A Process-based Model with Temperature, Water, and Lab-derived Data Improves Predictions of Daily Culex pipiens/restuans Mosquito Density

Abstract

While the number of human cases of mosquito-borne diseases has increased in North America in the last decade, accurate modeling of mosquito population density has remained a challenge. Longitudinal mosquito trap data over the many years needed for model calibration, and validation is relatively rare. In particular, capturing the relative changes in mosquito abundance across seasons is necessary for predicting the risk of disease spread as it varies from year to year. We developed a discrete, semi-stochastic, mechanistic process-based mosquito population model that captures life-cycle egg, larva, pupa, adult stages, and diapause for Culex pipiens (Diptera, Culicidae) and Culex restuans (Diptera, Culicidae) mosquito populations. This model combines known models for development and survival into a fully connected age-structured model that can reproduce mosquito population dynamics. Mosquito development through these stages is a function of time, temperature, daylight hours, and aquatic habitat availability. The time-dependent parameters are informed by both laboratory studies and mosquito trap data from the Greater Toronto Area. The model incorporates city-wide water-body gauge and precipitation data as a proxy for aquatic habitat. This approach accounts for the nonlinear interaction of temperature and aquatic habitat variability on the mosquito life stages. We demonstrate that the full model predicts the yearly variations in mosquito populations better than a statistical model using the same data sources. This improvement in modeling mosquito abundance can help guide interventions for reducing mosquito abundance in mitigating mosquito-borne diseases like West Nile virus.

Modelling the spatial and temporal distribution of tsetse flies for targeted control

“…Among the disease vectors, studies aiming to map and understand their spatial relative densities have focused mostly on mosquitoes and ticks. In addition to the commonly employed classical geostatistical models (Rosà et al, 2019;Talbot et al, 2019;Mudele et al, 2021;Shutt et al, 2022), these studies have increasingly turned to machine-learning technologies (González Jiménez et al, 2019;Joshi and Miller, 2021;Rahman et al, 2021;Schneider et al, 2022;Makridou et al, 2023). A significant advantage of machine learning over traditional geostatistical methods is its innate capacity to capture complex associations among variables, often resulting in higher predictive This study focuses on tsetse flies.…”