A Review of Bayesian Spatiotemporal Models in Spatial Epidemiology DOI Creative Commons
Yufeng Wang, Xue Chen, Feng Xue

et al.

ISPRS International Journal of Geo-Information, Journal Year: 2024, Volume and Issue: 13(3), P. 97 - 97

Published: March 18, 2024

Spatial epidemiology investigates the patterns and determinants of health outcomes over both space time. Within this field, Bayesian spatiotemporal models have gained popularity due to their capacity incorporate spatial temporal dependencies, uncertainties, intricate interactions. However, complexity modelling computations associated with vary across different diseases. Presently, there is a limited comprehensive overview applications in epidemiology. This article aims address gap through thorough review. The review commences by delving into historical development concerning disease mapping, prediction, regression analysis. Subsequently, compares these terms data distribution, general models, environmental covariates, parameter estimation methods, model fitting standards. Following this, essential preparatory processes are outlined, encompassing acquisition, preprocessing, available statistical software. further categorizes summarizes application Lastly, critical examination advantages disadvantages along considerations for application, provided. enhance comprehension dynamic distribution prediction epidemics. By facilitating effective scrutiny, especially context global COVID-19 pandemic, holds significant academic merit practical value. It also contribute improved ecological epidemiological prevention control strategies.

Language: Английский

A Review of Bayesian Spatiotemporal Models in Spatial Epidemiology DOI Creative Commons
Yufeng Wang, Xue Chen, Feng Xue

et al.

ISPRS International Journal of Geo-Information, Journal Year: 2024, Volume and Issue: 13(3), P. 97 - 97

Published: March 18, 2024

Spatial epidemiology investigates the patterns and determinants of health outcomes over both space time. Within this field, Bayesian spatiotemporal models have gained popularity due to their capacity incorporate spatial temporal dependencies, uncertainties, intricate interactions. However, complexity modelling computations associated with vary across different diseases. Presently, there is a limited comprehensive overview applications in epidemiology. This article aims address gap through thorough review. The review commences by delving into historical development concerning disease mapping, prediction, regression analysis. Subsequently, compares these terms data distribution, general models, environmental covariates, parameter estimation methods, model fitting standards. Following this, essential preparatory processes are outlined, encompassing acquisition, preprocessing, available statistical software. further categorizes summarizes application Lastly, critical examination advantages disadvantages along considerations for application, provided. enhance comprehension dynamic distribution prediction epidemics. By facilitating effective scrutiny, especially context global COVID-19 pandemic, holds significant academic merit practical value. It also contribute improved ecological epidemiological prevention control strategies.

Language: Английский

Citations

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