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• W2020898181 abstract "Spatial epidemiological tools are increasingly being applied to emerging viral zoonoses (EVZ), partly because of improving analytical methods and technologies for data capture and management, and partly because the demand is growing for more objective ways of allocating limited resources in the face of the emerging threat posed by these diseases. This review documents applications of geographical information systems (GIS), remote sensing (RS) and spatially-explicit statistical and mathematical models to epidemiological studies of EVZ. Landscape epidemiology uses statistical associations between environmental variables and diseases to study and predict their spatial distributions. Phylogeography augments epidemiological knowledge by studying the evolution of viral genetics through space and time. Cluster detection and early warning systems assist surveillance and can permit timely interventions. Advanced statistical models can accommodate spatial dependence present in epidemiological datasets and can permit assessment of uncertainties in disease data and predictions. Mathematical models are particularly useful for testing and comparing alternative control strategies, whereas spatial decision-support systems integrate a variety of spatial epidemiological tools to facilitate widespread dissemination and interpretation of disease data. Improved spatial data collection systems and greater practical application of spatial epidemiological tools should be applied in real-world scenarios." @default.
• W2020898181 created "2016-06-24" @default.
• W2020898181 creator A5016266491 @default.
• W2020898181 creator A5081115982 @default.
• W2020898181 date "2009-10-01" @default.
• W2020898181 modified "2023-09-28" @default.
• W2020898181 title "Emerging viral zoonoses: Frameworks for spatial and spatiotemporal risk assessment and resource planning" @default.
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• W2020898181 doi "https://doi.org/10.1016/j.tvjl.2008.05.010" @default.
• W2020898181 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7110545" @default.
• W2020898181 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/18718800" @default.
• W2020898181 hasPublicationYear "2009" @default.
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