Abstract: Modeling and Evaluating Intervention Options and Strategies for COVID-19 Containment: A Biological-Behavioral-Logistics Computation Decision Framework

Abstract

SARs, bird flu, H1N1, Ebola crisis in W. Africa, Zika and current SARS-CoV-2 underscore the critical importance of emergency response and medical preparedness. Such needs are wide-spread as globalization and air transportation facilitate rapid disease spread across the world. Computational modeling of infectious disease outbreaks and epidemics offer insights in propagation patterns and facilitate policy makers to synthesize potential interventions. Current models include inclined decay with an exponential adjustment, SEIR (susceptible, exposed, infectious, recovered) compartmental model, discrete time stochastic processes, and transmission tree. While many of these models incorporate contact tracing to predict spread pattern, key elements on optimal usage of scarce resources and effective and efficient process performance (e.g., diagnostics and screening, non-pharmaceutical interventions, trained personnel/robots for treatment, decontamination) have not been included. This is particularly critical in the fight of COVID-19 containment due to lack of testing kits and the prevalence of asymptomatic transmission, and the long period of hospitalization required by severely sick patients.This work focuses on designing a system computational decision modeling framework that simultaneously i) captures disease spread characteristics, ii) incorporates day-to-day hospital and home care processes and resource usage, iii) explores non-pharmaceutical intervention, social and human behavior and iv) allows for system optimization to minimize infection and mortality under time and labor constraints.