COVID 19: RAPID: Informed Decision Making for Pandemic Management

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The primary goal of the proposal is to develop a set of tools to inform and advise decision makers to control large-scale global pandemics. Specific research problems to be addressed include (1) Making reliable predictions for the state of the pandemic in the next few days/weeks/months. (2) Estimating the effect of governmental measures in the dynamics of the pandemic. (3) Designing protocols for effective control of a pandemic to prevent overloading the healthcare system. Efficient management of large-scale epidemics that are likely to overwhelm the health care system on a national scale, which would surely lead to a significant increase in the loss of human lives, including deaths not related to the specific pathogen causing the epidemic. This project will make contributions to the fundamental research on developing actionable models for epidemic estimation and control. Specifically, the focus is on models that are amenable to (i) the estimation of model parameters, (ii) the estimation of the state of the epidemic, and (iii) computing optimal intervention policies; all three with high degrees of confidence based on the relatively small datasets that are available while an epidemic is evolving. The research includes data-driven techniques to determine the impact of non-pharmaceutical social measures for epidemic management. A key challenge in this area is to reliably infer causal relationships between social measures and their effect in the epidemic dynamics. Methods will be developed to determine intervention policies that minimize the loss of human life and economic impact, without overwhelming the healthcare system. The existence of delays from actuation (e.g., through social measures) to effects (e.g., in changes of infection rates) and the large uncertainty in the actuation mechanisms pose significant technical challenges that must be addressed through robust decision policies. This project will also provide training opportunities for students in the areas of system modeling, identification, and control.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.