Warming temperatures reduce lifespan and vectorial capacity of Anopheles mosquitoes in Ghana
Bibliographic record
Abstract
Climate change and variability are altering the ecology of malaria vectors, with implications for disease transmission in sub-Saharan Africa. In this study, we analysed long-term historical temperature, rainfall and relative humidity data across Ghana's climatic zones to evaluate their trends. We then incorporated these data into simple climate-driven biological models to assess how they impacted Anopheles mosquito lifespan, their Vectorial Capacity and Extrinsic Incubation Period of malaria parasites. This approach allowed us to assess the potential impacts of climate change on malaria transmission dynamics in the country. The analysis revealed, on the long-term, significant temperature warming (over 1.5°C), marked decline in relative humidity, and no clear trends in rainfall across all climatic zones. Similarly, Anopheles mosquito lifespan (with seasonal variations of 5-11 days in the north and 9-14 days in the south) showed long-term decline while Extrinsic Incubation Period (with seasonal average range of 6-11 days in the north and up to 13 days in the south) showed shortened development time. Even though Vectorial Capacity showed no clear long-term trends, its values were generally below 10, indicating low-to-moderate malaria transmission potential nationwide. Although regional and local microclimatic variations may continue to support localized malaria transmission risk, the long-term rise in temperatures and decline in humidity are likely reducing mosquito longevity and malaria transmission potential in Ghana. These findings underscore the importance of climate-informed and region-specific strategies in the National Malaria Elimination Program to improve targeted interventions and optimize vector control efforts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".