ArticleGeoHealth2018
Identifying Environmental Risk Factors and Mapping the Distribution of West Nile Virus in an Endemic Region of North America.
Article in GeoHealth, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- West Nile Virus Transmission Suitability Modeling forGeoHealth · 2026Article
- Integrating Wind Speed Into Climate-Based West Nile Virus Models: A Comparative Analysis in Two Distinct Regions.GeoHealth · 2025Article
- Impacts of Urbanization and Habitat Characteristics on the Human Risk of West Nile Disease in the United States.Biology · 2025Article
- Multi-Model Prediction of West Nile Virus Neuroinvasive Disease With Machine Learning for Identification of Important Regional Climatic Drivers.GeoHealth · 2023Article
- Comparison of West Nile Virus Disease in Humans and Horses: Exploiting Similarities for Enhancing Syndromic Surveillance.Viruses · 2023Review
- Assessing the Influence of Climate on the Spatial Pattern of West Nile Virus Incidence in the United States.Environmental health perspectives · 2023Article
- Integrated Forecasts Based on Public Health Surveillance and Meteorological Data Predict West Nile Virus in a High-Risk Region of North America.Environmental health perspectives · 2022Article
- West Nile Virus Occurrence and Ecological Niche Modeling in Wild Bird Species and Mosquito Vectors: An Active Surveillance Program in the Peloponnese Region of Greece.Microorganisms · 2022Article
- Cloud-based applications for accessing satellite Earth observations to support malaria early warning.Scientific data · 2022Article
- A meteorological-based forecasting model for predicting minimal infection rates inCanada communicable disease report = Releve des maladies transmissibles au Canada · 2022Article
- Spatial patterns of West Nile virus distribution in the Volgograd region of Russia, a territory with long-existing foci.PLoS neglected tropical diseases · 2022Article
- Predictive model for microclimatic temperature and its use in mosquito population modeling.Scientific reports · 2021Article
- A proposed framework for the development and qualitative evaluation of West Nile virus models and their application to local public health decision-making.PLoS neglected tropical diseases · 2021Review
- Remote sensing of environmental risk factors for malaria in different geographic contexts.International journal of health geographics · 2021Article
- Toward an Integrated System of Climate Change and Human Health Indicators: A Conceptual Framework.Climatic change · 2021Article
- Shifting From Sectoral to Integrated Surveillance by Changing Collaborative Practices: Application to West Nile Virus Surveillance in a Small Island State of the Caribbean.Frontiers in public health · 2021Article
- Land cover affects microclimate and temperature suitability for arbovirus transmission in an urban landscape.PLoS neglected tropical diseases · 2020Article
- Exploring local and global regression models to estimate the spatial variability of Zika and Chikungunya cases in Recife, Brazil.Revista da Sociedade Brasileira de Medicina Tropical · 2020Article
- Review
- Identifying Environmental Risk Factors and Mapping the Distribution of West Nile Virus in an Endemic Region of North America.GeoHealth · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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