An Optimization Framework to Study the Balance Between Expected Fatalities Due to COVID-19 and the Reopening of U.S. Communities

Abstract

During the COVID-19 pandemic, communities faced two conflicting objectives: 1) minimizing infections among vulnerable populations with higher risk for severe illness and 2) enabling reopening to revive American livelihoods. The U.S. pandemic strategy myopically considered one objective at a time, with lockdowns that addressed the former, but was detrimental to the latter, and phased reopening that pursued the latter, but lost control over the former. How could we prioritize interventions to simultaneously minimize cases of severe illness and fatalities while reopening? A team of researchers anchored by the Center on Stochastic Modeling, Optimization, & Statistics (COSMOS), The University of Texas at Arlington, has formulated a computationally efficient optimization framework, referred to as COSMOS COVID-19 Linear Programming (CC19LP), to study the delicate balance between the expected fatality rate due to cases of severe illness and the level of normalcy in the community. The key to the CC19LP framework is a focus on “key contacts” that separate individuals at higher risk from the rest of the population. CC19LP minimizes expected fatalities by optimizing the use of available interventions, namely, COVID-19 testing, personal protective equipment (PPE), COVID-19 vaccines, and social precautions, such as distancing, handwashing, and face coverings. A C3.ai award-winning online CC19LP tool is accessible from the COSMOS COVID-19 project site ( <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://cosmos.uta.edu/projects/covid-19/</uri> ) and has been tested for all 3142 U.S. county areas. Results are demonstrated for several metropolitan counties with a deeper investigation for Miami-Dade County in Florida. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In this article, a computationally fast optimization framework is presented to study the delicate balance between reopening U.S. communities and controlling severe cases of COVID-19 that lead to hospitalizations and fatalities. This framework can provide guidance to decision-makers on optimal intervention strategies for protecting high-risk individuals while reopening communities. This optimization framework demonstrates a practical approach to conduct decision-making in an uncertain environment and can be useful for the prioritization of resources and interventions in the case of future epidemics or pandemics. Resources on understanding and implementing the framework are publicly available, including an award-winning online optimization tool that automatically accesses county-level data from Census, Centers for Disease Control and Prevention (CDC), and Johns Hopkins COVID-19 repositories.