In this renewal application, we will expand upon our ongoing research in which we successfully used satellite remote sensing to develop environmental models for mapping and forecasting mosquito-borne disease risk. As in the current project, our overarching goal is to reduce the gap between earth observation technologies and the biomedical fields by developing novel bioinformatics techniques and tools. The specific objective of our new project is to extend our work on the environmental modeling and forecasting of malaria risk by directly linking our existing environmental monitoring system with public health surveillance. Our overarching hypothesis is that an integrated approach to disease forecasting that combines elements of early warning (based on environmental risk factors) and early detection (based on patterns of disease cases) will be more effective than either approach alone, resulting in more accurate predictions that are useful to public health decision makers.. For this renewal we have chosen to focus on the Ethiopian Highlands because we believe that this approach to disease forecasting has the potential to make a particularly significant impact on public health decision-making in this region. We will address the following specific aims: SA1: Develop a web-based computer system for malaria epidemic forecasting that integrates epidemiological surveillance of malaria cases with environmental monitoring data from earth observing satellites;SA2: Test novel modeling and decision support approaches that combine multiple risk indicators based on seasonal climatic anomalies, lagged responses to environmental variability, and early detection of malaria epidemics;SA3: Develop and test interfaces for uploading surveillance data and visualizing malaria risk indicators in low-bandwidth environments. We will achieve these aims by developing the Epidemic Prognosis Incorporating Disease and Environmental Monitoring for Integrated Assessment (EPIDEMIA) computer system. This system will link our existing EASTWeb software for environmental monitoring with new subsystems for automated submission, processing, and summarization of malaria surveillance data. Novel algorithms will be developed and tested to generate forecasts using a combination of environmental risk factors and trends in malaria indicators, and a public health interface will be created to facilitate data acquisition from and dissemination of results to end users in low bandwidth environments. The EPIDEMIA system will be implemented and tested at a network of sentinel sites in the Amhara region of Ethiopia through our existing partnerships with the regional health bureau and a local NGO. We expect that the new tools and scientific knowledge gained through this research will ultimately be extendible to other regions to improve forecasting of future malaria epidemics support efforts toward malaria prevention, control, and ultimately elimination.

Public Health Relevance

Malaria is one of the most widespread infectious diseases in the world and is a major public health problem throughout sub-Saharan Africa. The development of early detection and early warning systems can help reduce the burden of this disease by predicting the timing and locations of epidemics and allowing public health practitioners to more effectively target resources for malaria prevention, control, and treatment. In particular, novel health informatics tools can help to achieve this goal by facilitating the efficient collection and integration of malaria surveillance and environmental monitoring data.

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
Research Project (R01)
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Biomedical Computing and Health Informatics Study Section (BCHI)
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Gezmu, Misrak
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South Dakota State University
Other Domestic Higher Education
United States
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Merkord, Christopher L; Liu, Yi; Mihretie, Abere et al. (2017) Integrating malaria surveillance with climate data for outbreak detection and forecasting: the EPIDEMIA system. Malar J 16:89
Liu, Yi; Hu, Jiameng; Snell-Feikema, Isaiah et al. (2015) Software to Facilitate Remote Sensing Data Access for Disease Early Warning Systems. Environ Model Softw 74:247-257
Midekisa, Alemayehu; Beyene, Belay; Mihretie, Abere et al. (2015) Seasonal associations of climatic drivers and malaria in the highlands of Ethiopia. Parasit Vectors 8:339
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Wimberly, Michael C; Midekisa, Alemayehu; Semuniguse, Paulos et al. (2012) Spatial synchrony of malaria outbreaks in a highland region of Ethiopia. Trop Med Int Health 17:1192-201
Midekisa, Alemayehu; Senay, Gabriel; Henebry, Geoffrey M et al. (2012) Remote sensing-based time series models for malaria early warning in the highlands of Ethiopia. Malar J 11:165
Chuang, Ting-Wu; Wimberly, Michael C (2012) Remote sensing of climatic anomalies and West Nile virus incidence in the northern Great Plains of the United States. PLoS One 7:e46882
Chuang, Ting-Wu; Hockett, Christine W; Kightlinger, Lon et al. (2012) Landscape-level spatial patterns of West Nile virus risk in the northern Great Plains. Am J Trop Med Hyg 86:724-31

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