Collaborative Research: IHBEM: Data-driven multimodal methods for behavior-based epidemiological modeling

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In this project, challenges of behavior-based epidemiological modeling are addressed by developing a unified modeling framework that incorporates new methods for incorporating novel data sources, extended epidemiological models, and evaluations of policy interventions. Capturing human behavior is complex and challenging, as new and unexpected behavioral patterns emerge constantly. This is especially evident during epidemics, which are shaped by a wide variety of behaviors and, in turn, accelerate the speed of behavioral changes. For example, the trajectory of the COVID-19 pandemic was greatly shaped by behaviors such as social distancing, mask-wearing, and vaccination, and these behaviors also emerged and changed dramatically over the course of the pandemic in response to changing disease risks, social norms, government decisions, and incentives. The aim of this project is to develop epidemiological models that can make predictions based on real-world behaviors and capture feedback loops between behaviors, epidemics, and government decisions, thus enabling more effective public health decisions.The aims of this project are accomplished by improving mathematical and machine learning methods for dealing with real-world epidemics and introducing novel approaches to the capture of real-world human behavior and integration of behavioral responses into epidemiological models. First, novel methods are proposed to denoise and derive meaning from multimodal, real-world sensors, such as mobile phones and search engine logs, the data from which is often highly imperfect but which provide unique opportunities to capture human behavior. This allows the capture of complex human behaviors in real time. Second, to bridge epidemiological models and real-world behaviors, agent-based models of disease dynamics are coupled with models of human behavior that capture how individuals choose behaviors based on perceived costs and benefits. Such models are computationally complex and require new methods to calibrate and validate on real data to enable realistic forecasting of epidemics. Finally, to evaluate the complex effects of public health decisions on behavior and epidemic outcomes, new scenario modeling tools and causal inference methods are developed to estimate effects of such decisions in the presence of confounders and interference.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.