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Reconciling model predictions with low reported cases of COVID-19 in Sub-Saharan Africa: insights from Madagascar.
Evans, Michelle V; Garchitorena, Andres; Rakotonanahary, Rado J L; Drake, John M; Andriamihaja, Benjamin; Rajaonarifara, Elinambinina; Ngonghala, Calistus N; Roche, Benjamin; Bonds, Matthew H; Rakotonirina, Julio.
  • Evans MV; Odum School of Ecology and Center for the Ecology of Infectious Diseases, University of Georgia , Athens, GA, USA.
  • Garchitorena A; MIVEGEC, Ecole Pierre Louis de Santé Publique, Université de Montpellier, CNRS, IRD , Montpellier, France.
  • Rakotonanahary RJL; PIVOT , Ranomafana, Madagascar.
  • Drake JM; PIVOT , Ranomafana, Madagascar.
  • Andriamihaja B; Odum School of Ecology and Center for the Ecology of Infectious Diseases, University of Georgia , Athens, GA, USA.
  • Rajaonarifara E; PIVOT , Ranomafana, Madagascar.
  • Ngonghala CN; Madagascar Institut pour la Conservation des Ecosystèmes Tropicaux , Antananarivo, Madagascar.
  • Roche B; MIVEGEC, Ecole Pierre Louis de Santé Publique, Université de Montpellier, CNRS, IRD , Montpellier, France.
  • Bonds MH; PIVOT , Ranomafana, Madagascar.
  • Rakotonirina J; Sorbonne Universite , Paris, France.
Glob Health Action ; 13(1): 1816044, 2020 12 31.
Article in English | MEDLINE | ID: covidwho-814069
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ABSTRACT
COVID-19 has wreaked havoc globally with particular concerns for sub-Saharan Africa (SSA), where models suggest that the majority of the population will become infected. Conventional wisdom suggests that the continent will bear a higher burden of COVID-19 for the same reasons it suffers from other infectious diseases ecology, socio-economic conditions, lack of water and sanitation infrastructure, and weak health systems. However, so far SSA has reported lower incidence and fatalities compared to the predictions of standard models and the experience of other regions of the world. There are three leading explanations, each with different implications for the final epidemic burden (1) low case detection, (2) differences in epidemiology (e.g. low R 0 ), and (3) policy interventions. The low number of cases have led some SSA governments to relaxing these policy interventions. Will this result in a resurgence of cases? To understand how to interpret the lower-than-expected COVID-19 case data in Madagascar, we use a simple age-structured model to explore each of these explanations and predict the epidemic impact associated with them. We show that the incidence of COVID-19 cases as of July 2020 can be explained by any combination of the late introduction of first imported cases, early implementation of non-pharmaceutical interventions (NPIs), and low case detection rates. We then re-evaluate these findings in the context of the COVID-19 epidemic in Madagascar through August 2020. This analysis reinforces that Madagascar, along with other countries in SSA, remains at risk of a growing health crisis. If NPIs remain enforced, up to 50,000 lives may be saved. Even with NPIs, without vaccines and new therapies, COVID-19 could infect up to 30% of the population, making it the largest public health threat in Madagascar for the coming year, hence the importance of clinical trials and continually improving access to healthcare.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia, Viral / Coronavirus Infections / Models, Theoretical Type of study: Experimental Studies / Observational study / Prognostic study Topics: Vaccines Limits: Humans Country/Region as subject: Africa Language: English Journal: Glob Health Action Year: 2020 Document Type: Article Affiliation country: 16549716.2020.1816044

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia, Viral / Coronavirus Infections / Models, Theoretical Type of study: Experimental Studies / Observational study / Prognostic study Topics: Vaccines Limits: Humans Country/Region as subject: Africa Language: English Journal: Glob Health Action Year: 2020 Document Type: Article Affiliation country: 16549716.2020.1816044