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PreprintCited by 177COVID-19 epidemiological studiesSARS-CoV-2 and COVID-19 ResearchSARS-CoV-2 detection and testing

The DOI matches its Crossref record; 44 of the 46 references it cites were found in a registry or an index.

SARS-CoV-2 infection in Africa: A systematic review and meta-analysis of standardised seroprevalence studies, from January 2020 to December 2021

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Lewis, HC, Ware, H, Whelan, M, Subissi, L, Li, Z, Ma, X, Nardone, A, Valenciano, M, Cheng, B, Noel, K, Cao, C, Yanes-Lane, M, Herring, B, Talisuna, A, Nsenga, N, Balde, T, Clifton, DA, Van Kerkhove, M, Buckeridge, DL, Bobrovitz, N, Okeibunor, J, Arora, RK, Bergeri, I, the UNITY Studies Collaborator Group·2022·BMJ Global Health
DOI 10.1101/2022.02.14.22270934PMC PMC9402450

Preprint — published as 10.1136/bmjgh-2022-008793

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Abstract

INTRODUCTION: Estimating COVID-19 cumulative incidence in Africa remains problematic due to challenges in contact tracing, routine surveillance systems and laboratory testing capacities and strategies. We undertook a meta-analysis of population-based seroprevalence studies to estimate SARS-CoV-2 seroprevalence in Africa to inform evidence-based decision making on public health and social measures (PHSM) and vaccine strategy. METHODS: We searched for seroprevalence studies conducted in Africa published 1 January 2020-30 December 2021 in Medline, Embase, Web of Science and Europe PMC (preprints), grey literature, media releases and early results from WHO Unity studies. All studies were screened, extracted, assessed for risk of bias and evaluated for alignment with the WHO Unity seroprevalence protocol. We conducted descriptive analyses of seroprevalence and meta-analysed seroprevalence differences by demographic groups, place and time. We estimated the extent of undetected infections by comparing seroprevalence and cumulative incidence of confirmed cases reported to WHO. PROSPERO: CRD42020183634. RESULTS: We identified 56 full texts or early results, reporting 153 distinct seroprevalence studies in Africa. Of these, 97 (63%) were low/moderate risk of bias studies. SARS-CoV-2 seroprevalence rose from 3.0% (95% CI 1.0% to 9.2%) in April-June 2020 to 65.1% (95% CI 56.3% to 73.0%) in July-September 2021. The ratios of seroprevalence from infection to cumulative incidence of confirmed cases was large (overall: 100:1, ranging from 18:1 to 954:1) and steady over time. Seroprevalence was highly heterogeneous both within countries-urban versus rural (lower seroprevalence for rural geographic areas), children versus adults (children aged 0-9 years had the lowest seroprevalence)-and between countries and African subregions. CONCLUSION: We report high seroprevalence in Africa suggesting greater population exposure to SARS-CoV-2 and potential protection against COVID-19 severe disease than indicated by surveillance data. As seroprevalence was heterogeneous, targeted PHSM and vaccination strategies need to be tailored to local epidemiological situations.

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Figure 1 PRISMA flow diagram. PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses.
Figure 1 PRISMA flow diagram. PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses.CC BY-NC · PubMed Central
Figure 2 Reported seroprevalence, variants of concern and cumulative incidence by selected countries over time, March 2020–December 2021. Plots for all countries are reported in online supplemental figure S2. Top panel, left axis: shaded areas represent the relative frequency of major variants of concern (VOCs) circulating, based on weekly counts of hCoV-19 genomes submitted to GISAID (Global Initiative on Sharing Avian Influenza Data) that we have aggregated by month. Weeks with fewer than 10 total submissions in a given country were excluded from the analysis. Top panel, right axis: daily confirmed cases reported to WHO on a national level per million people, smoothed using local regression (Locally Estimated Scatterplot Smoothing, LOESS). Middle panel: each point is an individual seroprevalence study, and identical shapes represent studies originating from the same cohort or repeated cross-sectional data source. Bottom panel: cumulative incidence of confirmed cases per 100 people.
Figure 2 Reported seroprevalence, variants of concern and cumulative incidence by selected countries over time, March 2020–December 2021. Plots for all countries are reported in online supplemental figure S2. Top panel, left axis: shaded areas represent the relative frequency of major variants of concern (VOCs) circulating, based on weekly counts of hCoV-19 genomes submitted to GISAID (Global Initiative on Sharing Avian Influenza Data) that we have aggregated by month. Weeks with fewer than 10 total submissions in a given country were excluded from the analysis. Top panel, right axis: daily confirmed cases reported to WHO on a national level per million people, smoothed using local regression (Locally Estimated Scatterplot Smoothing, LOESS). Middle panel: each point is an individual seroprevalence study, and identical shapes represent studies originating from the same cohort or repeated cross-sectional data source. Bottom panel: cumulative incidence of confirmed cases per 100 people.CC BY-NC · PubMed Central
Figure 3 Pooled seroprevalence from infection or vaccination (random-effects model) by UN subregion and quarter, Q3 2020–Q3 2021. Point estimates and 95% CIs (error bars) are reported for each quarter, as well as the number of samples pooled (n).
Figure 3 Pooled seroprevalence from infection or vaccination (random-effects model) by UN subregion and quarter, Q3 2020–Q3 2021. Point estimates and 95% CIs (error bars) are reported for each quarter, as well as the number of samples pooled (n).CC BY-NC · PubMed Central
Figure 4 Factors associated with seroprevalence. Top panel: meta-analysis results. We calculated the ratio in prevalence between subgroups within each study then aggregated the ratios across studies using inverse variance-weighted random-effects meta-analysis. Heterogeneity was quantified using the I2 statistic. Each row represents a separate meta-analysis. Bottom panel: multivariable analysis using a Poisson generalised linear mixed-effects regression to model seroprevalence. CLIA, chemiluminescent immunoassay; cumulative incidence, cumulative incidence of confirmed cases per 100 people; ELISA, enzyme-linked immunosorbent assay; LFIA, lateral flow immunoassay; PR, prevalence ratio.
Figure 4 Factors associated with seroprevalence. Top panel: meta-analysis results. We calculated the ratio in prevalence between subgroups within each study then aggregated the ratios across studies using inverse variance-weighted random-effects meta-analysis. Heterogeneity was quantified using the I2 statistic. Each row represents a separate meta-analysis. Bottom panel: multivariable analysis using a Poisson generalised linear mixed-effects regression to model seroprevalence. CLIA, chemiluminescent immunoassay; cumulative incidence, cumulative incidence of confirmed cases per 100 people; ELISA, enzyme-linked immunosorbent assay; LFIA, lateral flow immunoassay; PR, prevalence ratio.CC BY-NC · PubMed Central

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  1. 10.1101/2021.12.14.21267791 · DOI
  2. 10.1101/2021.11.17.21266471 · DOI
  3. 10.1101/2021.06.27.21259271 · DOI
  4. 10.1101/2020.11.17.20233460 · DOI

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Metadata

Authors
Lewis, H., Ware, H., Whelan, M., Subissi, L., Li, Z., Ma, X., Nardone, A., Valenciano, M., Cheng, B., Noel, K., Cao, C., Yanes-Lane, M., Herring, B., Talisuna, A., Nsenga, N., Balde, T., Clifton, D., Van Kerkhove, M., Buckeridge, D., Bobrovitz, N., Okeibunor, J., Arora, R., Bergeri, I., & the UNITY Studies Collaborator Group
Affiliations
  • University of Calgary (CA)
  • World Health Organization (CH)
  • University of Waterloo (CA)
  • University of Toronto (CA)
  • World Health Organization Regional Office for Africa (CG)
ORCID
Venue
BMJ Global Health
Publisher
openRxiv
Published
2022-02-15
Volume
7
Issue
8
Pages
e008793-e008793
ISSN
2059-7908 (ISSN-L)
DOI
10.1101/2022.02.14.22270934
PMC
PMC9402450
OpenAlex
W4292774342
Cited by
177
References
46
Citations by year
2026: 25 · 2025: 30 · 2024: 39 · 2023: 47 · 2022: 36
FWCI
4.07
Type
journal article
Language
en
Open access
Licence
cc-by-nc
Locations
  • BMJ Global Health (publishedVersion, cc-by-nc)
  • medRxiv (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • HAL (submittedVersion, cc-by-nc-nd)
  • HAL (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • World Health Organization
  • Public Health Agency of Canada
  • Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology
  • Public Health Agency
  • Bundesministerium für Gesundheit
  • Canadian Medical Association
  • Robert Koch Institut
  • Centers for Disease Control and Prevention
  • Medical Research Council (MR/T008822/1)
Topics
  • Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
  • Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
  • Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 detection and testing
Keywords
SARS-CoV-2, COVID-19 pandemic, seroprevalence, Africa, underreporting of infections, urban-rural differences, age differences in infection, subregional variation, public health and social measures, risk of bias assessment, SARS-CoV-2 reinfection
MeSH terms
COVID-19, SARS-CoV-2, Adult, Africa, Child, Europe, Humans, Seroepidemiologic Studies

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