The Arbovirus Mapping and Prediction (ArboMAP) system for West Nile virus forecasting

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.

Keywords: mosquito; outbreak; software; surveillance; weather.

Figure 1.

User-centered diagram showing the workflow for WNV forecasting. Step 1: Acquire updated entomological surveillance data, Step 2: Use the GEE app to update meteorological data, and Step 3: Use the RStudio GUI to generate a report. The system diagram on the bottom shows the high-level processes for modeling and forecasting.

Figure 2.

Examples of charts from an ArboMAP report for 2021 week 26 in South Dakota. (A) The relative risk of a county having at least one positive human West Nile virus. (B) The modeled epidemiological curve for the current year, including backcasts (historical predictions prior to the current week) and forecasts (future predictions after the current week). (C) Modeled epidemiological curves for all years, including fitted values in historical years (2004-2020) and forecasts for the current year (2021). (D) The weekly proportions of counties with at least one human case from historical years. (E) Daily temperatures in the current year compared to historical averages.