Pandemic Intensity Estimation using Dynamic Factor Modeling

Cooke Aaron () and Vivian John
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Cooke Aaron: U.S. Department of the Treasury, Washington D.C., USA. The views represented are those of the author and not necessarily those of the U.S. Department of the Treasury or the United States Government
Vivian John: Independent Researcher, San Francisco, USA

Statistics, Politics and Policy, 2025, vol. 16, issue 1, 37-61

Abstract: Individual and policy reactions to the coronavirus pandemic had disparate impacts on viral transmission and were heterogeneous in their influence on economic activity and personal outcomes (Kerpen, Phil, Stephen Moore, and Casey B. Mulligan. 2022. A Final Report Card on the States’ Response to Covid-19. Working Paper 29928. National Bureau of Economic Research). Pandemic researchers struggle with choosing from multiple measurements of disease intensity. This paper is the first to suggest using a restricted data-rich dynamic factor model, generated from a variety of economic and pandemic data series to provide a comprehensive measurement of disease intensity. We use this approach to evaluate vaccination efficacy. We also provide future researchers with an open-source Python package that can run a restricted dynamic factor model with bespoke data input. By using the information generated by this specification, policy makers can choose how to respond to future pandemics with a deeper understanding of the costs and benefits of their choices. This paper concentrates on the United States, and exploits variation between U.S. states, but this approach is generalizable for any populations with similar data availability.

Keywords: macroeconomic policy; government policy; public health (search for similar items in EconPapers)
JEL-codes: C55 I18 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1515/spp-2024-0042

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