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• W3008378181 abstract "When pressed for time, outbreak investigators often use homogeneous mixing models to model infectious diseases in data-poor regions. But recent outbreaks such as the 2014 Ebola outbreak in West Africa have shown the limitations of this approach in an era of increasing urbanization and connectivity. Both outbreak detection and predictive modeling depend on realistic estimates of human and disease mobility, but these data are difficult to acquire in a timely manner. This is especially true when dealing with an emerging outbreak in an under-resourced nation. Weighted travel networks with realistic estimates for population flows are often proprietary, expensive, or nonexistent. Here we propose a method for rapidly generating a mobility model from open-source data. As an example, we use road and river network data, along with population estimates, to construct a realistic model of human movement between health zones in the Democratic Republic of the Congo (DRC). Using these mobility data, we then fit an epidemic model to real-world surveillance data from the recent Ebola outbreak in the Nord Kivu region of the DRC to illustrate a potential use of the generated mobility estimation. In addition to providing a way for rapid risk estimation, this approach brings together novel techniques to merge diverse GIS datasets that can then be used to address issues that pertain to public health and global health security. Outbreak detection and predictive modeling depend on realistic estimates of human and disease mobility, but these data are difficult to acquire in a timely manner, especially when dealing with an emerging outbreak in an under-resourced nation. The authors propose a method for rapidly generating a mobility model from open-source data." @default.
• W3008378181 created "2020-03-06" @default.
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• W3008378181 date "2020-02-01" @default.
• W3008378181 modified "2023-09-26" @default.
• W3008378181 title "Methods for Rapid Mobility Estimation to Support Outbreak Response" @default.
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• W3008378181 doi "https://doi.org/10.1089/hs.2019.0101" @default.
• W3008378181 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7047125" @default.
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• W3008378181 hasPublicationYear "2020" @default.
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