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Infectious Disease Transmission Models to Predict, Evaluate, and Improve Understanding of COVID-19 Trajectory and Interventions

Abstract

Coronavirus disease (COVID-19) has spread rapidly around the world with devastating consequences. Large questions loom about how this epidemic will proceed and what interventions can slow the spread. In the face of a global pandemic with a novel infectious agent, policymakers face the difficult task of deciding how and when to adopt measures to control COVID-19—measures with profound economic and social impacts (1). They face the extraordinarily difficult challenge of finding balance between a societally tolerable burden of death on one side and economic activity and returning toward normalcy on the other. Mathematical models of infectious disease transmission serve a key role in guiding government response; they provide a framework for evaluating the potential impact of different policies—from mask wearing to relaxation of social distancing—on the course of the epidemic and on the expected number of lives lost and whether and when hospital capacity may be exceeded. We are working with the state of Colorado, using transmission models, to help policymakers predict the future course of the epidemic and estimate the potential impact of interventions to slow the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Given the critical role of infectious disease models in this pandemic, it is important to understand their strengths and limitations, as well as why different models may yield conflicting results.

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