The Heterogeneous Severity of COVID-19 in African Countries: A Modeling Approach

Salihu Sabiu Musa, Xueying Wang, Shi Zhao, Shudong Li, Nafiu Hussaini, Weiming Wang, Daihai He

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)

Abstract

The COVID-19 pandemic has had a considerable impact on global health and economics. The impact in African countries has not been investigated thoroughly via fitting epidemic models to the reported COVID-19 deaths. We downloaded the data for the 12 most-affected countries with the highest cumulative COVID-19 deaths to estimate the time-varying basic reproductive number (R(t)) and infection attack rate. We develop a simple epidemic model and fitted it to reported COVID-19 deaths in 12 African countries using iterated filtering and allowing a flexible transmission rate. We observe high heterogeneity in the case-fatality rate across the countries, which may be due to different reporting or testing efforts. South Africa, Tunisia, and Libya were most affected, exhibiting a relatively higher R(t) and infection attack rate. Thus, to effectively control the spread of COVID-19 epidemics in Africa, there is a need to consider other mitigation strategies (such as improvements in socioeconomic well-being, healthcare systems, the water supply, and awareness campaigns).

Original languageEnglish
Article number32
Pages (from-to)1-16
Number of pages16
JournalBulletin of Mathematical Biology
Volume84
Issue number3
DOIs
Publication statusPublished - Mar 2022

Keywords

  • Attack rate
  • Pandemic
  • Reproduction number
  • SARS-CoV-2
  • Seroprevalence

ASJC Scopus subject areas

  • Neuroscience(all)
  • Immunology
  • Mathematics(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Environmental Science(all)
  • Pharmacology
  • Agricultural and Biological Sciences(all)
  • Computational Theory and Mathematics

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