Optimal COVID-19 Containment Strategies: Evidence Across Multiple Mathematical Models

53 Pages Posted: 5 May 2021 Last revised: 23 Jun 2021

See all articles by Hyun-Soo Ahn

Hyun-Soo Ahn

University of Michigan, Stephen M. Ross School of Business

John Silberholz

University of Michigan, Stephen M. Ross School of Business

Xueze Song

University of Illinois Urbana-Champaign, Gies College of Business

Xiaoyu Wu

Duke University

Date Written: April 24, 2021

Abstract

Since March 2020, numerous models have been developed to support policymakers in understanding, forecasting, and controlling the COVID-19 pandemic. Differences in data, assumptions, and underlying theory, coupled with unknowns about a novel virus, led these models to generate divergent forecasts and proposed responses. A policymaker using a single model is left to wonder if their decision is truly of high quality or if they are being misled by the idiosyncrasies of the selected model. In addition, many COVID-19 optimization models are cast as optimal control problems with abstract decision variables and frequent changes to policy, so translating the optimal solution to implementable actions is not straightforward.

We propose a multi-model optimization (MMO) framework that identifies policies that perform well across structurally distinct models, and we apply this to design 12-month COVID-19 containment strategies. Our approach differs from the existing literature in two important aspects. First, we optimize using multiple state-of-the-art forecasting models currently in use. Second, we intentionally draw feasible intervention levels from each state’s own past and current responses, making it easy to implement the proposed policy.

We find that a policy based on a single model can perform badly (cost increases of 100% or more) when models are misspecified, and that the MMO policy significantly diminishes the impact of model uncertainties. We propose optimal containment policies for all 50 US states over a one-year period and find that the optimal policy can vary significantly by state. We also study the impacts of virus variants and lockdown fatigue.

Note: Funding Statement: No outside funding was used to conduct the research.

Declaration of Interests: All authors do not have any competing interests.

Keywords: COVID-19, comparative modeling, policy modeling

JEL Classification: I18, H75

Suggested Citation

Ahn, Hyun-Soo and Silberholz, John and Song, Xueze and Wu, Xiaoyu, Optimal COVID-19 Containment Strategies: Evidence Across Multiple Mathematical Models (April 24, 2021). Available at SSRN: https://ssrn.com/abstract=3834668 or http://dx.doi.org/10.2139/ssrn.3834668

Hyun-Soo Ahn (Contact Author)

University of Michigan, Stephen M. Ross School of Business ( email )

701 Tappan St
R5456
Ann Arbor, MI 48109-1234
United States

John Silberholz

University of Michigan, Stephen M. Ross School of Business ( email )

701 Tappan Street
Ann Arbor, MI MI 48109
United States

Xueze Song

University of Illinois Urbana-Champaign, Gies College of Business ( email )

601 E John St
Champaign, IL 61820
United States

Xiaoyu Wu

Duke University ( email )

201 Science Drive
Box 90312
Durham, NC 27708-9985
United States

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