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Statistical inference using GLEaM model with spatial heterogeneity and correlation between regions.

ABSTRACT: A better understanding of various patterns in the coronavirus disease 2019 (COVID-19) spread in different parts of the world is crucial to its prevention and control. Motivated by the previously developed Global Epidemic and Mobility (GLEaM) model, this paper proposes a new stochastic dynamic model to depict the evolution of COVID-19. The model allows spatial and temporal heterogeneity of transmission parameters and involves transportation between regions. Based on the proposed model, this paper also designs a two-step procedure for parameter inference, which utilizes the correlation between regions through a prior distribution that imposes graph Laplacian regularization on transmission parameters. Experiments on simulated data and real-world data in China and Europe indicate that the proposed model achieves higher accuracy in predicting the newly confirmed cases than baseline models.

SUBMITTER: Tan Y

PROVIDER: S-EPMC9534028 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Statistical inference using GLEaM model with spatial heterogeneity and correlation between regions.

Tan Yixuan Y Zhang Yuan Y Cheng Xiuyuan X Zhou Xiao-Hua XH

Scientific reports 20221005 1

A better understanding of various patterns in the coronavirus disease 2019 (COVID-19) spread in different parts of the world is crucial to its prevention and control. Motivated by the previously developed Global Epidemic and Mobility (GLEaM) model, this paper proposes a new stochastic dynamic model to depict the evolution of COVID-19. The model allows spatial and temporal heterogeneity of transmission parameters and involves transportation between regions. Based on the proposed model, this paper ...[more]

PMID: 36198691

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