Anti-Malaria Recommendations for Sub-Saharan Africa During the COVID-19 Pandemic
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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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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 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 it