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Bryan Lewis

  • Research Associate Professor
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Bio

Bio

Bryan Lewis is a research associate professor in the Network Systems Science and Advanced Computing division. His research has focused on understanding the transmission dynamics of infectious diseases within specific populations through both analysis and simulation. Lewis is a computational epidemiologist with more than 15 years of experience in crafting, analyzing, and interpreting the results of models in the context of real public health problems.

As a computational epidemiologist, Lewis acts as a liaison between the computer scientists and mathematicians designing and building simulation software and decision makers who want answers to pressing public policy questions. For more than a decade, Lewis has been heavily involved in a series of projects forecasting the spread of infectious disease as well as evaluating the response to them in support of the federal government. These projects have tackled diseases from ebola to pandemic influenza and melioidosis to cholera.

  • Public health and epidemiology, epidemiologic modeling, social network construction, and graph measures and dynamic networks

  • Virginia Tech, Genetics, Bioinformatics, and Computational Biology, Ph.D., 2011
    University of California - Berkeley, Infectious Diseases, M.P.H., 2001
    Carnegie Mellon University, Computational Biology, B.S., 1997

  • California Department of Health Services, Tuberculosis Control Branch, Surveillance and Epidemiology Section, 2001-2003

Selected Publications
Network Systems Science and Advanced Computing
Borchering R; Mullany L; Howerton E; Chinazzi M; Smith C; Qin M; Reich N; Contamin L; Levander J; Kerr J; Espino J; Hochheiser H; Lovett K; Kinsey M; Tallaksen K; Wilson S; Shin L; Lemaitre J; Hulse J; Kaminsky J; Lee E; Davis J; Mu K; Xiong X; Piontti A; Vespignani A; Srivastava A; Porebski P; Venkatramanan S; Adiga Ani; Lewis B; Klahn B; Outten J; Hurt B; Chen J; Mortveit H; Marathe M; Hoops S; Bhattacharya P; Machi D; Chen S; Paul R; Janies D; Thill J; Galanti M; Yamana T; Pei S; Shaman J; Espana G; Cavany S; Moore S; Perkins A; Healy J; Slayton R; Johansson M; Biggerstaff M; Shea K; Truelove S; Runge M; Viboud C; Lessler J . Lancet Regional Health Americas. 2023; 17
Network Systems Science and Advanced Computing
Wang L; Adiga Ani; Chen J; Lewis B; Sadilek A; Venkatramanan S; Marathe M . Knowledge-guided Machine Learning: Accelerating Discovery using Scientific Knowledge and Data. CRC Press. 2022;
Network Systems Science and Advanced Computing
Venkatramanan S; Sadilek A; Fadikar A; Barrett C; Biggerstaff M; Chen J; Dotiwalla X; Eastham P; Gipson B; Higdon D; Kucuktunc O; Lieber A; Lewis B; Reynolds Z; Vullikanti A; Wang L; Marathe M . Nature Communications. 2021; 12
Network Systems Science and Advanced Computing
Venkatramanan S; Chen J; Fadikar A; Gupta S; Higdon D; Lewis B; Marathe M; Mortveit H; Vullikanti A . PLoS computational biology. Public Library of Science. 2019; 15(9):e1007111
Network Systems Science and Advanced Computing
Venkatramanan S; Lewis B; Chen J; Higdon D; Vullikanti A; Marathe M . Epidemics. Elsevier. 2018; 22:43-49
Network Systems Science and Advanced Computing
Chakraborty P; Lewis B; Eubank S; Brownstein J; Marathe M; Ramakrishnan N . PLoS computational biology. Public Library of Science San Francisco, CA USA. 2018; 14(10):e1005964
Network Systems Science and Advanced Computing
Alexander K; Sanderson C; Marathe M; Lewis B; Rivers C; Shaman J; Drake J; Lofgren E; Dato V; Eisenberg M . PLoS Neglected Tropical Diseases. Public Library of Science. 2015; 9(6):e0003652
Network Systems Science and Advanced Computing
Lewis B; Swarup S; Bisset K; Eubank S; Marathe M; Barrett C . Journal of public health management and practice: JPHMP. NIH Public Access. 2013; 19(Suppl 2(0 2)):S42-48
Network Systems Science and Advanced Computing
Jimenez J; Lewis B; Eubank S . Complex Systems: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer, Cham. 2013; 126:165-178
Network Systems Science and Advanced Computing
Halloran, M Elizabeth; Ferguson, Neil M; Eubank S; Longini, Ira M; Cummings D; Lewis B; Xu, Shufu; Fraser, Christophe; Vullikanti A; Germann, Timothy C . Proceedings of the National Academy of Sciences. National Acad Sciences. 2008; 105(12):4639-4644
In the News

A research team from the University of Virginia’s Biocomplexity Institute was recognized with the UVA Provost’s Office Award for Collaborative Excellence in Public Service. As representatives of the Biocomplexity Institute’s COVID-19 Response Team, Jiangzhuo Chen, Bryan Lewis, and Srini Venkatramanan were recognized for their service to the University, the Commonwealth of Virginia and federal authorities during the pandemic, and which continues today.

COVID-19

The question on everyone's mind is whether the U.S. will have an easy, COVID-free spring, or if we're in for a resurgence in cases as the BA.2 variant of Omicron becomes dominant. Bryan Lewis, MPH, PhD, of the University of Virginia's Biocomplexity Institute, uses sequencing data and other information from COVID-19's past trajectory to model and project various scenarios for case rates, variants, and more.

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COVID-19

While sequencing efforts are better than before, experts say there's a long way to go. The only certain thing about the future of SARS-CoV-2 variants is that nothing is certain -- but researchers are doing their best to keep an eye out for the next troublesome variant, even in the face of numerous challenges.

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Epidemic Response

Researchers from UVA’s Biocomplexity Institute and School of Engineering and Applied Science, working with a team of multi-disciplinary scientists from around the world, have spent the last two years developing highly advanced computational models designed to inform policy makers, save lives and prepare for future global epidemics.

COVID-19

Bryan Lewis, a research associate professor at the Biocomplexity Institute and Initiative at the University of Virginia, has used models to predict future COVID-19 cases. From recent model runs, Lewis believes the omicron variant will become the dominant strain in the state before the end of the year.

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COVID-19

For much of the pandemic, the Biocomplexity Institute has been modeling the likely trajectory of COVID-19 in Virginia using mobility data, case rates, vaccination numbers and a slew of other statistics that can predict how, where and how fast the virus will continue to spread. With many Virginians — and public health officials — already preparing for holiday gatherings, understanding potential risks could be crucial for decision-making.

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COVID-19

This article discusses how the University of Virginia’s Biocomplexity Institute is helping local health officials select mobile vaccine sites. Our data dashboards and reports include information on mobility, drawn from anonymized cell phone data collected by a company called SafeGraph, showing where and when Virginians were traveling to help understand the impact of safety restrictions.

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COVID-19

Rather than a shut-down, what if we knew which places to close during a pandemic to curtail the spread of a virus and lessen the economic impact to a locality? A model showing how this can be done, created by researchers at the University of Virginia Biocomplexity Institute and Stanford and Northwestern universities, has won a Best Paper Award in the Applied Research track at the recently concluded ACM KDD conference.

COVID-19

When the coronavirus reached Virginia, public health officials worried there would be so many patients, they would need to start building field hospitals right away. But a team of University of Virginia scientists, part of the Biocomplexity Institute, told the state to wait. The governor’s stay-at-home order, quarantines and social distancing that began in March 2020 could slow the disease’s spread.

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COVID-19

Bryan Lewis, a computational epidemiologist at UVA’s Biocomplexity Institute, said the plummeting numbers were a surprise to scientists analyzing the data. “There are about 30 or 40-odd academic groups participating in the CDC’s forecasting hub, and I don’t think any of them predicted the cliff we fell off there,” Lewis said, adding that cases dropped dramatically not just in Virginia, but across the country.

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COVID-19

Researchers across Grounds joined the effort to defeat COVID-19—even as the world plunged abruptly into a new reality of shutdowns and social distancing—bringing their expertise together to understand the virus and prevent and treat infection.

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COVID-19

People are taking social distancing seriously – even when it comes to their health care decisions. The Centers for Disease Control and Prevention (CDC) reported Friday that influenza-like illness (ILI) activity decreased by 14 percent for the week ending March 28 compared to the prior week. 

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COVID-19

A third spike in influenza-like illness (ILI) activity in the United States now puts the 2019-20 flu season on track to be perhaps the longest in at least the last 20 years of record-keeping by the Centers for Disease Control and Prevention (CDC). 

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Lewis