Ensemble species distribution modeling of Culex tarsalis (Diptera: Culicidae) in the continental United States.

West Nile virus (WNV) is the primary mosquito-borne disease in the United States and has had case reports every year since its introduction in 1999. As such, it is critical that we characterize the distribution of WNV vectors. Estimates of Culex tarsalis Coquillett species distribution, a major WNV vector, are scarce. We used ensemble distribution modeling to estimate habitat suitability for this species across the contiguous United States by consolidating presence data from four publicly available mosquito trapping data servers. The central plains region and much of the western US were estimated to have high habitat suitability. We identified multiple metrics of temperature and precipitation to be important in predicting the occurrence of Cx. tarsalis in a given geographic area. Furthermore, we observed habitat suitability for Cx. tarsalis to be significantly higher in areas with a high incidence of West Nile neuroinvasive disease compared to areas with low WN disease incidence, suggesting that Cx. tarsalis is present in regions with a high incidence of disease.

Anopheles albimanus (Diptera: Culicidae) Ensemble Distribution Modeling: Applications for Malaria Elimination.

In the absence of entomological information, tools for predicting Anopheles spp. presence can help evaluate the entomological risk of malaria transmission. Here, we illustrate how species distribution models (SDM) could quantify potential dominant vector species presence in malaria elimination settings. We fitted a 250 m resolution ensemble SDM for Anopheles albimanus Wiedemann. The ensemble SDM included predictions based on seven different algorithms, 110 occurrence records and 70 model projections. SDM covariates included nine environmental variables that were selected based on their importance from an original set of 28 layers that included remotely and spatially interpolated locally measured variables for the land surface of Costa Rica. Goodness of fit for the ensemble SDM was very high, with a minimum AUC of 0.79. We used the resulting ensemble SDM to evaluate differences in habitat suitability (HS) between commercial plantations and surrounding landscapes, finding a higher HS in pineapple and oil palm plantations, suggestive of An. albimanus presence, than in surrounding landscapes. The ensemble SDM suggested a low HS for An. albimanus at the presumed epicenter of malaria transmission during 2018-2019 in Costa Rica, yet this vector was likely present at the two main towns also affected by the epidemic. Our results illustrate how ensemble SDMs in malaria elimination settings can provide information that could help to improve vector surveillance and control.

Meta-Analysis of the Relative Abundance of Nuisance and Vector Mosquitoes in Urban and Blue-Green Spaces.

Blue-green spaces (BGSs), urban areas characterized by the presence of vegetation and or water, and infrastructure form a potential solution for public health threats from increasing urbanization. We conducted a meta-analysis to test the hypothesis that blue-green spaces increase the abundance of nuisance and vector mosquito species compared to non-greened urban areas. After screening 7306 studies published since 1992, we identified 18 studies containing sufficient data from both traditional urban areas and BGSs. We found no significant difference in mean abundance of all mosquito taxa in three genera (Aedes, Culex, Anopheles) when comparing blue-green spaces and non-greened urban spaces. Similarly, a separate analysis of each individual genera found no significant differences. An analysis of the taxa by larval habitat guilds found no differences for container-breeding mosquitoes. Flood-water species tended to be more abundant in blue-green spaces, but the differences were not significant. The individual taxa of Aedes albopictus and the Culex pipiens complex showed no differences between blue-green and urban spaces, while the abundance of Aedes aegypti was significantly higher in traditional urban spaces. Due to the variety existing between and among the several types of blue-green spaces, further studies comparing each unique type of blue-green space or infrastructure will be necessary to draw conclusions regarding the influence of each structure on for urban mosquito communities.

A proposed framework for the development and qualitative evaluation of West Nile virus models and their application to local public health decision-making.

West Nile virus (WNV) is a globally distributed mosquito-borne virus of great public health concern. The number of WNV human cases and mosquito infection patterns vary in space and time. Many statistical models have been developed to understand and predict WNV geographic and temporal dynamics. However, these modeling efforts have been disjointed with little model comparison and inconsistent validation. In this paper, we describe a framework to unify and standardize WNV modeling efforts nationwide. WNV risk, detection, or warning models for this review were solicited from active research groups working in different regions of the United States. A total of 13 models were selected and described. The spatial and temporal scales of each model were compared to guide the timing and the locations for mosquito and virus surveillance, to support mosquito vector control decisions, and to assist in conducting public health outreach campaigns at multiple scales of decision-making. Our overarching goal is to bridge the existing gap between model development, which is usually conducted as an academic exercise, and practical model applications, which occur at state, tribal, local, or territorial public health and mosquito control agency levels. The proposed model assessment and comparison framework helps clarify the value of individual models for decision-making and identifies the appropriate temporal and spatial scope of each model. This qualitative evaluation clearly identifies gaps in linking models to applied decisions and sets the stage for a quantitative comparison of models. Specifically, whereas many coarse-grained models (county resolution or greater) have been developed, the greatest need is for fine-grained, short-term planning models (m-km, days-weeks) that remain scarce. We further recommend quantifying the value of information for each decision to identify decisions that would benefit most from model input.