H23F-0936:
Design of Epidemia – an Ecohealth Informatics System for Integrated Forecasting of Malaria Epidemics
Tuesday, 16 December 2014
Michael C Wimberly1, Estifanos Bayabil2, Belay Beyane3, Mekonnen Bishaw4, Geoffrey M Henebry1, Alemayehu Lemma2, Yi Liu1, Christopher L. Merkord1, Abere Mihretie2, Gabriel B Senay5 and Worku Yalew6, (1)South Dakota State University, Brookings, SD, United States, (2)Health, Development, and Anti-Malaria Association, Addis Ababa, Ethiopia, (3)Amhara Regional Health Bureau, Bahir Dar, Ethiopia, (4)GAMBY College of Medical Science, Bahir Dar, Ethiopia, (5)USGS EROS, Sioux Falls, SD, United States, (6)Bahir Dar University, Bahir Dar, Ethiopia
Abstract:
Early warning of the timing and locations of malaria epidemics can facilitate the targeting of resources for prevention and emergency response. In response to this need, we are developing the Epidemic Prognosis Incorporating Disease and Environmental Monitoring for Integrated Assessment (EPIDEMIA) computer system. The system incorporates software for capturing, processing, and integrating environmental and epidemiological data from multiple sources; data assimilation techniques that continually update models and forecasts; and a web-based interface that makes the resulting information available to public health decision makers. This technology will enable forecasts based on lagged responses to environmental risk factors as well as information about recent trends in malaria cases. Environmental driving variables will include a variety of remote-sensed hydrological indicators. EPIDEMIA will be implemented and tested in the Amhara Region of Ethiopia in collaboration with local stakeholders. We conducted an initial co-design workshop in July 2014 that included environmental scientists, software engineers, and participants from the NGO, academic, and public health sectors in Ethiopia. A prototype of the EPIDEMIA web interface was presented and a requirements analysis was conducted to characterize the main use cases for the public health community, identify the critical data requirements for malaria risk modeling, and develop of a list of baseline features for the public health interface. Several critical system features were identified, including a secure web-based interface for uploading and validating surveillance data; a flexible query system to allow retrieval of environmental and epidemiological data summaries as tables, charts, and maps; and an alert system to provide automatic warnings in response to environmental and epidemiological risk factors for malaria. Future system development will involve a cycle of implementation, training, usability testing, and upgrading. This innovative translational bioinformatics approach will allow us to assess the practical effectiveness of these tools as we continually improve the technologies.
