Informing COVID-19 Policy Under Uncertainty

Abstract

The COVID-19 pandemic has required swift action from public health officials and the infectious disease modeling community. However, deep uncertainties pose challenges both for choosing the best policies and for communicating the rationale behind those choices to the public. While models provide essential decision support, they should avoid attempts at precise predictions and help account for and hedge against uncertainty. This webinar, hosted by the Society for Decision Making Under Deep Uncertainty, is an interdisciplinary conversation among researchers who have sought to inform COVID-19 policy with a focus on confronting uncertainty. Michael Runge, Katriona Shea, and Rebecca Borchering will present their work on using multi-model decision support approaches to simulate multi-model COVID-19 scenarios through the COVID-19 Scenario Modeling Hub. Pedro Nascimento de Lima and Robert Lempert will discuss their work on using Robust Decision Making to stress-test reopening strategies and to evaluate how society might respond to a future pandemic. Matthew Biggerstaff, an Epidemiologist at CDC, will present CDC's use of modeling to improve the pandemic response. Finally, the panel will discuss lessons learned and remaining challenges for sound decision making under uncertainty during this and a next pandemic.

Date
Nov 29, 2021 9:00 AM
Location
DMDU 2021 Webinar Series
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), Policy Analysis and Decision Making Under Deep Uncertainty.

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