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. 2022 Nov 16;59(6):1947-1959.
doi: 10.1093/jme/tjac127.

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

D P Shutt  1 , D W Goodsman  2   3 , K Martinez  1 , Z J L Hemez  4 , J R Conrad  1   5 , C Xu  2 , D Osthus  6 , C Russell  7 , J M Hyman  8 , C A Manore  2
Affiliations

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

D P Shutt et al. J Med Entomol. .

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.

Keywords: Culex pipiens; mosquito; population dynamics; rainfall; temperature.

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Figures

Fig. 1.
Fig. 1.
Schematic representation of the Process Based Mosquito Model. Environmental mortality, dependent on water availability and/or temperature is applied across the age distribution prior to shifting the age of each surviving member based on the temperature dependent developmental velocity. Once an individual is 100% developed, they transition to the next stage, and if a non-diapausing adult, lay eggs.
Fig. 2.
Fig. 2.
Mosquito trap locations shown on a map of GTA Map (black). Hydrometric stations used for stage gauge water level data are shown in orange.
Fig. 3.
Fig. 3.
Functional fits of temperature dependent life history traits for Culex pipiens/restuans mosquitoes from which parameters in Table 1 were obtained. Data gathered from (Madder et al. 1983; Ciota et al. 2014). Each data were fit to a linear, Erying, and Briere model, and the model with the lowest AIC was selected for use in the process based model. The values used in these fits can be found in Supp Tables S1 and S2 (online only).
Fig. 4.
Fig. 4.
Mosquito abundance predictions from our process-based model using water stage gauge (orange) and precipitation (blue) data overlaid on observed mosquito trap averages (black dots) in the GTA from 2004 through 2017. The vertical red line (y = 3,300 day-steps equivalently January of 2013) indicates the separation between the training data used to fit parameters and the withheld testing data.
Fig. 5.
Fig. 5.
Mosquito abundance predictions from a Generalized Linear Model using water stage gauge (orange) and precipitation (blue) data overlaid on observed mosquito trap averages (black dots) in the GTA from 2004 through 2017. The vertical red line (y = 3,300 day-steps equivalently January of 2013) indicates the separation between the training data used to fit parameters and the withheld testing data.
Fig. 6.
Fig. 6.
Mosquito abundance predictions from our process-based model using water stage gauge (orange) and precipitation (blue) data for all modeled life stages. 1) Active mosquitoes which is again overlaid on observed mosquito trap averages (black dots) in the GTA from 2004 through 2017 as this was the population fit to data, 2) eggs, 3) larvae/pupae, and 4) total mosquitoes.
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