ArticleInternational journal of environmental research and public health2019
Agglomerative Clustering of Enteric Infections and Weather Parameters to Identify Seasonal Outbreaks in Cold Climates.
Article in International journal of environmental research and public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Investigating seasonal patterns in enteric infections: a systematic review of time series methods.Epidemiology and infection · 2022Pooled it
- Seasonal impacts onApplied and environmental microbiology · 2026Article
- Global seasonal and pandemic patterns in influenza: An application of longitudinal study designs.International statistical review = Revue internationale de statistique · 2022Article
- How Seasonality of Malnutrition Is Measured and Analyzed.International journal of environmental research and public health · 2021Review
- IoT-IIRS: Internet of Things based intelligent-irrigation recommendation system using machine learning approach for efficient water usage.PeerJ. Computer science · 2021Article
- Effects of Data Aggregation on Time Series Analysis of Seasonal Infections.International journal of environmental research and public health · 2020Article
- Assessing Seasonality Variation with Harmonic Regression: Accommodations for Sharp Peaks.International journal of environmental research and public health · 2020Article
- Rotavirus Seasonality: An Application of Singular Spectrum Analysis and Polyharmonic Modeling.International journal of environmental research and public health · 2019Article
- Spatiotemporal Patterns of Cholera Hospitalization in Vellore, India.International journal of environmental research and public health · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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
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Registered trials
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