Use of the NASA Giovanni Data System for Geospatial Public Health Research: Example of Weather-Influenza Connection
| Autor: | Richard K. Kiang, James G. Acker, Steven Kempler, Radina P. Soebiyanto |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
medicine.medical_specialty
biosphere Geospatial analysis Meteorology Geography Planning and Development Climate change lcsh:G1-922 precipitation computer.software_genre Environmental data remote sensing Earth and Planetary Sciences (miscellaneous) Temperate climate medicine Computers in Earth Sciences Air quality index climate disease business.industry Public health Environmental resource management public health Biosphere ocean humanities Geography Remote sensing (archaeology) weather atmosphere business computer environment lcsh:Geography (General) |
| Zdroj: | ISPRS International Journal of Geo-Information, Vol 3, Iss 4, Pp 1372-1386 (2014) |
| ISSN: | 2220-9964 |
| Popis: | 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). |
| Databáze: | OpenAIRE |
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