Project Description

Outbreaks of malaria are often linked to climate variations and land use changes that affect the life cycles of the mosquito vector and the Plasmodium parasite. Early warning of the timing and locations of epidemics can facilitate more effective targeting of resources for disease prevention, control, and treatment. Our malaria research involves developing novel informatics systems to acquire and harmonize remotely-sensed environmental data and epidemiological surveillance data. We analyze these data to identify the environmental triggers of malaria outbreaks and determine the best forecasting models for predicting epidemics. The results have been applied to create the Epidemic Prognosis Incorporating Disease and Environmental Monitoring for Integrated Assessment (EPIDEMIA) system, which supports malaria forecasting in epidemic-prone regions of the Ethiopian highlands. The system incorporates a set of linked software components that automate data access and harmonization, implement predictive modeling of malaria outbreaks, and generate charts and maps to communicate the predictions.

EPIDEMIA system flowchart
Conceptual diagram of information flow through the EPIDEMIA system (Merkord et al. 2017).

EPIDEMIA is being developed and tested by an interdisciplinary team that includes scientists from the University of Oklahoma and South Dakota State University along with partners from public health agencies, non-governmental organizations, and universities in Ethiopia. Weekly malaria surveillance data is provided by the Amhara Regional Health Bureau (ARHB) , and project scientists generate weekly forecasting reports that are shared with the ARHB and other public health organizaitons in Ethiopia. The software for operational data aquisition, processing and modeling is implemented using the R language and environment and Google Earth Engine, and we are in the process of transferring these tools to our partners in Ethiopia. This research is funded by a grant from the National Institutes of Health, National Institute of Allergy and Infectious Diseases (R01AI079411). Development of software for acquisition and processing of earth observation data has also been supported by NASA (NNX11AF67G). Additional information is available at

EPIDEMIA forecast graph
Malaria forecast for Abargelie Woreda, Waghimira Zone, for week 39 of 2016 (Merkord et al. 2017).

Partner Organizations


  • Merkord, C. L., Y. Liu, A. Mihretie, T. Gebrehiwot, W. Awoke, E. Bayabil, G. M. Henebry, G. T. Kassa, M. Lake, and M. C. Wimberly. 2017. Integrating malaria surveillance with climate data for outbreak detection and forecasting: the EPIDEMIA system. Malaria Journal 16:89.
  • Alemu, H. T., A. T. Kaptue, B. G. Senay, M. C. Wimberly, and G. M. Henebry. 2015. Evapotranspiration in the Nile Basin: Identifying dynamics and drivers, (2002-2011). Water 7: 4914-4931.
  • Liu, Y., J. Hu, I. Snell-Feikema, M. S. VanBemmel, A. Lamsal, M. C. Wimberly. 2015. Software to facilitate remote sensing data access for disease early warning systems. Environmental Modelling and Software 74: 238-246.
  • Midekisa A., B. Beyene, A. Mihretie, E. Bayabil, M. C. Wimberly. 2015. Seasonal associations of climatic drivers and malaria in the highlands of Ethiopia. Parasites & Vectors 8: 339.
  • Midekisa, A., G. B. Senay, and M. C. Wimberly. 2014. Multi-sensor Earth Observations to Characterize Wetlands and Malaria Epidemiology in Ethiopia. Water Resources Research 50: 8791-8806.
  • Wimberly, M. C., and A. Midekisa. 2014. Hydro-epidemiology of the Nile Basin: Understanding the complex linkages between water and infectious diseases. Pages 219-236 In: A. M. Melesse, W. Abtew, and S. G. Setegn, editors. Nile River Basin: Ecohydrological Challenges, Climate Change and Hydropolitics. Springer, New York.
  • Midekisa, A., G. Senay, G. M. Henebry, P. Semuniguse, and M. C. Wimberly. 2012. Remote sensing-based time series models for malaria early warning in the highlands of Ethiopia. Malaria Journal 11: 165.
  • Wimberly, M. C., A. Midekisa, P. Semuniguse, H. Teka, G. M. Henebry, T. Chuang, and G. B. Senay. 2012. Spatial synchrony of malaria outbreaks in a highland region of Ethiopia. Tropical Medicine & International Health 17: 1192-1201.