Heterogeneity learning for SIRS model: an application to the COVID-19
Statistics and Its Interface
; 14(1):73-81, 2021.
Article
Dans Anglais
| Web of Science | ID: covidwho-1008389
ABSTRACT
We propose a Bayesian Heterogeneity Learning approach for Susceptible-Infected-Removal-Susceptible (SIRS) model that allows underlying clustering patterns for transmission rate, recovery rate, and loss of immunity rate for the latest corona virus (COVID-19) among different regions. Our proposed method provides simultaneously inference on parameter estimation and clustering information which contains both number of clusters and cluster configurations. Specifically, our key idea is to formulates the SIRS model into a hierarchical form and assign the Mixture of Finite mixtures priors for heterogeneity learning. The properties of the proposed models are examined and a Markov chain Monte Carlo sampling algorithm is used to sample from the posterior distribution. Extensive simulation studies are carried out to examine empirical performance of the proposed methods. We further apply the proposed methodology to analyze the state level COVID-19 data in U.S.
Collection:
Bases de données des oragnisations internationales
Base de données:
Web of Science
langue:
Anglais
Revue:
Statistics and Its Interface
Année:
2021
Type de document:
Article
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