Managing epidemics by managing mobility
This NSF grant will study the coupling between personal mobility and the spread of infectious disease. Recent experiences with COVID-19 have highlighted the importance of directly modeling transportation flows within epidemiological models and understanding the impacts of complex, local-scale travel patterns and their network effects. In particular, the project will help address questions of the following nature: i) Which communities are most likely to accelerate disease propagation throughout the network? ii) Which recurrent travel patterns are most likely to become disease vectors? iii) What combination of social-distancing and travel restriction measures are needed to safely reactivate a region? iv) Where should preventive screening be administered (when resources are limited) to minimize contagion throughout the network? The project aims to engage with public health experts and deliver implementations of the models in publicly accessible forms to support policy maker engagement.
The technical contributions of the project include : i) development of enhanced spatial meta-population susceptible-exposed-infectious-recovered (SEIR) models that explicitly capture complex high fidelity travel patterns and their nuanced interactions with the dynamics of the disease; ii) extensions that can go beyond the traditional notion of a meta-population to explicitly model infections that occur en-route (e.g. while using a transit system); iii) network analysis tools for strategic planning that can explain the implications on contagion of different mobility patterns and mitigation strategies that alter these patterns; iv) optimal control strategies for managing outbreaks and planning for the reopening phase using advanced analytical and numerical methods; and v) data-driven Bayesian auto-calibration techniques that utilize a combination of real-time case data, historic data, and local expert knowledge. The team will build an open source simulation tool that allows the outputs of this project to be accessible, reproducible and extensible by others.
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.
