Seroprevalence of seven arboviruses of public health importance in sub-Saharan Africa: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The arboviruses continue to be a threat to public health and socioeconomic development in sub-Saharan Africa (SSA). Seroprevalence surveys can be used as a population surveillance strategy for arboviruses in the absence of treatment and vaccines for most arboviruses, guiding the public health interventions. The objective of this study was to analyse the seroprevalence of arboviruses in SSA through a systematic review and meta-analysis. METHODS: We searched PubMed/MEDLINE, Web of Science, Embase, Scopus and ScienceDirect databases for articles published between 2000 and 2022 reporting the seroprevalence of immunoglobulin G (IgG) antibodies to seven arboviruses in various human populations residing in SSA. The included studies were assessed using the checklist for assessing the risk of bias in prevalence studies, and the data were extracted using a standard form. A random effects model was used to estimate pooled seroprevalences. The potential sources of heterogeneity were explored through subgroup analyses and meta-regression. The protocol had been previously registered on International Prospective Register of Systematic Reviews with the identifier: CRD42022377946. RESULTS: A total of 165 studies from 27 countries, comprising 186 332 participants, were included. Of these, 141 were low-risk and 24 were moderate-risk. The pooled IgG seroprevalence was 23.7% (17.9-30.0%) for Chikungunya virus, 22.7% (17.5-28.4%) for dengue virus, 22.6% (14.1-32.5%) for West Nile virus, 16.4% (7.1-28.5%) for yellow fever virus, 13.1% (6.4-21.7%) for Zika virus, 9.2% (6.5-12.3%) for Rift Valley fever virus and 6.0% (3.1-9.7) for Crimean-Congo haemorrhagic fever virus. Subgroup and meta-regression analyses showed that seroprevalence differed considerably between countries, study populations, specific age categories, sample sizes and laboratory methods. CONCLUSION: This SRMA provides information on the significant circulation of various arboviruses in SSA, which is essential for the adoption and planning of vaccines. These findings suggest the need to invest in surveillance and research activities on arbovirus in SSA countries to increase our understanding of their epidemiology to prevent and respond to future epidemics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.019 | 0.003 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".