Bayesian Hierarchical Spatial Modeling of COVID-19 Cases in Bangladesh

被引：1
作者：
Karim M.R. [1 ]
Sefat-E-Barket [1 ]
机构：
[1] Department of Statistics, Jahangirnagar University, Savar, Dhaka
来源：
Annals of Data Science | 2024年 / 11卷 / 5期
关键词：
Bayesian hierarchical models; Conditional Autoregressive model; Convolution model; COVID-19; Modified CAR model; Spatial dependency;
D O I：
10.1007/s40745-022-00461-1
中图分类号：
学科分类号：
摘要：
This research aimed to investigate the spatial autocorrelation and heterogeneity throughout Bangladesh’s 64 districts. Moran I and Geary C are used to measure spatial autocorrelation. Different conventional models, such as Poisson-Gamma and Poisson-Lognormal, and spatial models, such as Conditional Autoregressive (CAR) Model, Convolution Model, and modified CAR Model, have been employed to detect the spatial heterogeneity. Bayesian hierarchical methods via Gibbs sampling are used to implement these models. The best model is selected using the Deviance Information Criterion. Results revealed Dhaka has the highest relative risk due to the city’s high population density and growth rate. This study identifies which district has the highest relative risk and which districts adjacent to that district also have a high risk, which allows for the appropriate actions to be taken by the government agencies and communities to mitigate the risk effect. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.
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页码：1581 / 1607
页数：26
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