Insights and Challenges for the Development of Mosquito Control Action Thresholds Using Historical Mosquito...

Area of Science:

• Vector-borne disease ecology
• Environmental science
• Public health entomology

Background:

• Mosquito control operations rely on timely interventions, often guided by surveillance data.
• Developing proactive action thresholds can enhance the efficiency of mosquito control programs.
• Historical data from the Anastasia Mosquito Control District (AMCD) provides a valuable resource for such development.

Purpose of the Study:

• To identify climate predictors for developing proactive action threshold models for mosquito control.
• To analyze historical mosquito surveillance and climate data to inform vector control strategies.
• To assess the predictive capability of generalized linear models for mosquito population dynamics.

Main Methods:

• Exploratory data analysis of historical mosquito surveillance (light and BG traps) and climate data (temperature, rainfall, humidity) from 2004-2021.
• Construction of generalized linear models (negative binomial regression) using continuous climate variables and categorized "hot," "wet," or "humid" days.
• Model validation using residual error tests, effect comparisons, and predictive performance on recent surveillance data.

Main Results:

• Significant climate predictors for mosquito species groups varied considerably.
• Floodwater Aedes mosquitoes, a dominant group, were most significantly predicted by precipitation (total or frequency) 2-4 weeks prior to collection.
• Model validation indicated limited predictive abilities for both continuous and categorized climate variable models.

Conclusions:

• Climate data can inform mosquito control thresholds, though model predictive power is currently limited.
• Precipitation patterns are key predictors for floodwater Aedes, suggesting potential for proactive control.
• Findings offer insights for optimizing AMCD's program and provide guidance for other control programs and military vector control operations lacking surveillance.