A Process-based Model with Temperature, Water, and Lab-derived Data Improves Predictions of Daily Culex...

Area of Science:

• Ecology
• Epidemiology
• Mathematical Biology

Background:

• Mosquito-borne diseases are increasing in North America, yet accurate mosquito population modeling remains challenging.
• Longitudinal data for model calibration and validation are scarce, hindering seasonal abundance prediction.
• Understanding seasonal mosquito abundance is vital for forecasting disease transmission risk.

Purpose of the Study:

• To develop a mechanistic, process-based model for predicting mosquito population dynamics.
• To capture the life cycle stages (egg, larva, pupa, adult, diapause) of Culex pipiens and Culex restuans.
• To improve the prediction of yearly variations in mosquito abundance for disease risk assessment.

Main Methods:

• Developed a discrete, semi-stochastic, age-structured mechanistic model for mosquito populations.
• Integrated known models for mosquito development and survival, influenced by time, temperature, daylight, and habitat.
• Utilized laboratory data and Greater Toronto Area mosquito trap data for parameterization, incorporating precipitation and water-body data as habitat proxies.

Main Results:

• The mechanistic model successfully reproduces mosquito population dynamics.
• The model accounts for nonlinear interactions between temperature and aquatic habitat availability.
• The developed model demonstrates superior prediction of yearly mosquito population variations compared to a statistical model.

Conclusions:

• This improved mosquito abundance modeling can inform targeted interventions for disease control.
• The model provides a valuable tool for mitigating mosquito-borne diseases, such as West Nile virus.
• Accurate population dynamics modeling is essential for effective public health strategies against vector-borne illnesses.