Nekorchuk DM, Bharadwaja A, Simonson S, Ortega E, França CMB, Dinh E, Reik R, Burkholder R, Wimberly MC. The Arbovirus Mapping and Prediction (ArboMAP) system for West Nile virus forecasting.
JAMIA Open 2024;
7:ooad110. [PMID:
38186743 PMCID:
PMC10766066 DOI:
10.1093/jamiaopen/ooad110]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Revised: 12/04/2023] [Accepted: 12/20/2023] [Indexed: 01/09/2024] Open
Abstract
Objectives
West 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 Methods
ArboMAP 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.
Results
ArboMAP 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 Conclusion
Routine prediction of mosquito-borne disease risk is feasible and can be implemented by public health departments using ArboMAP.
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