personal accounts from victims and their families indicate that arrests occurred in the absence of any criminal suspicion. This empirical data suggests many Black “convicts” were instead victims of human trafficking. Because completing these White-on-Black crimes required coordination among multiple parties, a criminal conspiracy was formed that implicated White participants in kidnapping, false imprisonment, perjury, peonage, reckless endangerment, and reckless homicide.

This Essay examines archival evidence that suggests the criminal trafficking of Black men was a common, if not widespread, practice between 1880 to 1915. Under this alternative view the term “convict leasing” is over-inclusive and mislabels these victims of human trafficking. Under the alternative view the historical Black crime rate is not only inflated but fabricated; conversely, the historical White crime rate omits a significant amount of criminal activity. This alternative view centers the criminal conduct of White beneficiaries, inviting a close accounting of their crimes and ill-gotten gains.

• Function Versus Consequence in Restraint of Trade Analysis 53 Univ. Baltimore L. Rev. 387 (2024) by PETER CARSTENSEN , UW Law School

The conventional interpretation of the Sherman Act’s prohibition of agreements in restraint of trade is, at best, ambiguous and uncertain. Conduct such as price fixing among competitors is per se illegal, except when it is not. Although the stated basis for the distinction is unclear, the function of the agreement embodying the restraint explains the apparently conflicting results. Regrettably, courts and commentators usually employ this conventional classification framework that focuses on desirability of the putative consequences of specific agreements. These categories have intuitive appeal but lack substantive coherence. In contrast, the functional approach focuses on the nature of the agreement in restraint of trade itself. The core of this approach is the distinction between naked and ancillary restraints. The policy goal is the preservation of the market process rather than a primary concern for the consequences of specific restraints on any measure of economic welfare. It is the thesis of this essay that the functional approach provides a better method to explain and predict the results of specific cases. It is more consistent with the language and meaning of the Sherman Act. It is also a better method for the analysis of restraints of trade. Because, like the conventional framework, the functional approach necessarily must employ presumptions, its normative merit is contingent on the scope and application of those presumptions.

• Judicial Biography of Australian Justice, Sir Gerard Brennan Book Review Univ. Wis. Legal Studies Research Paper No. 1805 (2024) by WILLIAM H. CLUNE , UW Law School

This is a book review in interview format with me interviewing the book’s author, Jeffrey Fitzgerald. The book is a judicial biography of the famous and influential Australian jurist, Sir Gerard Brennan. Largely in chronological sequence, the book also identifies cross-cutting themes such as the evolution of his jurisprudence over time.

My questions are designed to highlight issues that have parallels in American law, thus introducing the book to American readers. A second focus is the interaction of law and society. Law and society issues pervade the book because it is a longitudinal account of the judge’s encounters with important legal issues that arose in a changing Australian society, his influence on that society, and the corresponding evolution of his jurisprudence. It is law as both a dependent and independent variable, a classic law and society formulation. The judge’s decisions and jurisprudence operate as “constitutive law,” reflecting both the influence of society on law and its influence on society while remaining relatively autonomous from both. Former U.S. Supreme Court Justice Steven Breyer’s recent exposition of judicial “pragmatism” is congruent with the jurisprudence of Justice Brennan.

The paper has eight parts with questions and answers as sub-parts: (1) the book and Brennan’s career; (2) constitutional law, federalism, separation of powers, judicial review; (3) civil rights, aboriginal people’s rights, racial discrimination, and other rights; (4) impact on other areas of law (e.g., torts, contracts, criminal law); (5) Brennan’s principles of jurisprudence (6) the High Court, its divisions, and politics (7) personal, family, and professional life; (8) conclusion: mutual influence of law and society.

For the full text of these works and additional scholarship from UW Law faculty and staff, visit the  University of Wisconsin Law School Legal Studies Research Paper Series  on SSRN. A free email subscription is available at the top right of the page.