A Process-based Model with Temperature, Water, and Lab-derived Data Improves Predictions of Daily Culex pipiens/restuans Mosquito Density. Issue 6 (7th October 2022)

• Record Type: Journal Article
• Authors: Shutt, D P; Goodsman, D W; Martinez, K; Hemez, Z J L; Conrad, J R; Xu, C; Osthus, D; Russell, C; Hyman, J M; Manore, C A
• Editors: Gaff, Holly
• Is Part Of: Journal of medical entomology. Volume 59:Issue 6(2022)
• Page Start: 1947
• Page End: 1959
• Publication Date: 2022-10-07
• Subjects: Culex pipiens -- mosquito -- population dynamics -- temperature -- rainfall; Insects as carriers of disease -- Periodicals; 616.968
• DOI: 10.1093/jme/tjac127
• Languages: English
• ISSNs: 0022-2585

Abstract

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.