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

COVID-19 cumulative incidence postmortem surveillance serology underreporting

Journal

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
ISSN: 1537-6591
Titre abrégé: Clin Infect Dis
Pays: United States
ID NLM: 9203213

Informations de publication

Date de publication:
08 02 2023
Historique:
received: 03 07 2022
pubmed: 6 10 2022
medline: 11 2 2023
entrez: 5 10 2022
Statut: ppublish

Résumé

The 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. We 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. Multiplicative 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. While 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.

Sections du résumé

BACKGROUND
The 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.
METHODS
We 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.
RESULTS
Multiplicative 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.
CONCLUSIONS
While 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.

Identifiants

pubmed: 36196586
pii: 6748257
doi: 10.1093/cid/ciac797
pmc: PMC9619616
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

424-432

Subventions

Organisme : National Institutes of Health (NIH)
ID : R01GM130668

Commentaires et corrections

Type : ErratumIn

Informations de copyright

© The Author(s) 2022. Published by Oxford University Press on behalf of Infectious Diseases Society of America.

Déclaration de conflit d'intérêts

Potential conflicts of interest. N. E. K. reports occasional consulting for Abata Therapeutics in an unrelated field. D. S. reports previous employment at Pfizer Vaccines (until September 2021); current employment at Hillevax Inc; occasional consulting (until March 2022) through companies as Guidepoint, GLG, and Third Bridge; and stock options from Pfizer Inc and Hillevax Inc. C. V. reports an Elsevier contract for an editor-in-chief role with the journal Epidemics. M. L. reports research grants from the National Cancer Institute/NIH, the UK National Institute for Health and Care Research, the Morris-Singer Fund, the Open Philanthropy Project, the Centers for Disease Control and Prevention (CDC) (separately via Carnegie Mellon University and University of Utah), NIAID/NIH (via University of Michigan), Wellcome Trust, and Pfizer Inc; occasional consulting for Merck & Co, Inc, and Janssen Pharmaceuticals; speaking engagements with Bristol-Myers Squibb and Sanofi Pasteur; participation in the scientific advisory committee of the Coalition for Epidemic Preparedness Innovations; and serving as Director for Science at the CDC Center for Forecasting and Outbreak Analytics. M. S. reports research grants from NIH and Pfizer Inc. All other authors report no potential conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.

Références

Sci Adv. 2021 Mar 5;7(10):
pubmed: 33674304
Int J Epidemiol. 2021 Aug 30;50(4):1091-1102
pubmed: 34058004
BMJ Glob Health. 2021 Feb;6(2):
pubmed: 33563720
BMJ Glob Health. 2022 Aug;7(8):
pubmed: 35998978
J Clin Virol. 2020 Jul;128:104393
pubmed: 32387968
PLoS Comput Biol. 2021 Jun 17;17(6):e1008994
pubmed: 34138845
Lancet. 2021 Apr 3;397(10281):1265-1275
pubmed: 33773118
Clin Infect Dis. 2022 Mar 9;74(5):871-881
pubmed: 34111244
Nat Commun. 2021 Oct 6;12(1):5851
pubmed: 34615863
JAMA Netw Open. 2021 Jan 4;4(1):e2033706
pubmed: 33399860
Nature. 2021 Dec;600(7887):127-132
pubmed: 34695837
Int J Environ Res Public Health. 2021 Aug 16;18(16):
pubmed: 34444386
Public Health Pract (Oxf). 2020 Nov;1:100038
pubmed: 34173573
Lancet Infect Dis. 2021 Apr;21(4):e75-e76
pubmed: 32763195
Rand Health Q. 2017 Jun 19;6(3):5
pubmed: 28845357
Syst Dyn Rev. 2021 Jan-Mar;37(1):5-31
pubmed: 34230767
Lancet. 2022 Apr 16;399(10334):1469-1488
pubmed: 35219376
Lancet. 2022 Apr 16;399(10334):1513-1536
pubmed: 35279232
BMJ. 2021 Feb 17;372:n334
pubmed: 33597166
Sci Rep. 2021 Feb 10;11(1):3455
pubmed: 33568776
Nat Med. 2021 Sep;27(9):1495-1496
pubmed: 34400842
BMC Infect Dis. 2021 Aug 30;21(1):889
pubmed: 34461847

Auteurs

Nicole E Kogan (NE)

Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA.
Machine Intelligence Group for the Betterment of Health and the Environment, Network Science Institute, Northeastern University, Boston, Massachusetts, USA.

Shae Gantt (S)

Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA.

David Swerdlow (D)

Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA.

Cécile Viboud (C)

Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland, USA.

Muhammed Semakula (M)

Rwanda Biomedical Centre, Kigali, Rwanda.

Marc Lipsitch (M)

Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA.

Mauricio Santillana (M)

Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA.
Machine Intelligence Group for the Betterment of Health and the Environment, Network Science Institute, Northeastern University, Boston, Massachusetts, USA.

Articles similaires

[Redispensing of expensive oral anticancer medicines: a practical application].

Lisanne N van Merendonk, Kübra Akgöl, Bastiaan Nuijen
1.00
Humans Antineoplastic Agents Administration, Oral Drug Costs Counterfeit Drugs

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female
Humans United States Aged Cross-Sectional Studies Medicare Part C
1.00
Humans Yoga Low Back Pain Female Male

Classifications MeSH