Updates on malaria epidemiology and profile in Cabo Verde from 2010 to 2019: The goal of Elimination
Bibliographic record
Abstract
Abstract Background: Located in West Africa, Cabo Verde is an archipelago consisting of nine inhabited islands. Malaria has been endemic since the settlement of the islands during the 16th century and is poised to achieve malaria elimination in January 2021. The aim of this research is to characterise the trends in malaria cases from 2010 to 2019 in Cabo Verde as the country transitions from endemic transmission to elimination and prevention of reintroduction phases.Methods: All confirmed malaria cases reported to the Ministry of Health between 2010 and 2019 were extracted from the passive malaria surveillance system. Individual-level data available included age, sex, municipality of residence, and the self-reported countries visited if travelled within the past 30 days, therby classified as imported. Trends in reported cases were visualised and multivariable logistic regression used to assess risk factors associated with a malaria case being imported and differences over time.Results: A total of 814 incident malaria cases were reported in the country between 2010 and 2019, the majority of which were Plasmodium falciparum. Overall, prior to 2017, when the epidemic occurred, 58.1% (95% CI: 53.6-64.6) of infections were classified as imported, whereas during the post-epidemic period, 93.3% (95% CI: 86.9-99.7) were imported. The last locally acquired case was reported in January 2018. Imported malaria cases were more likely to be 25-40 years old (AOR: 15.1, 95% CI: 5.9-39.2) compared to those under 15 years of age and more likely during the post-epidemic period (AOR: 56.1; 95% CI: 13.9-225.5) and most likely to be reported on São Vicente Island (AOR=4256.9, 95% CI=260-6.9e+4) compared to Boavista.Conclusions: Cabo Verde has made substantial gains in reducing malaria burden in the country over the past decade and are poised to achieve elimination in 2021. However, the high mobility between the islands and continental Africa, where malaria is still highly endemic, means there is a constant risk of malaria reintroduction. Characterisation of imported cases provides useful insight for program and enables better evidence-based decision-making to ensure malaria elimination can be sustained.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".