Malaria Forecasting in the Amhara Region of Ethiopia: Development of an R & Google Earth Engine-based System to Bridge the Research to Local Operations Gap

Authors: Dawn M Nekorchuk*, University of Oklahoma, Abere Mihretie, Health, Development, and Anti-Malaria Association, Addis Ababa, Ethiopia, Teklehaymanot Gebrehiwot, Amhara Public Health Institute, Bahir Dar, Ethiopia , Justin K Davis, University of Oklahoma, Andrea Hess, University of Oklahoma, Aklilu Getinet, Health, Development, and Anti-Malaria Association, Addis Ababa, Ethiopia, Worku Awoke, Bahir Dar University, Bahir Dar, Ethiopia, Michael C Wimberly, University of Oklahoma
Topics: Medical and Health Geography, Applied Geography, Africa
Keywords: Disease forecasting, Early detection, Early warning, Geohealth, Infectious disease modeling, Malaria, R2O, Research to Operations
Session Type: Poster
Day: 4/4/2019
Start / End Time: 3:05 PM / 4:45 PM
Room: Lincoln 2, Marriott, Exhibition Level
Presentation File: Download

The Epidemic Prognosis Incorporating Disease and Environmental Monitoring for Integrated Assessment (EPIDEMIA) project produced operational malaria forecasts for the Amhara region of Ethiopia. Public health monitoring of environmentally-mediated diseases can benefit from incorporating information on related environmental factors, and this was the approach used to integrate local malaria surveillance data and remotely-sensed environmental predictors. This melding of data can improve the ability to detect early indication of outbreaks, allowing for more efficient and proactive public health interventions.

Our local public health partners expressed interest in being self-sufficient in creating the weekly reports themselves. The EPIDEMIA modeling of disease transmission for early detection and early warning evaluation was done in R, a free software for statistical computing. We developed the “epidemiar” R package to provide a generalized set of functions for disease forecasting. The package is flexible enough to forecast a wide range of infectious diseases that are affected by environmental conditions.

In addition, we designed workflows and wrote customized code for our Ethiopian colleagues, including a Google Earth Engine script to capture the necessary summaries of the environmental variables. The output of the modeling and forecasting is fed into formatting documents to create distributable reports with maps and graphs of the results.

This new epidemiar R package facilitates the combination of earth science data with public health surveillance to support early detection and early warning of disease transmission events for infectious diseases with environmental drivers. The results can be used to help support operational public health monitoring and interventions.

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