Predicting West Nile Virus Infection Risk From the Synergistic Effects of Rainfall and Temperature

Area of Science:

• Environmental Science
• Epidemiology
• Vector-borne Disease Ecology

Background:

• Mosquito-based surveillance is crucial for estimating West Nile virus (WNV) transmission risk to humans.
• Temperature and precipitation significantly influence mosquito infection rates and WNV transmission dynamics.
• Predictive models integrating weather data offer early warnings for WNV infection risk.

Purpose of the Study:

• To evaluate a prior WNV mosquito infection model's predictive accuracy in a new region.
• To enhance the model's predictive power by including additional weather factors impacting mosquito development.

Main Methods:

• Assessed the performance of an existing WNV mosquito infection model.
• Incorporated local mosquito infection rates and weather data for model calibration.
• Utilized interaction terms between temperature and precipitation to refine predictions.

Main Results:

• The original model captured general trends but required local calibration for improved accuracy.
• Interaction terms between temperature and precipitation significantly enhanced model performance.
• Temperature exhibited a stronger influence than rainfall; high temperatures amplified infection risk during low rainfall periods.

Conclusions:

• The refined model demonstrates practical, stable, and operationally valid performance for predicting WNV infection rates.
• Local data calibration is essential for accurate WNV transmission risk assessment.
• Understanding weather-environment interactions is key to predicting vector-borne disease outbreaks.