Abstract
BackgroundModels of the effects of environmental factors on West Nile virus disease risk have yielded conflicting outcomes. The role of precipitation has been especially difficult to discern from existing studies, due in part to habitat and behavior characteristics of specific vector species and because of differences in the temporal and spatial scales of the published studies. We used spatial and statistical modeling techniques to analyze and forecast fine scale spatial (2000 m grid) and temporal (weekly) patterns of West Nile virus mosquito infection relative to changing weather conditions in the urban landscape of the greater Chicago, Illinois, region for the years from 2004 to 2008.ResultsIncreased air temperature was the strongest temporal predictor of increased infection in Culex pipiens and Culex restuans mosquitoes, with cumulative high temperature differences being a key factor distinguishing years with higher mosquito infection and higher human illness rates from those with lower rates. Drier conditions in the spring followed by wetter conditions just prior to an increase in infection were factors in some but not all years. Overall, 80% of the weekly variation in mosquito infection was explained by prior weather conditions. Spatially, lower precipitation was the most important variable predicting stronger mosquito infection; precipitation and temperature alone could explain the pattern of spatial variability better than could other environmental variables (79% explained in the best model). Variables related to impervious surfaces and elevation differences were of modest importance in the spatial model.ConclusionFinely grained temporal and spatial patterns of precipitation and air temperature have a consistent and significant impact on the timing and location of increased mosquito infection in the northeastern Illinois study area. The use of local weather data at multiple monitoring locations and the integration of mosquito infection data from numerous sources across several years are important to the strength of the models presented. The other spatial environmental factors that tended to be important, including impervious surfaces and elevation measures, would mediate the effect of rainfall on soils and in urban catch basins. Changes in weather patterns with global climate change make it especially important to improve our ability to predict how inter-related local weather and environmental factors affect vectors and vector-borne disease risk.Local impact of temperature and precipitation on West Nile virus infection in Culex species mosquitoes in northeast Illinois, USA.
Highlights
Models of the effects of environmental factors on West Nile virus disease risk have yielded conflicting outcomes
Our three research questions are: 1) Interannually: what are the conditions associated with higher mosquito infection in some years compared to others? 2) Intra-annually: what temporal characteristics of rainfall and temperature precede changes in mosquito infection and with what temporal lag? 3) Spatially: can the patterns of rainfall and temperature help explain the differences in mosquito infection across space? We used surveillance data from the Illinois Department of Public Health (IDPH) and publicly available meteorological readings, and consider the heterogeneity of urban land cover through an analysis of digital spatial data to identify and forecast favorable conditions for West Nile virus (WNv) amplification in the greater Chicago area
Spatial patterns of Minimum Infection Rate (MIR) We found that random forests (RF) (Table 3 & Figure 6) in general outperformed regression trees (RT) (Table 4)
Summary
Models of the effects of environmental factors on West Nile virus disease risk have yielded conflicting outcomes. That several years have passed since the introduction of the WNv in North America, longitudinal data from testing of mosquitoes and host species (including records of human and equine illness) reported through systematic surveillance are available for development of models of the risk of infection. These records can be used to examine differences in infection between and within years and among locations to better understand the risk of transmission of the virus and to predict the possibility of place and time-specific outbreaks
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