ArticlePeerJ2017
A comparison of least squares regression and geographically weighted regression modeling of West Nile virus risk based on environmental parameters.
Article in PeerJ, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review on Modeling Methods and Influential Factors for Mapping Dengue-Related Risk in Urban Settings.International journal of environmental research and public health · 2022Pooled it
- The paradoxical impact of drought on West Nile virus risk: insights from long-term ecological data.Proceedings. Biological sciences · 2025Article
- Epidemiological characteristics and influencing factors of scrub typhus in Jiangxi Province.Parasites & vectors · 2025Article
- Spatial Distribution Characteristics and Driving Factors of Formicidae in Small Watersheds of Loess Hilly Regions.Insects · 2025Article
- Impact of climate change on the global circulation of West Nile virus and adaptation responses: a scoping review.Infectious diseases of poverty · 2024Article
- Spatial distribution of mortality from colorectal cancer in the southern region of Brazil.PloS one · 2023Article
- Impact of urbanisation and environmental factors on spatial distribution of COVID-19 cases during the early phase of epidemic in Singapore.Scientific reports · 2022Article
- GIS-based spatio-temporal analysis and modeling of COVID-19 incidence rates in Europe.Spatial and spatio-temporal epidemiology · 2022Article
- Spatial differentiation and determinants of COVID-19 in Indonesia.BMC public health · 2022Article
- Article
- Spatio-temporal analysis of association between incidence of malaria and environmental predictors of malaria transmission in Nigeria.Scientific reports · 2019Article
- Geostatistical predictive modeling for asthma and chronic obstructive pulmonary disease using socioeconomic and environmental determinants.Environmental monitoring and assessment · 2019Article
- Forecasting the West Nile Virus in the United States: An Extensive Novel Data Streams-Based Time Series Analysis and Structural Equation Modeling of Related Digital Searching Behavior.JMIR public health and surveillance · 2019Article
- Spatiotemporal Bayesian modeling of West Nile virus: Identifying risk of infection in mosquitoes with local-scale predictors.The Science of the total environment · 2019Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe primary aim of the study reported here was to determine the effectiveness of utilizing local spatial variations in environmental data to uncover the statistical relationships between West Nile Virus (WNV) risk and environmental factors. Because least squares regression methods do not account for spatial autocorrelation and non-stationarity of the type of spatial data analyzed for studies that explore the relationship between WNV and environmental determinants, we hypothesized that a geographically weighted regression model would help us better understand how environmental factors are related to WNV risk patterns without the confounding effects of spatial non-stationarity.
methodsWe examined commonly mapped environmental factors using both ordinary least squares regression (LSR) and geographically weighted regression (GWR). Both types of models were applied to examine the relationship between WNV-infected dead bird counts and various environmental factors for those locations. The goal was to determine which approach yielded a better predictive model.
resultsLSR efforts lead to identifying three environmental variables that were statistically significantly related to WNV infected dead birds (adjusted
conclusionsThe spatial granularity resulting from the geographically weighted approach provides a better understanding of how environmental spatial heterogeneity is related to WNV risk as implied by WNV infected dead birds, which should allow improved planning of public health management strategies.
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