Environmental correlates of Aedes aegypti abundance in the West Valley region of San Bernardino County, California, USA, from 2017 to 2023: an ecological modeling study
Bibliographic record
Abstract
BACKGROUND: Aedes mosquitoes, particularly Aedes aegypti and Ae. albopictus, are major vectors of globally significant diseases such as dengue, Zika, and chikungunya. Since 2013, Ae. aegypti populations have rapidly expanded in California, making control efforts difficult due to their widespread, small-scale breeding sites and strong adaptation to urban environments. METHODS: Remote sensing technologies, coupled with Geographic Information Systems (GIS), offer innovative solutions for mosquito surveillance and control. However, understanding the environmental drivers of mosquito abundance, particularly in California's diverse ecological settings, remains an important gap. To address this gap, we analyzed Ae. aegypti abundance (2017 to 2023) in relation to environmental variables, such as temperature, precipitation, surface water, elevation, and built environment. We applied hotspot analysis to identify spatial clusters of high mosquito abundance and used a generalized additive model (GAM) with a negative binomial distribution to assess environmental and meteorological influences on mosquito counts. RESULTS: Hotspot analyses revealed clusters of Ae. aegypti hotspots near residential areas. Aedes aegypti counts increased with higher surface water availability and temperature. CONCLUSIONS: Our study characterizes the spatial and temporal dynamics of Ae. aegypti mosquito abundance in the West Valley region of San Bernardino County from 2017 to 2023, shedding light on the influence of environmental factors and human activities on temporal trends. Our findings emphasize the critical role of temperature and water availability in shaping mosquito population dynamics, highlighting the need for proactive vector control strategies in response to environmental changes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".