Association of West Nile virus illness and urban landscapes in Chicago and Detroit

BACKGROUND: West Nile virus infection in humans in urban areas of the Midwestern United States has exhibited strong spatial clustering during epidemic years. We derived urban landscape classes from the physical and socio-economic factors hypothesized to be associated with West Nile Virus (WNV) transmission and compared those to human cases of illness in 2002 in Chicago and Detroit. The objectives were to improve understanding of human exposure to virus-infected mosquitoes in the urban context, and to assess the degree to which environmental factors found to be important in Chicago were also found in Detroit. RESULTS: Five urban classes that partitioned the urban space were developed for each city region. The classes had many similarities in the two settings. In both regions, the WNV case rate was considerably higher in the urban class associated with the Inner Suburbs, where 1940–1960 era housing dominates, vegetation cover is moderate, and population density is moderate. The land cover mapping approach played an important role in the successful and consistent classification of the urban areas. CONCLUSION: The analysis demonstrates how urban form and past land use decisions can influence transmission of a vector-borne virus. In addition, the results are helpful to develop hypotheses regarding urban landscape features and WNV transmission, they provide a structured method to stratify the urban areas to locate representative field study sites specifically for WNV, and this analysis contributes to the question of how the urban environment affects human health

Local impact of temperature and precipitation on West Nile virus infection in Culex species mosquitoes in northeast Illinois, USA

<p>Abstract</p> <p>Background</p> <p>Models 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.</p> <p>Results</p> <p>Increased air temperature was the strongest temporal predictor of increased infection in <it>Culex pipiens </it>and <it>Culex restuans </it>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.</p> <p>Conclusion</p> <p>Finely 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.</p> <p>Local impact of temperature and precipitation on West Nile virus infection in <it>Culex </it>species mosquitoes in northeast Illinois, USA.</p

Predicting West Nile Virus Infection Risk From the Synergistic Effects of Rainfall and Temperature

Mosquito-based surveillance is a practical way to estimate the risk of transmission of West Nile virus (WNV) to people. Variations in temperature and precipitation play a role in driving mosquito infection rates and transmission of WNV, motivating efforts to predict infection rates based on prior weather conditions. Weather conditionsand sequential patterns of meteorological events can have particularly important, but regionally distinctive, consequences for WNV transmission, with high temperatures and low precipitation often increasing WNV mosquitoinfection. Predictive models that incorporate weather can thus be used to provide early indications of the risk of WNV infection. The purpose of this study was first, to assess the ability of a previously published model of WNV mosquito infection to predict infection for an area within the region for which it was developed, and second, to improve the predictive ability of this model by incorporating new weather factors that may affect mosquito development. The legacy model captured the primary trends in mosquito infection, but it was improved considerably when calibrated with local mosquito infection rates. The use of interaction terms between precipitationand temperature improved model performance. Specifically, temperature had a stronger influence than rainfall, so that lower than average temperature greatly reduced the effect of low rainfall on increased infectionrates. When rainfall was lower, high temperature had an even stronger positive impact on infection rates. The final model is practical, stable, and operationally valid for predicting West Nile virus infection rates in future weeks when calibrated with local data