Change-Point Detection in Homogeneous Segments of COVID-19 Daily Infection

نویسندگان

چکیده

Modeling the number of individuals in different states is a principal tool event an epidemic. The natural transition between possible often includes deliberate interference such as isolation or vaccination. Thus, mathematical model may need to be re-calibrated due various factors. considered this paper SIRD epidemic model. An additional parameter moment changing description phenomenon when parameters change and not pre-specified. Detecting estimating real time subject statistical research. A sequential (online) approach was applied using Bayesian shift point detection algorithm trimmed exact linear time. We show how methods analysis behave instances. These are verified on simulated data pandemic selected European country. simulation performed with social network graph obtain practical representation ability. epidemiological used come from territory Poland concern COVID-19 Poland. results satisfactory moments where needs re-calibrated. effectiveness proposed combination methods.

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ژورنال

عنوان ژورنال: Axioms

سال: 2022

ISSN: ['2075-1680']

DOI: https://doi.org/10.3390/axioms11050213