Hospitalization dynamics during the first COVID-19 pandemic wave: SIR modelling compared to Belgium, France, Italy, Switzerland and New York City data

TL;DR

This study applies the classical SIR model to COVID-19 hospitalization data from multiple regions, deriving an analytical formula for bed occupancy and estimating key epidemic parameters with high accuracy.

Contribution

It introduces an analytical formula for hospital bed occupancy based on the SIR model and demonstrates its effectiveness across diverse COVID-19 outbreaks.

Findings

High correlation (>98.8%) between model and data

Estimated doubling time, recovery, and hospitalization durations vary among outbreaks

Finer models may be unnecessary for macroscopic epidemic data

Abstract

Using the classical Susceptible-Infected-Recovered epidemiological model, an analytical formula is derived for the number of beds occupied by Covid-19 patients. The analytical curve is fitted to data in Belgium, France, New York City and Switzerland, with a correlation coefficient exceeding 98.8%, suggesting that finer models are unnecessary with such macroscopic data. The fitting is used to extract estimates of the doubling time in the ascending phase of the epidemic, the mean recovery time and, for those who require medical intervention, the mean hospitalization time. Large variations can be observed among different outbreaks.