Epidemiology of SARS-CoV-2 Infection in Ethiopia: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: The Coronavirus disease of 2019 (COVID-19) is a catastrophic emerging global health threat caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 has a wide range of complications and sequelae. It is devastating in developing countries, causing serious health and socioeconomic crises as a result of the increasingly overburdened healthcare system. This study was conducted to determine the prevalence of SARS-CoV-2 infection in Ethiopia. Methods: Electronic databases, such as PubMed, Google Scholar, Web of Science, Research Gate, Embase, and Scopus were thoroughly searched from March to April 2022 to identify relevant studies. The quality of the included studies was evaluated using the Newcastle-Ottawa Quality scale for cross-sectional studies. STATA-12 was used for analysis. A random-effects model was used to compute the pooled prevalence of SARS-CoV-2 infection. The heterogeneity was quantified by using the I2 value. Subgroup analysis was done for sex, age of study subjects, population type, diagnostic methods, and publication year. Publication bias was assessed using a funnel plot and Egger’s test. A sensitivity analysis was also done. Result: 11 studies consisting of 35,376 study participants (15,759 male and 19,838 female) were included in this systematic review and meta-analysis. The pooled prevalence of SARS-CoV-2 was 8.83%. There was substantial heterogeneity, with an I2 value of 99.3%. The pooled prevalence of SARS-CoV-2 was higher in males (9.27%) than in females (8.8%). According to the publication year, a higher prevalence was obtained in 2021 (12.69%). Similarly, it was higher in the population of specific groups (16.65%) than in the general population (5.75%). Conclusion: the national pooled prevalence of SARS-CoV-2 infection in the Ethiopian population was 8.83%. This indicates that the burden of COVID-19 is still high, which urges routine screening and appropriate treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.041 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".