Science Data Products for Public Health Decision Support

The Public Health Applications in Remote Sensing (PHAiRS) project is engineering an enhanced syndromic surveillance system for dust-related respiratory diseases in the southwestern United States based on assimilating Earth observation (EO) data from NASA experimental satellites. There is a rich literature describing the roles and benefits of using EO data in public health, but most of the documentation is based on anecdotal inferences derived front traditional image interpretation. For several reasons, public health communities cannot rely on evidence of this type because: (1) they need science results that verify, validate, and benchmark the statistical and economic benefits front these exotic inputs; and, (2) they lack the systems that can deliver such reliable information economically and swiftly. In PHAiRS, several data sets are being assimilated as replacement parameters in the Dust Regional Atmospheric Model (DREAM) to improve simulations of particulate matter entrainment, timing of entrainment, concentrations, and subsequent movement as governed by hourly weather variables available in a regional version of the National Centers for Environmental Prediction (NCEP/Eta) model. On-going simulations from DREAM measure hourly, daily and weekly model improvements from individual EO data replacements that are refreshed on a weekly, seasonal, or inter-annual basis. The overall aims are to: (a) combine the measured improvements from several EO data series that optimize dust forecast scenarios for public health authorities; (b) benchmark each step in the process to document the benefits of EO data inputs into respiratory health care; and (c) develop retrospective and forecast statistics front model runs that boost system reliability and user confidence. Ultimately, the goal is to develop a reliable respiratory public health syndromic surveillance system that can be translated into routine uses of EO data from future NPOESS sensors.