# Meyers Lab **Entity class:** Research laboratory **Institutional home:** [[wiki/Department of Integrative Biology at the University of Texas at Austin|Department of Integrative Biology]], [[wiki/College of Natural Sciences at the University of Texas at Austin|College of Natural Sciences]], [[wiki/University of Texas at Austin|University of Texas at Austin]] **Principal investigator:** [[wiki/Lauren Ancel Meyers|Lauren Ancel Meyers]] **Public account:** [@meyerslab](https://x.com/meyerslab) The **Meyers Lab** develops computational methods for anticipating and combating infectious-disease outbreaks. Its work joins [[wiki/Network Epidemiology|network epidemiology]], mathematical modeling, machine learning, outbreak detection, forecasting, intervention analysis, and decision support across influenza, Ebola, Zika, COVID-19, and other emerging viral threats. The laboratory is the durable research unit behind several successive public-health programs. In 2018, the group evaluated more than 600 candidate influenza data streams with [[wiki/Texas Advanced Computing Center|TACC]] resources and supplied the resulting forecasting methods to the [[wiki/Defense Threat Reduction Agency|Defense Threat Reduction Agency]]’s [[wiki/Biosurveillance Ecosystem|Biosurveillance Ecosystem]]. In March 2020, Meyers established the [[wiki/UT COVID-19 Modeling Consortium|UT COVID-19 Modeling Consortium]], which joined the lab with TACC, [[wiki/Dell Medical School|Dell Medical School]], health systems, and public-health partners. The later [[wiki/Center for Pandemic Decision Science|Center for Pandemic Decision Science]] and [[wiki/epiENGAGE Center for Forecasting and Outbreak Analytics|epiENGAGE]] continue the forecasting-and-response lineage. ## Influenza, vaccination, and PNAS The lab’s current work includes influenza forecasting, vaccine-impact estimation, antiviral strategy, and outbreak-forecast ensembles. A 2025 [[wiki/Proceedings of the National Academy of Sciences|PNAS]] paper led by Kai Bi and coauthored by Meyers estimated that vaccination averted **69,886 influenza-associated hospitalizations** during the 2022–2023 U.S. season. The reported quantity is a model-based counterfactual estimate with uncertainty, rather than a direct count of individually identified prevented hospitalizations. The study also modeled higher-coverage scenarios; the lab highlighted a [[wiki/PNAS|PNAS]] podcast discussion of the findings in January 2026. ## COVID-19 and the Austin operational chain During COVID-19, the lab produced projections, staged-alert designs, risk maps, hospital-capacity forecasts, testing and school-opening analyses, treatment and vaccination scenarios, and retrospective studies of contact-tracing effectiveness. [[wiki/Dell Medical School|Dell Medical School]] supplied clinical and population-health participation; Dell clinicians helped specify realistic vaccination, treatment, hospital-care, and capacity conditions. [[wiki/Austin Public Health|Austin Public Health]] supplied public-health authority and local contact-tracing data for specific studies. [[wiki/City of Austin|City of Austin]] officials used the broader modeling program for local planning, while TACC supplied high-performance computation, data infrastructure, visualization, and dashboards. ## Relationships - **led by:** [[wiki/Lauren Ancel Meyers|Lauren Ancel Meyers]]. - **housed in:** [[wiki/Department of Integrative Biology at the University of Texas at Austin|Department of Integrative Biology]] within [[wiki/College of Natural Sciences at the University of Texas at Austin|Texas Science]] at [[wiki/University of Texas at Austin|UT Austin]]. - **clinical and population-health partner:** [[wiki/Dell Medical School|Dell Medical School]]. - **computational partner:** [[wiki/Texas Advanced Computing Center|TACC]]. - **local public-health partners:** [[wiki/Austin Public Health|Austin Public Health]] and [[wiki/City of Austin|City of Austin]]. - **program lineage:** [[wiki/Surety BioEvent App|Surety BioEvent App]] → [[wiki/Biosurveillance Ecosystem|BSVE]] transfer → [[wiki/UT COVID-19 Modeling Consortium|UT COVID-19 Modeling Consortium]] → [[wiki/Center for Pandemic Decision Science|Center for Pandemic Decision Science]] and [[wiki/epiENGAGE Center for Forecasting and Outbreak Analytics|epiENGAGE]]. - **mirror-ledger analysis:** [[research/Harms Incurred While Bringing Preventive Systems Online|Harms Incurred While Bringing Preventive Systems Online]] uses the lab's model-based counterfactual method as the template for counting inflicted harm alongside averted harm in [[wiki/The Second Error Function|The Second Error Function]]. ## Sources / Provenance - [Lauren Ancel Meyers — Integrative Biology](https://integrativebio.utexas.edu/directory/lauren-ancel-meyers) (current profile accessed 2026-09-23). - [Lauren Ancel Meyers — Dell Medical School](https://dellmed.utexas.edu/directory/lauren-ancel-meyers) (current profile accessed 2026-09-23). - [Modeling a Global Pandemic — Oden Institute](https://oden.utexas.edu/news-and-events/news/Modeling-Global-Pandemic-Profile-Lauren-Ancel-Meyers/) (2020). - [Flu Season Forecasts Could Be More Accurate with Access to Health Care Companies’ Data — UT Austin](https://news.utexas.edu/2018/09/19/this-data-source-could-enable-better-flu-forecasts/) (2018-09-19). - [Estimated impact of 2022–2023 influenza vaccines on annual hospital burden in the United States — PNAS](https://pmc.ncbi.nlm.nih.gov/articles/PMC12646225/) (2025). - [Meyers Lab post on the PNAS podcast](https://x.com/meyerslab/status/2016498670976340424) (2026-01-28).