Identifying Environmental Risk Factors and Mapping the Distribution of West Nile Virus in an Endemic Region of North America

• Published in: Geohealth Vol. 2; no. 12; pp. 395 - 409
• Main Authors: Hess, A., Davis, J. K., Wimberly, M. C.
• Format: Journal Article
• Language: English
• Published: United States John Wiley and Sons Inc 01.12.2018; American Geophysical Union (AGU)
• Subjects: Atmospheric Processes; Biogeosciences; climate; Climate and Interannual Variability; Climate Change and Variability; Climate Variability; Climatology; Decadal Ocean Variability; disease map; Geohealth; Global Change; Hydrology; land cover; mosquito‐borne disease; Oceanography: General; Oceanography: Physical; Oceans; Public Health; Remote Sensing; Soil Moisture; spatial model; Vector Born Diseases; remote sensing; climate; disease map; spatial model; mosquito‐borne disease; land cover
• ISSN: 2471-1403; 2471-1403
• DOI: 10.1029/2018GH000161

Understanding the geographic distribution of mosquito‐borne disease and mapping disease risk are important for prevention and control efforts. Mosquito‐borne viruses (arboviruses), such as West Nile virus (WNV), are highly dependent on environmental conditions. Therefore, the use of environmental data can help in making spatial predictions of disease distribution. We used geocoded human case data for 2004–2017 and population‐weighted control points in combination with multiple geospatial environmental data sets to assess the environmental drivers of WNV cases and to map relative infection risk in South Dakota, USA. We compared the effectiveness of (1) land cover and physiography data, (2) climate data, and (3) spectral data for mapping the risk of WNV in South Dakota. A final model combining all data sets was used to predict spatial patterns of disease transmission and characterize the associations between environmental factors and WNV risk. We used a boosted regression tree model to identify the most important variables driving WNV risk and generated risk maps by applying this model across the entire state. We found that combining multiple sources of environmental data resulted in the most accurate predictions. Elevation, late‐season humidity, and early‐season surface moisture were the most important predictors of disease distribution. Indices that quantified interannual variability of climatic conditions and land surface moisture were better predictors than interannual means. We suggest that combining measures of interannual environmental variability with static land cover and physiography variables can help to improve spatial predictions of arbovirus transmission risk. Plain Language Summary West Nile virus, a disease transmitted by mosquitoes, is often considered a tropical disease. However, within the United States, the most cases per capita are reported in the northern Great Plains, with South Dakota having the highest rate. In this study, we combined human case data with satellite‐based Earth observations and other environmental data to highlight the places in South Dakota with high West Nile virus risk and identify their environmental characteristics. Areas in northeastern South Dakota, specifically in the northern James River Valley, had the highest West Nile virus risk. These high‐risk zones were associated with low‐lying pasturelands and poorly drained soils in areas with high year‐to‐year variability in climate and land surface moisture. Key Points Integrating multiple types of geospatial data improves risk maps for West Nile virus transmission Elevation, land cover, soils, summer climate, and remotely sensed spring land surface moisture were associated with West Nile virus cases Indices measuring interannual variability of climate and land surface moisture were important predictors of West Nile virus risk