Spatial and Temporal Variation in Vector-borne Disease Risk; Influence of Land Cover, Irrigation, and Multiple Vector Species on West Nile Virus Transmission
Bibliographic record
Abstract
Vector-borne pathogens, such as Malaria, Dengue, West Nile and Zika virus, infect hundreds of millions of people each year and lead to widespread human morbidity and mortality, with enormous spatial and temporal variation in disease risk. The recent emergence of West Nile virus (WNV) into the developed world offers a unique opportunity to better understand ecological drivers that contribute to variation in disease risk, as a step toward more effective disease management through targeted interventions. In Chapter 1, we examined correlations between rice cultivation and WNV human disease incidence in rice-growing regions within the United States (US). We found WNV human disease incidence increased with the fraction of each county under rice cultivation in California, but not in the southern US. We show that this is likely due to regional differences in the mosquitoes transmitting WNV. These results illustrate how cultivation of particularly water-demanding agricultural crops can increase mosquito-borne disease risk and how spatial variation in vector ecology can alter the relationship between land cover and disease. In Chapter 2, we examined the effect of irrigation, climate and land cover on mosquito abundance and WNV human disease cases across California. Irrigation made up nearly a third of total water inputs to the region, with irrigation exceeding precipitation in some dry regions. Irrigation reduced seasonal variability in mosquito abundance by more than 40%, and increased abundance by more than an order of magnitude. In addition, irrigation increased human WNV cases and explained 33% of variation in WNV cases among California counties. These results suggest that irrigation can increase and decouple mosquito populations from natural precipitation variability, resulting in sustained and increased disease risk. In Chapter 3, we quantified the risk of WNV transmission to humans from 6 Culex mosquito species by integrating mosquito abundance, infection prevalence, vector competence, and blood feeding patterns, and examined correlations between risk indices and human disease cases. Human WNV cases were strongly correlated with the density of infectious vectors feeding on humans. However, different mosquito vector species contributed to transmission in different land use types and within seasons and across years. Culex tarsalis was more abundant in agricultural areas, whereas Culex pipiens and Culex quinquefasciatus were abundant in developed and agricultural areas, and Culex erythrothorax was abundant in wetland areas. As a result, WNV risk did not change substantially along either agricultural or urbanization land use gradients because the diversity of vectors maintained high disease risk across a range of habitats. These results show how a diversity of vectors can maintain an ecosystem disservice - vector borne disease – by their differential response to environmental disturbance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".