Vector diversity and malaria prevalence: global trends and local determinants
Bibliographic record
Abstract
Identifying determinants of global infectious disease burden is a central goal of disease ecology. While it is widely accepted that host diversity structures parasite diversity and disease prevalence, the influence of diversity in vectors-obligatory intermediate hosts for many parasites-has rarely been examined. Malaria, for instance, can be transmitted by over 70 mosquito species, but the impact of this diversity on malaria risk remains unclear. Further, environmental factors, like temperature, may modify this impact by influencing arthropod life history and behaviour. We studied the relationship between vector diversity, malaria prevalence and environmental attributes by curating and analysing data from open-access sources. Globally, the association between vector species richness and malaria prevalence differed by latitude, indicating strong dependence on environmental conditions. Processes by which the environment impacts vector community assemblage and function, and subsequently disease prevalence, varied across regions. In Africa, the environment exerted a top-down influence on disease by shaping vector communities, whereas in Southeast Asia, disease prevalence depended on more complex interactions between the physical and socioeconomic environment (rainfall and GDP) and vector diversity. This work highlights the role of vector diversity in structuring disease distribution and offers insights to disease macroecology theory.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".