Anti-Malaria Recommendations for Sub-Saharan Africa During the COVID-19 Pandemic
Bibliographic record
Abstract
Because of COVID-19, the vulnerable healthcare systems of many African countries have faced additional burdens. As governments divert resources towards COVID-19 efforts, researchers and international organizations have voiced concerns on how the pandemic would affect malaria incidence, especially in malaria-endemic regions. In this study, we searched relevant keywords on PubMed to systematically review the existing literature on malaria recommendations and malaria outcomes during COVID-19. Special attention was brought to the malaria recommendations in Nigeria, The Democratic Republic of Congo, and South Africa, as these three countries vary in malaria and COVID-19 incidence. We included 20 relevant publications that highlight the importance of chemoprevention, vector control, and rapid diagnostics in decreasing malaria incidence in the context of COVID-19. We also examined how malaria recommendations vary among the three countries of interest. We found that while both insecticide-treated nets and antimalarials are essential to preventing additional malaria cases, continuous supply of antimalarials is especially important in preventing hundreds of thousands of additional malaria deaths. Certain countries like South Africa still use chloroquine against Plasmodium vivax. Unwarranted use of chloroquine against COVID-19 not only increases chloroquine resistance but decreases supplies available against P. vivax. To encourage community safety and compliance, additional protection is recommended for indoor-residual spraying delivery teams and seasonal malaria chemoprevention campaign community health workers. Finally, mass drug administrations are recommended only for urban regions with low malaria endemicity, and malaria rapid diagnostic tests should be used together with COVID-19 diagnostics. Continued funding and government efforts are required to implement these recommendations and prevent additional malaria drug resistance, cases, and deaths during the COVID-19 pandemic.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".