A Malaria-Free World? Bring Back Public Health Entomologists
Bibliographic record
Abstract
Malaria incidence and mortality rates continue to rise due to many interconnected factors. Current efforts to dampen this increase include innovative interventions such as a trial of the RTS, S malaria vaccine in Africa, using dual insecticides to impregnate mosquito nets, and the release of Wolbachia -infected Anopheles mosquitoes, malaria’s vector. The present review focuses on global efforts to control Anopheles mosquitoes, briefly reviewing the history of world-wide programs led by the World Health Organization, and emphasizing the need for qualified personnel to design, implement, and evaluate vector control interventions. Although incidence of malaria has declined in some areas between 2015 and 2022, the global incidence and mortality rate continues to be much higher than the target outlined in the sustainable development goals. Despite the geographical reduction in malaria-endemic regions, surveillance shows disturbing trends including the invasion of species to other areas, the presence of species once thought to be eradicated, the discovery of Anopheles stephensi , a day-biting efficient vector from Asia, and a severe shortage of healthcare workers and field public health entomologists in malaria-endemic areas, with the highest burden continuing to be in the WHO African region. Factors including adaptation of vectors, territorial expansion of new vectors, insecticide resistance, and climate change have complicated the worldwide situation. At the heart of malaria control and elimination, Anopheles control is of undisputed importance. Malaria continues to be a significant global health burden and goals for its reduction have not been met. The scarcity of properly trained public health entomologists and a lack of properly managed vector control programs is a challenge that must be addressed. Countries suffering from a high burden of malaria need field entomologists to provide technical information and guidance on vector control activities so that communities can have simple and practical approaches to controlling malaria.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".