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Joshua S. Weitz is a Professor of Biology at the University of Maryland where he holds the Clark Leadership Chair in Data Analytics. Previously, he held the Tom and Marie Patton Chair at Georgia Tech where he founded the Graduate Program in Quantitative Biosciences. Weitz received his PhD in Physics from MIT in 2003 and did postdoctoral training in ecology and evolutionary biology at Princeton from 2003-2006. He directs an interdisciplinary group focusing on understanding how viruses transform the fate of cells, populations, and ecosystems and is the author of the forthcoming textbook ‘Quantitative Biosciences: Dynamics across Cells, Organisms, and Populations’ (Princeton University Press).  Weitz is a Fellow of the AAAS & the American Academy of Microbiology and is a Simons Foundation Investigator in Theoretical Physics of Living Systems.

Studies:

  • Viral impacts on population dynamics, community structure, and ecosystem functioning
  • Phage therapy for treatment of multi-drug resistant pathogens
  • Epidemic dynamics and the link between human behavior and disease transmission
  • Quantitative and computational biosciences in microbial systems

Selected Publications, Awards, and Support

Mallory is a postdoctoral associate working with Professor Joshua Weitz at the University of Maryland in the Department of Biology and Institute for Health Computing. She uses quantitative methods to study the interplay between human behavior and infectious diseases: how human activity can impact epidemic dynamics and how people respond to outbreaks. Her work includes mathematical modeling to understand how social divisions and risk perception shape disease dynamics; analyzing social media data to characterize vaccine decision-making; and causal inference to quantify how climate change is shifting dengue burden. She received her PhD in Biology from Stanford University in 2024.

Studies:

  • Incorporating novel data streams (e.g., Reddit posts, large-scale psychological studies, and Google search trends) into models of human behavior and infectious diseases
  • Applying machine learning to electronic health records to forecast vaccine uptake and infectious disease outbreak risk
  • Using causal inference to estimate the contribution of anthropogenic climate change to vector-borne disease burden

Publications and preprints:

Harris MJ, Cardenas KJ, Mordecai EA. Social divisions and risk perception drive divergent epidemics and large later waves. Evol Hum Sci. 2023 Feb.

Harris MJ, Murtfeldt R, Wang S, Mordecai EA, West JD. Perceived experts are prevalent and influential within an antivaccine community on Twitter. PNAS Nexus. 2024 Feb.

Harris MJ, Hay SI, and Drake JM. Evidence of critical slowing down prior to malaria resurgence in Kericho, Kenya. Biol Lett. 2020 Mar.

Harris MJ, Trok JT, Martel KS, Borbor Cordova MJ, Diffenbaugh NS, Munayco CV, Lescano AG, Mordecai EA. Extreme precipitation, exacerbated by anthropogenic climate change, drove Peru’s record-breaking 2023 dengue outbreak (in review).

Childs ML, Lyberger KP, Harris MJ, Burke M, Mordecai EA. Climate warming is expanding dengue burden in the Americas and Asia (in review).

Stephen Beckett is a computational ecologist and quantitative biologist whose research focuses on the dynamics and ecology of viruses. His research into infectious disease dynamics and marine microbial ecology is driven through the development and use of mathematical models, computational methods, and software development. Dr. Beckett received his PhD in Biological Sciences from the University of Exeter in 2015, before joining Georgia Tech as Postdoctoral Fellow, and later transitioning into a research scientist role in 2019. Dr. Beckett joined the University of Maryland in 2023 as an Associate Research Scientist in the Department of Biology and in 2024 became affiliated with the University of Maryland Institute for Health Computing. He is a member of the UMD Pandemic Readiness Initiative. He serves on the editorial boards for journals Mathematics in Medical and Life Sciences and PLOS Computational Biology.

Studies:

  • Advancing the integration of epidemiological modeling with human behavior; and how population heterogeneity impacts disease dynamics.
  • Characterizing the potential of wastewater surveillance and public understanding.
  • Developing reproducible open-source tools to improve outbreak analytics and communication.

Selected publications and preprints:

Beckett S.J., Demory D., Coenen A.R., Casey J.R., Dugenne M., Follett C.L., Connell P., Carlson M.C.G., Hu S.K., Wilson S.T., Muratore D., Rodriguez-Gonzalez R.A., Peng S., Becker K.W., Mende D.R., Armbrust E.V., Caron D.A., Lindell D., White A.E., Ribalet F., Weitz J.S. Disentangling top-down drivers of mortality underlying diel population dynamics of Prochlorococcus in the North Pacific Subtropical Gyre. 2024. Nature Communications 15: 2105.

Sinclair A.H., Taylor M.K., Brandel-Tanis F., Davidson A., Chande A.T., Rishishwar L., Andris C., Adcock R. A., Weitz J.S., Samanez-Larkin G.R., Beckett S.J. Communicating COVID-19 exposure risk with an interactive website counteracts risk misestimation. 2023. PLoS ONE 18(10): e0290708.

Beckett S.J., Brandel-Tanis F.A., Nguyen Q., Chande A.T., Rishishwar L., Andris C., Weitz J.S. localcovid19now: processing and mapping COVID-19 case data at subnational scales. 2023. Journal of Open Source Software 8(81): 4898.

Sinclair A.H., Taylor M.K, Weitz J.S., Beckett S.J., Samanez-Larkin G.R. Reasons for Receiving or Not Receiving Bivalent COVID-19 Booster Vaccinations Among Adults — United States, November 1–December 10, 2022. 2023. MMWR: Morbidity & Mortality Weekly Report 72: 73-75.

Harris J.D., Gallmeier E., Dushoff J., Beckett S.J., Weitz J.S. Infections are not alike: the effects of covariation between individual susceptibility and transmissibility on epidemic dynamics. (in review)

Sinclair A.H., Taylor M.K., Beckett S.J., Chande A.T., Weitz J.S., Samanez-Larkin G.R. Personalized Feedback about Immunity Corrects Risk Misestimation and Motivates Vaccination. (in review).