J2.3 Design and Implementation of Integrated Surveillance and Modeling Systems for Climate-Sensitive Diseases (Invited Presentation)

Monday, 23 January 2017: 4:30 PM
Conference Center: Tahoma 5 (Washington State Convention Center )
Michael C. Wimberly, South Dakota State Univ., Brookings, SD; and J. K. Davis, C. L. Merkord, Y. Liu, G. M. Henebry, and M. B. Hildreth

Climatic variations have a multitude of effects on human health, ranging from the direct impacts of extreme heat events to indirect effects on the vectors and hosts that transmit infectious diseases. Disease surveillance has traditionally focused on monitoring human cases, and in some instances tracking populations sizes and infection rates of arthropod vectors and zoonotic hosts. For climate-sensitive diseases, there is a potential to strengthen surveillance and obtain early indicators of future outbreaks by monitoring environmental risk factors using broad-scale sensor networks that include earth-observing satellites as well as ground stations. We highlight the opportunities and challenges of this integration by presenting modeling results and discussing lessons learned from two projects focused on surveillance and forecasting of mosquito-borne diseases. The Epidemic Prognosis Incorporating Disease and Environmental Monitoring for Integrated Assessement (EPIDEMIA) project integrates malaria case surveillance with remotely-sensed environmental data for early detection of malaria epidemics in the Amhara region of Ethiopia and has been producing weekly forecast reports since 2015. The South Dakota Mosquito Information System (SDMIS) project similarly combines entomological surveillance with environmental monitoring to generate weekly maps for West Nile virus (WNV) in the north-central United States. We are currently implementing a new disease forecasting and risk reporting framework for the state of South Dakota during the 2016 WNV transmission season. Both of these efforts utilize a variety of NASA earth science datasets to monitor environmental variability and inform forecasting models. These data products include meterological fields from the North American Land Data Assimilation System (NLDAS); rainfall data from the Tropical Rainfall Measuring Mission (TRMM) and the Global Predipication Measurement (GPM) mission; and land surface temperature and surface relfectance products form the Moderate Resolution Imaging Spectrodariometer (MODIS). Despite important differences in disease ecology and geographic setting, our experiences with these two projects highlight several important lessons that can inform future efforts at disease early warning based on climatic predictors. These include the need to engage end users in system design from the outset, the critical role of automated workflows to facilitate the timely integration of multiple data streams, the importance of focused visualizations that synthesize modeling results, and the challenge of linking risk indicators and forecasts to specific public health responses.
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