Seeking Robust COVID-19 Response and Adaptation Strategies

Abstract

SARS-CoV-2, the virus that causes COVID-19, was first confirmed in the United States on January 19th, 2020, when a man in a Snohomish county urgent care presented with a cough and fever. In the five months since, COVID-19 has spread to every state in the union, with more than 2 million confirmed cases and over 100,000 related deaths. RAND has developed an epidemiological model that describes how Nonpharmaceutical Interventions (NPIs) can delay the spread of the virus, and a general equilibrium model to estimate the economic effects of these interventions. This talk builds on this work and further relaxes structural and parametrical assumptions in this model, employing RDM to investigate i) To what extent current strategies based on NPIs are robust to existing epidemiological and behavioral uncertainty; ii) Under which conditions COVID-19 interventions could fail, and iii) How these strategies could be improved and made more robust. Preliminary findings suggest that strategies based on interrupting economic activity alone as a means of managing outbreaks are not robust to a wide range of futures—that is, they either impose significant economic costs or do not manage infections sufficiently. Including actions that reduce the spread without hindering economic activity—such as mask wearing and other physical distancing measures, have the potential alternatives to improve the robustness of COVID-19 response and recovery strategies, particularly when combined with an adaptive strategy that evolves in response to testing data.

Date
Nov 10, 2020 1:00 PM — Nov 13, 2020 3:00 PM
Location
Web
Pedro Nascimento de Lima
Pedro Nascimento de Lima

I’m a Ph.D. Candidate in Policy Analysis working at the intersection of Simulation Modeling (ABM, Systems Dynamics, Microsim), Infectious Diseases and Decision Making Under Deep Uncertainty. Oh, and I love doing all of that with R.

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