NSF Award
2032465
As COVID-19 spreads throughout communities, government officials face a series of challenging decisions, each fraught with an array of difficult tradeoffs. Current efforts to stop the spread of the novel coronavirus by closing non-essential businesses have resulted in the loss of millions of jobs. In addition to the economic costs, lockdown measures have had other unintended consequences that are difficult to measure. Unquestionably, the suffering caused by COVID-19 will go far beyond clinical effects alone. However, if no measures are taken, the virus may spread rapidly, overwhelming local healthcare resources and causing substantial human loss. This raises the urgent question: How can leaders make public policy decisions regarding the COVID-19 pandemic in a scientific way that is locally appropriate and properly accounts for both near-term and longer-term costs of policy interventions? This project combines rigorous mathematical modeling, innovative approaches to data collection, and input from policymakers, to develop a decision aid framework that weighs the costs and benefits of various policy interventions at a local level and tailors interventions to the locale considering the effects of specific indicators such as urbanization, economic distress, and availability of regional healthcare. Additionally, graduate students will be trained in the context of this research. <br/><br/>To develop a rigorous understanding of the tradeoffs involved in policy interventions geared at mitigating COVID-19, two complementary approaches will be applied. First, the benefits of policy interventions will be estimated using a new dynamical game-theoretic mathematical COVID-19 epidemic model which accounts for the interactions between social behavior, policy interventions, and disease contagion. Second, the costs of policy interventions will be estimated via geospatial data aggregation and analysis which identifies local vulnerability to unintended consequences of policy interventions and assesses the disparities of these impacts across racial and socioeconomic divides. This project will advance knowledge in two complementary directions. First, the project?s data collection and aggregation activities will create datasets which would be otherwise impossible to recreate if this historic opportunity were missed; these datasets will facilitate our understanding of the connection between the spread of COVID-19 and the emergence of social behavior, identify the long-term costs of infection, and are anticipated to be applicable to future pandemics as well. Second, the project?s game-theoretic epidemiological model captures the feedback interconnection between two dynamical systems (human behavior and disease spread, respectively). These systems and their abstractions have been well-studied in isolation, but their interconnection is not well understood. Mathematical models will be reviewed and revised based on feedback from policymakers in order to best tailor interventions to the locale considering the effects of specific indicators such as urbanization, economic distress, and availability of regional healthcare. This interdisciplinary project will significantly advance understanding of these inherent societal feedback effects in a data driven, real world context. This RAPID award is made by the Ecology and Evolution of Infectious Disease Program in the Division of Environmental Biology, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.<br/><br/>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.
