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Annals of Data Science
Erschienen in:

22.01.2023

Bayesian Hierarchical Spatial Modeling of COVID-19 Cases in Bangladesh

verfasst von: Md. Rezaul Karim, Sefat-E-Barket

Erschienen in: Annals of Data Science | Ausgabe 5/2024

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Abstract

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.
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Metadaten
Titel
Bayesian Hierarchical Spatial Modeling of COVID-19 Cases in Bangladesh
verfasst von
Md. Rezaul Karim
Sefat-E-Barket
Publikationsdatum
22.01.2023
Verlag
Springer Berlin Heidelberg
Erschienen in
Annals of Data Science / Ausgabe 5/2024
Print ISSN: 2198-5804
Elektronische ISSN: 2198-5812
DOI
https://doi.org/10.1007/s40745-022-00461-1

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