SARS-CoV-2 infection in Africa: A systematic review and meta-analysis of standardised seroprevalence studies, from January 2020 to December 2021
Bibliographic record
Abstract
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 01-01-2020 to 30-12-2021 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 protocol for seroepidemiological investigations. 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 54 full texts or early results, reporting 151 distinct seroprevalence studies in Africa Of these, 95 (63%) were low/moderate risk of bias studies. SARS-CoV-2 seroprevalence rose from 3.0% [95% CI: 1.0-9.2%] in Q2 2020 to 65.1% [95% CI: 56.3-73.0%] in Q3 2021. The ratios of seroprevalence from infection to cumulative incidence of confirmed cases was large (overall: 97:1, ranging from 10:1 to 958:1) and steady over time. Seroprevalence was highly heterogeneous both within countries - urban vs. rural (lower seroprevalence for rural geographic areas), children vs. adults (children aged 0-9 years had the lowest seroprevalence) - and between countries and African sub-regions (Middle, Western and Eastern Africa associated with higher seroprevalence). Conclusion We report high seroprevalence in Africa suggesting greater population exposure to SARS-CoV-2 and protection against COVID-19 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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.002 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".