COVID-19: RAPID: Networked Compartmental Modeling and Analysis for Spread of COVID-19

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The ongoing COVID-19 pandemic is caused by a novel coronavirus, which was only identified in December 2019. Due to the novelty of the virus and the speed at which it is currently sweeping the world, not only is there very little known about the virus but there is also very little data. Despite the lack of data, there are many important questions that need to be answered. Is social distancing working effectively in `flattening the curve’? How much more effective would mandated shelter-in-place be in containing the spread? Is it worth the social cost? What is the effect of 10% of the population ignoring these protocols? What is the marginal benefit of enforcing quarantines versus implementation cost? Today the important questions seem to be related to mitigation as the biggest concern is the immediate matter at hand: the impending peak of hospitalizations due to COVID-19. However, it is also necessary to be looking ahead to a potential resurgence of this virus with a new set of questions. What will be the effect of asynchronously `opening up' different parts of the country as people are still recovering from COVID-19? How will we know we are not lifting restrictions pre-maturely? Precise answers to these questions are needed in order to make informed policy decisions, and this requires a deep understanding and accurate models of COVID-19 which are simply not available today. Unfortunately, there is no time to learn about this virus before needing to act to mitigate the tremendous damage that is already being incurred socially, economically, and even in terms of lost lives. Instead, new data must be rapidly incorporated into models and these questions must be re-visited on a constant basis to be able to quickly provide at least a reasonable understanding of the important questions above.This project addresses the rapidly evolving modeling problem for COVID-19. Taking a systems point of view, this project seeks to investigate the effects of various overlooked artifacts of COVID-19 in the leading models used to inform policy decisions today. The numerical methods and mathematical models can provide significant complementary support to the epidemiologists worldwide on understanding how the virus spreads. The outcomes of this project will be novel stochastic and deterministic networked meta-population models as opposed to the commonly seen lumped population models. The models developed will expand simple Susceptible-Infected-Removed (SIR) models to capture a number of different properties specific to COVID-19 by adding more compartments. These models will provide a more rigorous analysis of the network effects of the ongoing pandemic which may prove especially useful as different parts of the country, or even the world, are imposing/lifting various levels of mobility restrictions at different times.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.