A model of COVID-19 pandemic evolution in African countries

TL;DR

This paper models COVID-19 evolution in African countries, estimating key parameters like R0 and unaffected populations, providing insights into containment and vaccination strategies based on data from the first year of the pandemic.

Contribution

It introduces a simultaneous modeling approach for active, recovered, and death cases in African countries, estimating time-dependent R0 and unaffected population fractions.

Findings

R0 decreased from up to 4 to around 1

Unaffected populations range between 1-10% of recovered cases

Modeling provides insights into containment and vaccination strategies

Abstract

We studied the COVID-19 pandemic evolution in selected African countries. For each country considered, we modeled simultaneously the data of the active, recovered and death cases. In this study, we used a year of data since the first cases were reported. We estimated the time-dependent basic reproduction numbers, $R_0$, and the fractions of infected but unaffected populations, to offer insights into containment and vaccine strategies in African countries. We found that $R_0\leq 4$ at the start of the pandemic but has since fallen to $R_0 \sim 1$. The unaffected fractions of the populations studied vary between $1-10$\% of the recovered cases.