Mitigation Strategies for COVID-19: Lessons from the K-SEIR Model Calibrated to the Observable Data

Alexander Lipton and Marcos Lopez de Prado
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Alexander Lipton: The Jerusalem School of Business Administration, The Hebrew University of Jerusalem, Jerusalem 91120, Israel
Marcos Lopez de Prado: Department of Operations Research and Information Engineering, Cornell University, New York, NY 14850, USA

JRFM, 2022, vol. 15, issue 6, 1-38

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.

Keywords: COVID-19; SIR model; SEIR model; K-SEIR model; lockdowns; nursing homes (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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