Project Details
National Science Foundation
In this project, we aim to utilize the knowledge and insights we gained from COVID-19 to develop new computational methods and mathematical foundations that support the next generation of models for computational epidemiology. We aim to significantly advance mathematical and machine learning methods for dealing with real-world epidemics, introduce novel approaches for capturing real-world human behavior and integrating behavioral responses into epidemiological models, and leverage those models to reduce the global burden (both in mortality and economic impact) of infectious diseases by enabling public health officials to respond more rapidly and effectively to infectious disease outbreaks.
Capturing human behavior is a complex and challenging process, as new and unexpected behavioral patterns emerge constantly. These challenges are 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 policies, and incentives. It is thus essential to develop epidemiological models that can make predictions based on real-world behaviors and capture feedback loops between behaviors, epidemics, and policies. We propose to address the challenges of behavior-based epidemiological modeling by developing a unified modeling framework. First, digital sensors such as mobile phones and search engine logs provide unique opportunities to capture human behavior, but data from these sensors are often highly imperfect. We propose novel methods to denoise and derive meaning from multimodal, real-world sensors, so that we can harness them to capture complex human behaviors in real time. Second, to bridge epidemiological models and real-world behaviors, we plan to couple disease dynamics with models of human behavior that capture how individuals choose behaviors based on perceived costs and benefits. Such agent-based models are computationally complex and require new methods to calibrate and validate on real data so that we can realistically forecast how epidemics will unfold. Finally, to evaluate the complex effects of public health policies on behavior and epidemic outcomes, we develop new scenario modeling tools and causal inference methods to estimate policy effects in the presence of confounders and interference. Our proposed research, which includes new methods for incorporating novel data sources, extending epidemiological models, and evaluating policy interventions, will greatly improve our ability to incorporate real-world human behavior into epidemiological models and inform more effective public health policies.
We expanded our existing behavior-epidemic modeling framework by incorporating in the decision-making process the role of individuals’ immunity profiles, the impact of information delay and risk misperception factors. We used empirical mobility data as a proxy to validate our behavioral models. Moreover, we leveraged the mean-field adaptive behavior model to network-based behavior model where each network’s node adapts their behavior based on local risk conditions. Finally, we leveraged our behavior-epidemic models to incorporate centralized and decentralized interventions.
Espinoza B, Saad-Roy C, Grenfell B, Levin S, Marathe M. Adaptive human behaviour modulates the impact of immune life history and vaccination on long-term epidemic dynamics. Proceedings B. 2024 Oct 30;291(2033):20241772.
Nguyen M, Freedman A, Cheung M, Saad-Roy C, Espinoza B, Grenfell B, Levin S. The complex interplay between risk tolerance and the spread of infectious diseases. Journal of the Royal Society Interface. 2025 Apr 23;22(225):20240486.
Chen J, Espinoza B, Chou J, Gumel A, Levin S, Marathe M. A simple model of coupled individual behavior and its impact on epidemic dynamics. Mathematical Biosciences. 2025 Feb 1;380:109345.
Espinoza B, Chen J, Orr M, Saad-Roy C, Levin S, Marathe M. The impact of risk compensation adaptive behavior on the final epidemic size. Mathematical Biosciences. 2025 Feb 1;380:109370.
Chou J, Chen J, Marathe M. Estimate Causal Effects of Entangled Treatment on Graphs using Disentangled Instrumental Variables. Proceedings of the IEEE International Conference on Big Data. 2024 Dec 15:1260-1267.
Chou J, Chen J, Marathe M. State-of-the-art and Challenges in Causal Inference on Graphs: Confounders and Interferences. Proceedings of the IEEE 6th International Conference on Cognitive Machine Intelligence. 2024 Oct 28:1-13.