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Bayesian Hierarchical Spatial Modeling of COVID-19 Cases in Bangladesh

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  • Md. Rezaul Karim

    (Jahangirnagar University)

  • Sefat-E-Barket

    (Jahangirnagar University)

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.

Suggested Citation

  • Md. Rezaul Karim & Sefat-E-Barket, 2024. "Bayesian Hierarchical Spatial Modeling of COVID-19 Cases in Bangladesh," Annals of Data Science, Springer, vol. 11(5), pages 1581-1607, October.
    • Handle: RePEc:spr:aodasc:v:11:y:2024:i:5:d:10.1007_s40745-022-00461-1
    DOI: 10.1007/s40745-022-00461-1
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    1. Jim Griffin & Mark Steel, 2007. "Bayesian stochastic frontier analysis using WinBUGS," Journal of Productivity Analysis, Springer, vol. 27(3), pages 163-176, June.
    2. James M. Tien, 2017. "Internet of Things, Real-Time Decision Making, and Artificial Intelligence," Annals of Data Science, Springer, vol. 4(2), pages 149-178, June.
    3. Kelejian, Harry H. & Prucha, Ingmar R., 2010. "Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances," Journal of Econometrics, Elsevier, vol. 157(1), pages 53-67, July.
    4. Sanjay Kumar, 2020. "Monitoring Novel Corona Virus (COVID-19) Infections in India by Cluster Analysis," Annals of Data Science, Springer, vol. 7(3), pages 417-425, September.
    5. Kazi Abdul, Mannan & Khandaker Mursheda, Farhana, 2020. "The COVID-19 pandemic: Challenges and reality of quarantine, isolation and social distancing for the returnee migrants in Bangladesh," MPRA Paper 101348, University Library of Munich, Germany, revised 2020.
    6. Sylvain Coly & Myriam Garrido & David Abrial & Anne-Françoise Yao, 2021. "Bayesian hierarchical models for disease mapping applied to contagious pathologies," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-28, January.
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