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Use of the NASA Giovanni Data System for Geospatial Public Health Research: Example of Weather-Influenza Connection

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Goddard Earth Sciences Data and Information Services Center/Adnet Inc., Code 610.2, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
2
Goddard Earth Sciences Technology and Research (GESTAR), Universities Space Research Association, Columbia, MD 21046, USA
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NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
4
Goddard Earth Sciences Data and Information Services Center/NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
ISPRS Int. J. Geo-Inf. 2014, 3(4), 1372-1386; https://doi.org/10.3390/ijgi3041372
Received: 14 July 2014 / Revised: 20 October 2014 / Accepted: 14 November 2014 / Published: 10 December 2014
(This article belongs to the Special Issue Remote Sensing and Geospatial Technologies in Public Health)
The NASA Giovanni data analysis system has been recognized as a useful tool to access and analyze many different types of remote sensing data. The variety of environmental data types has allowed the use of Giovanni for different application areas, such as agriculture, hydrology, and air quality research. The use of Giovanni for researching connections between public health issues and Earth’s environment and climate, potentially exacerbated by anthropogenic influence, has been increasingly demonstrated. In this communication, the pertinence of several different data parameters to public health will be described. This communication also provides a case study of the use of remote sensing data from Giovanni in assessing the associations between seasonal influenza and meteorological parameters. In this study, logistic regression was employed with precipitation, temperature and specific humidity as predictors. Specific humidity was found to be associated (p < 0.05) with influenza activity in both temperate and tropical climate. In the two temperate locations studied, specific humidity was negatively correlated with influenza; conversely, in the three tropical locations, specific humidity was positively correlated with influenza. Influenza prediction using the regression models showed good agreement with the observed data (correlation coefficient of 0.5–0.83). View Full-Text
Keywords: remote sensing; climate; weather; public health; disease; environment; atmosphere; ocean; biosphere; precipitation remote sensing; climate; weather; public health; disease; environment; atmosphere; ocean; biosphere; precipitation
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Acker, J.; Soebiyanto, R.; Kiang, R.; Kempler, S. Use of the NASA Giovanni Data System for Geospatial Public Health Research: Example of Weather-Influenza Connection. ISPRS Int. J. Geo-Inf. 2014, 3, 1372-1386.

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