On regime changes of COVID-19 outbreak.
60J85
60M20
62-07
COVID-19
change point analysis
linear birth–death processes
statistical inference for branching processes
Journal
Journal of applied statistics
ISSN: 0266-4763
Titre abrégé: J Appl Stat
Pays: England
ID NLM: 9883455
Informations de publication
Date de publication:
2023
2023
Historique:
pmc-release:
13
02
2024
medline:
2
8
2023
pubmed:
2
8
2023
entrez:
2
8
2023
Statut:
epublish
Résumé
The COVID-19 pandemic has had a very serious impact on societies and caused large-scale economic changes and death toll worldwide. The first cases were detected in China, but soon the virus spread quickly worldwide and the intensity of newly reported infections grew high during this initial period almost everywhere. Later, despite all imposed measures, the intensity shifted abruptly multiple times during the two-year period between 2020 and 2022 causing waves of too high infection rates in almost every part of the world. To target this problem, we assume the data heterogeneity as multiple consecutive regime changes. The research study includes the development of a model based on automatic regime change detection and their combination with the linear birth-death process for long-run data fits. The results are empirically verified on data for 38 countries and US states for the period from February 2020 to April 2022. Finally, the initial phase (conditions) properties of infection development are studied.
Identifiants
pubmed: 37529570
doi: 10.1080/02664763.2023.2177625
pii: 2177625
pmc: PMC10388815
doi:
Types de publication
Journal Article
Langues
eng
Pagination
2343-2359Informations de copyright
© 2023 Informa UK Limited, trading as Taylor & Francis Group.
Déclaration de conflit d'intérêts
No potential conflict of interest was reported by the author(s).
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