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

ABSTRACT: 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

SUBMITTER: Stashevsky PS 

PROVIDER: S-EPMC6617417 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature