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Principal Investigator: Shweta Bansal
Organization: GEORGETOWN UNIVERSITY
Fiscal Year: 2024
Award: $426,660
Funding agency: National Institute of General Medical Sciences
Project Summary
My research program addresses fundamental questions about population-scale infectious disease dynamics and
brings to light the role of social factors in these dynamics. The impact that carefully formulated and extensively
validated models can have in predicting spatio-temporal disease dynamics and providing a rational assessment
of alternative intervention strategies is understood by policymakers and clinical practitioners alike. However, as
the COVID-19 pandemic has demonstrated, continuing to neglect socio-behavioral processes presents a critical
barrier to future model development: behavioral surveillance gaps jeopardize our ability to predict pathogen
emergence; a poor understanding of the feedback loops between behavior and disease hampers the forecasting of
disease dynamics; and a limited appreciation of the nonlinear impacts of anti-mitigation behavior reduces hope of
eliminating diseases before they take hold. At the same time, public health inequities fueled by income inequality
and systemic racism pose a dire and urgent threat.
To address these pressing gaps, my research team uses a multi-scale socio-behavioral disease modeling approach
to integrate interacting elements of health, physical and socially-constructed environments, and community and
individual behavior to predict social and spatial heterogeneities in respiratory disease burden. We develop
generative and inferential models for a systematic understanding of the constant, compounding socio-behavioral
processes that give rise to disease heterogeneities across individuals, communities, and systems. We also leverage
opportunistic datasets to characterize behavior and disease across geography and time to resolve questions that
have eluded explanation without socio-behavioral data.
Our future work will advance the theory of respiratory disease dynamics with a focus on two case studies, SARS-
CoV-2 and influenza, in the United States. The work will make significant contributions to our understanding
of respiratory virus epidemiology, seasonality, spatial epidemiology, health inequities, and public health policy
and will spur innovation for integrating large data streams into infectious disease models. These advances will
generalize to improve our understanding of other respiratory, partially immunizing viruses that cause epidemics
or pandemics. Our focus on the US public health system also serves as a crucial case study to characterize the
consequences of intense variation across social, environmental, economic, and demographic dimensions and inform
the impact of heterogeneity on data collection, model complexity, disease outcomes, and management strategies.
Our work has broad implications at a time when heterogeneities will be amplified by future perturbations, including
emerging diseases, climate change, and sociopolitical unrest. Understanding the mechanisms underlying the causes
of epidemiological heterogeneity across space, time, and the landscape of vulnerability will help inform resource
allocation, design outbreak intervention, optimize disease surveillance, strengthen health systems, and improve
access to healthcare.
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