Mitigation Strategies for COVID-19: Lessons from the K-SEIR Model

Abstract

We develop a detailed epidemiological multi-factor model, the K-Susceptible-Exposed-Infected-Removed (K-SEIR) model, as well as 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, it can be used to simulate and analyze the consequences of the sheltering-in-place for each specific group, as well as compare 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 sensible 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 best as possible to avoid high infection and mortality rates is an absolute must.

Note: Funding: This research is self-funded.

Conflict of Interest: We have no competing interests of any kind.

Keywords: COVID-19, pandemic, SEIR, K-SEIR, epidemiology, case fatality rate, reproductive numbers, lockdowns

JEL Classification: G0, G1, G2, G15, G24, E44