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Abstract ObjectivesWest Nile virus (WNV) is the most common mosquito-borne disease in the United States. Predicting the location and timing of outbreaks would allow targeting of disease prevention and mosquito control activities. Our objective was to develop software (ArboMAP) for routine WNV forecasting using public health surveillance data and meteorological observations. Materials and MethodsArboMAP was implemented using an R markdown script for data processing, modeling, and report generation. A Google Earth Engine application was developed to summarize and download weather data. Generalized additive models were used to make county-level predictions of WNV cases. ResultsArboMAP minimized the number of manual steps required to make weekly forecasts, generated information that was useful for decision-makers, and has been tested and implemented in multiple public health institutions. Discussion and ConclusionRoutine prediction of mosquito-borne disease risk is feasible and can be implemented by public health departments using ArboMAP.
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Meteorological data for public health surveillance
Meteorological data for public health surveillanceMichael Wimberly, Professor from the University of Oklahoma, walks us through integrating meteorological data for public health surveillance and disease forecasting. Public health surveillance involves the collection, analysis, interpretation, and dissemination of health-related data to plan, implement, and evaluate public health practices. The resulting information supports the detection of emerging health threats, planning interventions, and evaluating policies and programs to protect and improve population health.
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- Award ID(s):
- 2200299
- PAR ID:
- 10528302
- Publisher / Repository:
- Open Access Government
- Date Published:
- Journal Name:
- Open Access Government
- Edition / Version:
- 1
- Volume:
- 42
- Issue:
- 1
- ISSN:
- 2516-3817
- Page Range / eLocation ID:
- 38 to 39
- Subject(s) / Keyword(s):
- One Health disease surveillance
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.56367/OAG-042-10923
