Evidence map›Paper›PMID 36196586›Full record

ArticleClinical infectious diseases : an official publication of the Infectious Diseases Society of America2023

Leveraging Serosurveillance and Postmortem Surveillance to Quantify the Impact of Coronavirus Disease 2019 in Africa.

Nicole E Kogan, Shae Gantt, David Swerdlow, Cécile Viboud, Muhammed Semakula, Marc Lipsitch, Mauricio Santillana

Erratum issuedAbstract read
In one paragraph

Article in Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Nicole E KoganDepartment of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA.ORCID 0000-0003-1622-0537
Shae GanttDepartment of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA.
David SwerdlowDepartment of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA.
Cécile ViboudDivision of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland, USA.
Muhammed SemakulaRwanda Biomedical Centre, Kigali, Rwanda.
Marc LipsitchDepartment of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA.
Mauricio SantillanaDepartment of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA.

Funding

Development of an Open-Source and Data-Driven Modeling Platform to Monitor and Forecast Disease ActivityR01GM130668 · NIGMS · NORTHEASTERN UNIVERSITY · PI SANTILLANA, MAURICIO · 2018 to 2022
$1.9M
National Institutes of Health (NIH) R01GM130668NIGMS NIH HHS R01 GM130668
6 · The paper itself

Abstract

backgroundThe coronavirus disease 2019 (COVID-19) pandemic has had a devastating impact on global health, the magnitude of which appears to differ intercontinentally: For example, reports suggest that 271 900 per million people have been infected in Europe versus 8800 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 and environmental explanations have been proposed to clarify this discrepancy, systematic underascertainment of infections may be equally responsible.

methodsWe sought to quantify magnitudes of underascertainment in COVID-19's cumulative incidence in Africa. Using serosurveillance and postmortem surveillance, we constructed multiplicative factors estimating ratios of true infections to reported cases in Africa since March 2020.

resultsMultiplicative factors derived from serology data (subset of 12 nations) suggested a range of COVID-19 reporting rates, from 1 in 2 infections reported in Cape Verde (July 2020) to 1 in 3795 infections reported in Malawi (June 2020). 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 that a key reason for low case burden in many African nations is significant underdetection and underreporting.

conclusionsWhile estimating the exact burden of COVID-19 is challenging, the multiplicative factors we present furnish incidence estimates reflecting likely-to-worst-case ranges of infection. Our results stress the need for expansive surveillance to allocate resources in areas experiencing discrepancies between reported cases, projected infections, and deaths.

Indexed as

COVID-19EuropeHumansIncidenceMalawiPandemicsCOVID-19cumulative incidencepostmortem surveillanceserologyunderreporting

Identifiers

PMID36196586
PMCPMC9619616

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.