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Agglomerative Clustering of Enteric Infections and Weather Parameters to Identify Seasonal Outbreaks in Cold Climates

The utility of agglomerative clustering methods for understanding dynamic systems that do not have a well-defined periodic structure has not yet been explored. We propose using this approach to examine the association between disease and weather parameters, to compliment the traditional harmonic reg...

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Autores principales: Stashevsky, Pavel S., Yakovina, Irina N., Alarcon Falconi, Tania M., Naumova, Elena N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6617417/
https://www.ncbi.nlm.nih.gov/pubmed/31212876
http://dx.doi.org/10.3390/ijerph16122083
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author Stashevsky, Pavel S.
Yakovina, Irina N.
Alarcon Falconi, Tania M.
Naumova, Elena N.
author_facet Stashevsky, Pavel S.
Yakovina, Irina N.
Alarcon Falconi, Tania M.
Naumova, Elena N.
author_sort Stashevsky, Pavel S.
collection PubMed
description The utility of agglomerative clustering methods for understanding dynamic systems that do not have a well-defined periodic structure has not yet been explored. We propose using this approach to examine the association between disease and weather parameters, to compliment the traditional harmonic regression models, and to determine specific meteorological conditions favoring high disease incidence. We utilized daily records on reported salmonellosis and non-specific enteritis, and four meteorological parameters (ambient temperature, dew point, humidity, and barometric pressure) in Barnaul, Russia in 2004–2011, maintained by the CliWaDIn database. The data structure was examined using the t-distributed stochastic neighbor embedding (t-SNE) method. The optimal number of clusters was selected based on Ward distance using the silhouette metric. The selected clusters were assessed with respect to their density and homogeneity. We detected that a well-defined cluster with high counts of salmonellosis occurred during warm summer days and unseasonably warm days in spring. We also detected a cluster with high counts of non-specific enteritis that occurred during unusually “very warm” winter days. The main advantage offered by the proposed technique is its ability to create a composite of meteorological conditions—a rule of thumb—to detect days favoring infectious outbreaks for a given location. These findings have major implications for understanding potential health impacts of climate change.
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spelling pubmed-66174172019-07-18 Agglomerative Clustering of Enteric Infections and Weather Parameters to Identify Seasonal Outbreaks in Cold Climates Stashevsky, Pavel S. Yakovina, Irina N. Alarcon Falconi, Tania M. Naumova, Elena N. Int J Environ Res Public Health Article The utility of agglomerative clustering methods for understanding dynamic systems that do not have a well-defined periodic structure has not yet been explored. We propose using this approach to examine the association between disease and weather parameters, to compliment the traditional harmonic regression models, and to determine specific meteorological conditions favoring high disease incidence. We utilized daily records on reported salmonellosis and non-specific enteritis, and four meteorological parameters (ambient temperature, dew point, humidity, and barometric pressure) in Barnaul, Russia in 2004–2011, maintained by the CliWaDIn database. The data structure was examined using the t-distributed stochastic neighbor embedding (t-SNE) method. The optimal number of clusters was selected based on Ward distance using the silhouette metric. The selected clusters were assessed with respect to their density and homogeneity. We detected that a well-defined cluster with high counts of salmonellosis occurred during warm summer days and unseasonably warm days in spring. We also detected a cluster with high counts of non-specific enteritis that occurred during unusually “very warm” winter days. The main advantage offered by the proposed technique is its ability to create a composite of meteorological conditions—a rule of thumb—to detect days favoring infectious outbreaks for a given location. These findings have major implications for understanding potential health impacts of climate change. MDPI 2019-06-12 2019-06 /pmc/articles/PMC6617417/ /pubmed/31212876 http://dx.doi.org/10.3390/ijerph16122083 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Stashevsky, Pavel S.
Yakovina, Irina N.
Alarcon Falconi, Tania M.
Naumova, Elena N.
Agglomerative Clustering of Enteric Infections and Weather Parameters to Identify Seasonal Outbreaks in Cold Climates
title Agglomerative Clustering of Enteric Infections and Weather Parameters to Identify Seasonal Outbreaks in Cold Climates
title_full Agglomerative Clustering of Enteric Infections and Weather Parameters to Identify Seasonal Outbreaks in Cold Climates
title_fullStr Agglomerative Clustering of Enteric Infections and Weather Parameters to Identify Seasonal Outbreaks in Cold Climates
title_full_unstemmed Agglomerative Clustering of Enteric Infections and Weather Parameters to Identify Seasonal Outbreaks in Cold Climates
title_short Agglomerative Clustering of Enteric Infections and Weather Parameters to Identify Seasonal Outbreaks in Cold Climates
title_sort agglomerative clustering of enteric infections and weather parameters to identify seasonal outbreaks in cold climates
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6617417/
https://www.ncbi.nlm.nih.gov/pubmed/31212876
http://dx.doi.org/10.3390/ijerph16122083
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