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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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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. |
format | Online Article Text |
id | pubmed-6617417 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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