Accounting for spatial variation in climatic factors predicts spatial variations in mosquito abundance in the desert southwest.

Background: Mosquitoes are vectors for diseases globally, making development of models that better explain mosquito abundances imperative. Mosquito population dynamics are particularly sensitive to local weather conditions, and mosquito-borne disease outbreaks can be spatially concentrated. There is a need for improved modeling studies to address whether spatial variation in disease outbreaks is driven by spatial variation in weather conditions, especially in dry and hot environments. In this study, we build a climate-driven model of mosquito population dynamics and compare whether predictions of mosquito abundance at the county scale are improved by accounting for sub-county climate variation. Methods: Using a 5-year time series of weekly mosquito abundance data collected for each zip code in Maricopa County, USA, we assess how local variation in climate can explain and predict mosquito population dynamics. We built a mechanistic model of mosquito population dynamics influenced by daily temperature and 30-day accumulated precipitation. We grouped zip codes based on similar patterns of temperature and precipitation using functional clustering. We compared two approaches: one using county-level average climate and another using data from the identified climate clusters. We use MCMC to fit the mechanistic model using averaged climate data in each cluster, then compare the modeling fit to observed data of the county-level model to the model based on climate-based clusters. Results: Simple, climate-forced modeling accurately estimates detailed mosquito abundance trajectories throughout a five-year period. Modeling mosquito abundances in the sub-county spatial clusters demonstrated that the same effects of temperature and precipitation on population growth rates could explain small-scale changes in mosquito populations. However, when we aggregate the sub-county model fits to the county-scale, the resulting fits are more precise but are sometimes overly confident, leading to lower overall accuracy and predictive performance. Conclusions: Our study demonstrates the importance of collecting fine-scale mosquito abundance data to improve our understanding and the predictability of mosquito population dynamics. The strong performance of both the cluster-based and county-level models illustrates the value of spatially sensitive modeling in this application. We anticipate that such modeling efforts will also aid in using weather forecasts to predict mosquito populations, aiding in efforts to control the spread of infectious disease.