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  • 2022
  • Working Paper

THEMIS: A Framework for Cost-Benefit Analysis of COVID-19 Non-Pharmaceutical Interventions

By: Dimitris Bertsimas, Michael Lingzhi Li and Saksham Soni
  • Format:Print
  • | Language:English
  • | Pages:18
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Abstract

Since December 2019, the world has been ravaged by the COVID-19 pandemic, with over 150 million confirmed cases and 3 million confirmed deaths worldwide. To combat the spread of COVID-19, governments have issued unprecedented non-pharmaceutical interventions (NPIs), ranging from mass gathering restrictions to complete lockdowns. Despite their proven effectiveness in reducing virus transmission, the policies often carry significant economic and humanitarian cost, ranging from unemployment to depression, PTSD, and anxiety. In this paper, we create a data-driven system dynamics framework, THEMIS, that allows us to compare the costs and benefits of a large class of NPIs in any geographical region across different cost dimensions. As a demonstration, we analyzed thousands of alternative policies across 5 countries (United States, Germany, Brazil, Singapore, Spain) and compared with the actual implemented policy.

Our results show that moderate NPIs (such as restrictions on mass gatherings) usually produce the worst results, incurring significant cost while unable to sufficiently slow down the pandemic to prevent the virus from becoming endemic. Short but severe restrictions (complete lockdown for 4-5 weeks) generally produced the best results for developed countries, but only if the speed of reopening is slow enough to prevent a resurgence. Developing countries exhibited very different trade-off profiles from developed countries, and suggests that severe NPIs such as lockdowns might not be as suitable for developing countries in general.

Keywords

COVID-19; Health Pandemics; Policy; Framework; Cost vs Benefits; Outcome or Result; United States; Germany; Brazil; Singapore; Spain

Citation

Bertsimas, Dimitris, Michael Lingzhi Li, and Saksham Soni. "THEMIS: A Framework for Cost-Benefit Analysis of COVID-19 Non-Pharmaceutical Interventions." Working Paper, April 2022.
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About The Author

Michael Lingzhi Li

Technology and Operations Management
→More Publications

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  • Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments By: Kosuke Imai and Michael Lingzhi Li
  • Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules By: Michael Lingzhi Li and Kosuke Imai
  • Learning to Cover: Online Learning and Optimization with Irreversible Decisions By: Alexander Jacquillat and Michael Lingzhi Li
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