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Leveraging Serosurveillance and Postmortem Surveillance to Quantify the Impact of COVID-19 in Africa
Nicole Kogan; Shae Gantt; David Swerdlow; Cecile Viboud; Muhammed Semakula; Marc Lipsitch; Mauricio Santillana.
Afiliação
  • Nicole Kogan; Harvard T. H. Chan School of Public Health | Northeastern University
  • Shae Gantt; Harvard T. H. Chan School of Public Health
  • David Swerdlow; Harvard T.H. Chan School of Public Health
  • Cecile Viboud; National Institutes of Health
  • Muhammed Semakula; Rwanda Biomedical Centre
  • Marc Lipsitch; Harvard T. H. Chan School of Public Health | CDC
  • Mauricio Santillana; Northeastern University | Harvard T. H. Chan School of Public Health
Preprint em Inglês | medRxiv | ID: ppmedrxiv-22277196
ABSTRACT
BackgroundThe COVID-19 pandemic has had a devastating impact on global health, the magnitude of which appears to differ intercontinentally for example, reports suggest 271,900 per million people have been infected in Europe versus 8,800 per million people in Africa. While Africa is the second largest continent by population, its reported COVID-19 cases comprise <3% of global cases. Although social, environmental, and environmental explanations have been proposed to clarify this discrepancy, systematic infection underascertainment may be equally responsible. MethodsWe seek to quantify magnitude of underascertainment in COVID-19s cumulative incidence in Africa. Using serosurveillance and postmortem surveillance, we constructed multiplicative factors estimating ratios of true infections to reported cases in African nations since March 2020. ResultsMultiplicative factors derived from serology data - in a subset of 12 nations - suggested a range of COVID-19 reporting rates, from 1 in 630 infections reported in Kenya (May 2020) to 1 in 15 infections reported in South Africa (November 2021). The largest multiplicative factor, 3,795, corresponded to Malawi (June 2020), suggesting <0.05% of infections captured. A similar set of multiplicative factors for all nations derived from postmortem data points toward the same

conclusion:

reported COVID-19 cases are unrepresentative of true infections, suggesting a key reason for low case burden in many African nations is significant underdetection and underreporting. ConclusionsWhile estimating COVID-19s exact burden is challenging, the multiplicative factors we present provide incidence curves reflecting likely-to-worst-case ranges of infection. Our results stress the need for expansive surveillance to allocate resources in areas experiencing severe discrepancies between reported cases, projected infections, and deaths. SummaryHere we present a range of estimates quantifying the extent of underascertainment of COVID-19 cumulative incidence in Africa. These estimates, constructed from serology and mortality data, suggest that systematic underdetection and underreporting may be contributing to the seemingly low burden of COVID-19 reported in Africa.
Licença
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Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Tipo de estudo: Experimental_studies / Estudo observacional / Revisão sistemática Idioma: Inglês Ano de publicação: 2022 Tipo de documento: Preprint
Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Tipo de estudo: Experimental_studies / Estudo observacional / Revisão sistemática Idioma: Inglês Ano de publicação: 2022 Tipo de documento: Preprint
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