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PLoS One. 2015 Mar 20;10(3):e0121475. doi: 10.1371/journal.pone.0121475. eCollection 2015.

Accurate prediction of severe allergic reactions by a small set of environmental parameters (NDVI, temperature).

Author information

1
Department of Experimental Endocrinology, University of Crete, School of Medicine, Heraklion, Greece; Department of Emergency Medicine, University of Crete, School of Medicine, Heraklion, Greece.
2
Department of Biology, University of Crete, School of Sciences and Technology, Heraklion, Greece.
3
Department of Experimental Endocrinology, University of Crete, School of Medicine, Heraklion, Greece.
4
Health Center of Gerani, Chania General Hospital, Mournies Chania, Greece.
5
Department of Emergency Medicine, University of Crete, School of Medicine, Heraklion, Greece.
6
Department of Biology, University of Crete, School of Sciences and Technology, Heraklion, Greece; Botanical Garden, University of Crete, Gallos Campus, Rethymnon, Greece.

Abstract

Severe allergic reactions of unknown etiology,necessitating a hospital visit, have an important impact in the life of affected individuals and impose a major economic burden to societies. The prediction of clinically severe allergic reactions would be of great importance, but current attempts have been limited by the lack of a well-founded applicable methodology and the wide spatiotemporal distribution of allergic reactions. The valid prediction of severe allergies (and especially those needing hospital treatment) in a region, could alert health authorities and implicated individuals to take appropriate preemptive measures. In the present report we have collecterd visits for serious allergic reactions of unknown etiology from two major hospitals in the island of Crete, for two distinct time periods (validation and test sets). We have used the Normalized Difference Vegetation Index (NDVI), a satellite-based, freely available measurement, which is an indicator of live green vegetation at a given geographic area, and a set of meteorological data to develop a model capable of describing and predicting severe allergic reaction frequency. Our analysis has retained NDVI and temperature as accurate identifiers and predictors of increased hospital severe allergic reactions visits. Our approach may contribute towards the development of satellite-based modules, for the prediction of severe allergic reactions in specific, well-defined geographical areas. It could also probably be used for the prediction of other environment related diseases and conditions.

PMID:
25794106
PMCID:
PMC4368791
DOI:
10.1371/journal.pone.0121475
[Indexed for MEDLINE]
Free PMC Article

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