Environmental factors contributing to southern house mosquito presence in Clark County, Nevada, using machine learning
Bibliographic record
Abstract
Abstract The southern house mosquito (Culex quinquefasciatus), a prevalent specie in the southern United States, is a primary vector for diseases including West Nile virus, St. Louis encephalitis, and lymphatic filariasis. In this study, we applied a multi-step machine learning approach to investigate environmental factors influencing the annual presence of Culex quinquefasciatus in Clark County, Nevada, using data from the Global Biodiversity Information Facility spanning 1980 to 2023. Our methodology integrated recursive feature elimination to select top predictors, gradient boosting classifier (GBC) gain-based importance for ranking, shapley additive explanations to capture nonlinear relationships and enhance interpretability, and Spearman correlations to assess monotonic relationships. Among the combination of over twenty temperature and precipitation indices analysed, our results indicate that increased frequency of winter conditions with minimum temperatures below 0 °C significantly reduces the annual presence of Culex quinquefasciatus (Spearman correlation = −0.42, p <0.05). Conversely, a decrease in the frequency of abnormally wet conditions was found to promote Culex quinquefasciatus proliferation. Among the climatic factors, fewer cold days ranked highest, contributing 16.57% to the GBC model’s climatic feature importance, which highlights the critical role of warmer winters in the proliferation of Culex quinquefasciatus. However, when accounted for, urbanization emerged as the dominant factor driving the increased presence of Culex quinquefasciatus, outpacing climatic factors with a 75.96% contribution in the GBC model. Overall, our findings highlight warmer temperatures, reduced precipitation, and increased urbanization as key drivers of Culex quinquefasciatus presence in Clark County. This insight is crucial for informing targeted vector control strategies and public health interventions in urban desert regions, such as Clark County, where environmental and anthropogenic factors converge to increase the risk of mosquito-borne disease transmission.
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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 | 0.001 |
| 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) | 0.000 | 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 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".