72 - NASA Data Supports Longterm Study of Rift Valley fever

Since the early 1980s NASA Earth Observations of the environment initiated and continue to play an ever-increasing role in the monitoring of conditions associated with vectorborne disease (VBD) emergence and outbreaks. This is because epidemiologies of many vector-borne pathogens are driven by climate and environmental conditions that critically influence vector survival, reproduction, biting rates, feeding patterns, pathogen incubation and replication periods, and the efficiency of pathogen transmission among multiple hosts. Specific shifts in patterns of climate and weather persisting over a period of time are known to precede certain vector-borne disease outbreaks. Rift Valley fever disease systems is model disease that has led the way in demonstrating the importance of NASA Earth Observation data application use. The measurements including vegetation indices, rainfall, soil moisture have been used to infer and understand the conditions under which populations of Rift Valley fever vector populations emerge, increase in number and propagate and create elevated risk of outbreaks. This understanding has enabled, the data to be used over large areas as inputs into Rift Valley fever prediction models. At the same time due to the consistent and long time series nature of these measurements, the data has been critical to the classification of other areas with a potential for Rift Valley fever emergence and detailed vector ecology and habitat characterization. These these measurements deliver high temporal resolution (daily to monthly) data, large area coverage (regional to global) and spatial footprint on order of 30m to 10km. Continuity and consistency of measurements from NOAA- Advanced Very High-Resolution Radiometer (AVHRR), Terra/Aqua Moderate Resolution Imaging Spectroradiometer, Global Precipitation Measurement (GPM) mission and Soil Moisture Active Passive (SMAP) are therefore critical in meeting the needs vector-borne disease surveillance now and into the future. Lastly, improvements in the spatial resolution of current global sub seasonal to seasonal (S2S) forecasts to match that of satellite observations (1km – 10km) combined with the use of machine learning techniques will greatly contribute to improvements in disease prediction models. This resource is a critical component in designing systems for early warning and the same time yielding new insights into disease outbreaks patterns and dynamics.