Mitigation Strategies for COVID-19: Lessons from the K-SEIR Model Calibrated to the Observable Data
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jrfm-15-00248.pdf
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2.98 MB
Format
Adobe PDF
Checksum (MD5)
04dd59f811b44494c882262695ced880
Author(s)
Lipton, Alexander
de Prado, Marcos Lopez
Date Issued
June 1, 2022
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Journal of Risk and Financial Management 15 (6): 248 (2022)
Version
Final published version
Abstract
This article develops a detailed epidemiological multi-factor model, the K-susceptible–exposed–infected–removed (K-SEIR) model, and several simpler sub-models as its building blocks. The general model enables us to account for all the relevant COVID-19 features, its disparate impact on different population groups, and interactions within and between the groups. It also includes the availability (or lack thereof) of spare hospital beds and intensive care units (ICU) to accommodate the pent-up demand due to the pandemic. We use the most recent hospitalization and mortality data to calibrate the model. Since our model is multi-factor, we can use it to simulate and analyze the consequences of the sheltering-in-place for each specific group and compare the lives saved and lost due to this measure. We show that in countries with well-developed healthcare systems and a population willing to abide by suitable containment and mitigation procedures, the sheltering in place of the entire community is excessive and harmful when considered holistically. At the same time, sealing nursing homes as thoroughly as possible to avoid high infection and mortality rates is an absolute necessity.
MIT Department
MIT Connection Science (Research institute)
Terms of Use
Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.3390/jrfm15060248
