Applying the Global Macroscope for Ecological Forecasting of Vector-Borne Disease Outbreaks

Michael Wimberly, South Dakota State University, michael.wimberly@sdstate.edu (Presenter)
Ting-Wu Chuang, Taipei Medical University, chtingwu@tmu.edu.tw
Geoffrey Henebry, South Dakota State University, geoffrey.henebry@sdstate.edu
Michael Hildreth, South Dakota State University, michael.hildreth@sdstate.edu
John S Kimball, University of Montana, johnk@ntsg.umt.edu
Yi Liu, South Dakota State University, yi.liu@sdstate.edu
Alemayehu Midekisa, University of California San Francisco, alemayehu.midekisa@sdstate.edu
Gabriel Senay, USGS EROS Data Center, senay@usgs.gov

Vector-borne pathogens are often linked to environmental fluctuations that affect habitat suitability for their arthropod vectors and zoonotic reservoir hosts. Satellite remote sensing can provide consistent monitoring of many of these environmental variables at landscape to global scales, and thus can be applied to develop ecological forecasting models that can potentially provide early warning of environmental conditions that are likely to trigger a disease outbreak. We have conducted research on ecological modeling and forecasting of two highly seasonal mosquito-borne diseases: West Nile virus (WNV) in the northern Great Plains North America and epidemic malaria in the highlands of Ethiopia. Various approaches were used to model temporal variability human disease cases. In both study areas, disease cases were found to exhibit lagged responses to remotely-sensed indices of temperature and moisture at lags of 1-12 weeks. For WNV in the northern Great Plains, warm temperature anomalies during the preceding winter were also an important predictor of the number of malaria cases during the summer transmission season. In the highlands of Ethiopia, a wetlands map derived from Landsat and Shuttle Radar Topographic Mission (SRTM) data was found to be a strong predictor of the regional geographic pattern of malaria incidence. We have also used state space models to carry out time series forecasting of malaria in a data assimilation framework in which predictions are continually updated and evaluated using near-real-time streams of remote sensing data and disease surveillance data. These forecasts are currently being implemented in the Amhara region of Ethiopia through the development of the Epidemic Prognosis Incorporating Disease and Environmental Monitoring for Integrated Assessment (EPIDEMIA) computer system. These results highlight the potential for using remotely-sensed environmental data to study vector-borne disease dynamics as well as the prospects for applying these tools to conduct operational forecasting.

Presentation Type:  Poster

Session:  General Contributions   (Tue 4:35 PM)

Associated Project(s): 

• Wimberly, Mike: TE: Integrating Multi-Sensor Satellite Data for Malaria Early Warning in the Amhara Region of Ethiopia ...details
• Related Activity: Related Activity or Previously Funded CC&E Activity not listed ...details

Poster Location ID: 259