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The Arbovirus Mapping and Prediction (ArboMAP) system for West Nile virus forecasting
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Journal Article
The Arbovirus Mapping and Prediction (ArboMAP) system for West Nile virus forecasting
2024
Overview
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.
Lay Summary
West Nile virus (WNV) is the most common mosquito-borne disease in the United States. To reduce the risk of WNV, public health agencies distribute information about how to avoid mosquito bites and use insecticides to reduce the abundances of disease-transmitting mosquitoes. Information about when and where the risk of getting WNV is highest would help these agencies to target their activities and use limited resources more efficiently. To support this goal, we developed the ArboMAP software system for predicting the risk of WNV disease in humans. ArboMAP uses information about recent weather combined with data obtained from trapping mosquitoes and testing them for presence of WNV to predict how many human cases will occur in future weeks. Predictions extend throughout the current WNV season (typically May-September) and are made for each county within a state. The system is implemented as a set of free software tools that can be used by epidemiologists in state and municipal departments of health. Feedback from public health agencies in South Dakota, Louisiana, Oklahoma, and Michigan has been incorporated to enhance the usability of the system and design visualizations that summarize the forecasts.
Publisher
Oxford University Press
