Vector diversity and malaria prevalence: global trends and local determinants
Bibliographic record
Abstract
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 prevalence across large spatial scales, the influence of vector diversity on disease risk has rarely been examined despite the role of vectors as obligatory intermediate hosts for many parasites. Malaria, for instance, can be transmitted by over 70 species of mosquitoes, but the impact of this diversity on malaria risk remains unclear. Further, such relationships are likely dependent on the context in which disease transmission occurs, as arthropod life history and behavior are highly sensitive to environmental factors such as temperature. We studied the relationship between vector diversity, malaria prevalence, and environmental attributes using a unique dataset we curated by integrating several open-access sources. Globally, the association between vector species richness and malaria prevalence differed by latitude, indicating that this relationship is strongly dependent on underlying environmental conditions. Structural equation models further revealed different processes by which the environment impacts vector community assemblage and function, and subsequently disease prevalence, in different regions. In Africa, the environment exerted a top-down influence on disease through its role in shaping vector communities, whereas in Southeast Asia, disease prevalence is influenced by more complex interactions between the physical and socioeconomic environment (i.e., rainfall and GDP) and vector diversity across sites. This work highlights the key role of vector diversity in structuring disease distribution at large spatial scales and offers crucial insights to vector management and disease control. Significance statement The global health threat from persistent and emerging vector-borne diseases continues to increase and is exacerbated by rapid environmental and societal change. Predicting how disease burden will shift in response to these changes necessitates a clear understanding of existing determinants of disease risk. We focused on an underappreciated potential source of variation in disease burden – vector diversity – and its role in structuring global malaria distribution. Our work revealed that vector diversity influences malaria prevalence and that the strength and nature of this association strongly depend on local environmental context. Extending disease transmission theory, surveillance, and control to embrace heterogeneity in vector community structure and function across space and time is an asset in the fight against vector-borne diseases.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".