Pandemic Intensity Estimation using Dynamic Factor Modeling
Bibliographic Data
| ID | 6170226 |
|---|---|
| Authors | Andrew Cooke (0009-0008-6440-9100, United States Department of the Treasury), John Vivian (0000-0002-4778-7723, Film Independent) |
| Year | 2025 |
| Volume | 16 |
| Issue | 1 |
| Pages | 37-61 |
| Publication date | 2025-02-08 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Statistics Politics and Policy (JOURNAL) |
| Journal identifiers | ISSN: 2151-7509 • E-ISSN: 2194-6299 |
| Publisher | De Gruyter (PUBLISHER • DE) |
| DOI | 10.1515/spp-2024-0042 |
| OpenAlex | W4407264305 |
| Language | EN |
| Citations received | 1 |
| References cited | 15 |
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
Biology · Computational biology · Coronavirus disease 2019 (COVID-19 · Disease · Econometrics · Economics · Estimation · Factor (programming language · Infectious disease (medical specialty · Intensity (physics · Pandemic · Physics · Agricultural risk and resilience · Computer Science · COVID-19 epidemiological studies · COVID-19 Pandemic Impacts · Medicine · Internal Medicine
| Highly cited | No |
|---|