Hot-spots detection in count data by Poisson assisted smooth sparse tensor decomposition

© 2022 Informa UK Limited, trading as Taylor & Francis Group.

Détails bibliographiques
Publié dans:Journal of applied statistics. - 1991. - 50(2023), 14 vom: 14., Seite 2999-3029
Auteur principal: Zhao, Yujie (Auteur)
Autres auteurs: Huo, Xiaoming, Mei, Yajun
Format: Article en ligne
Langue:English
Publié: 2023
Accès à la collection:Journal of applied statistics
Sujets:Journal Article CUSUM Hot-spots detection Poisson regression spatio-temporal model tensor decomposition
Description
Résumé:© 2022 Informa UK Limited, trading as Taylor & Francis Group.
Count data occur widely in many bio-surveillance and healthcare applications, e.g. the numbers of new patients of different types of infectious diseases from different cities/counties/states repeatedly over time, say, daily/weekly/monthly. For this type of count data, one important task is the quick detection and localization of hot-spots in terms of unusual infectious rates so that we can respond appropriately. In this paper, we develop a method called Poisson assisted Smooth Sparse Tensor Decomposition (PoSSTenD), which not only detect when hot-spots occur but also localize where hot-spots occur. The main idea of our proposed PoSSTenD method is articulated as follows. First, we represent the observed count data as a three-dimensional tensor including (1) a spatial dimension for location patterns, e.g. different cities/countries/states; (2) a temporal domain for time patterns, e.g. daily/weekly/monthly; (3) a categorical dimension for different types of data sources, e.g. different types of diseases. Second, we fit this tensor into a Poisson regression model, and then we further decompose the infectious rate into two components: smooth global trend and local hot-spots. Third, we detect when hot-spots occur by building a cumulative sum (CUSUM) control chart and localize where hot-spots occur by their LASSO-type sparse estimation. The usefulness of our proposed methodology is validated through numerical simulation studies and a real-world dataset, which records the annual number of 10 different infectious diseases from 1993 to 2018 for 49 mainland states in the United States
Description:Date Revised 10.02.2024
published: Electronic-eCollection
Citation Status PubMed-not-MEDLINE
ISSN:0266-4763
DOI:10.1080/02664763.2022.2112557