Abstract Number:
2112
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Jaeseon Lee (1), Jungsoon Choi (2)
Institutions:
(1) Texas A&M University, N/A, (2) Hanyang University, N/A
Co-Author:
First Author:
Presenting Author:
Abstract Text:
Hepatitis A, a highly contagious viral liver infection, is globally widespread. Data on hepatitis A and covariates like average temperature and per capita income are collected across space and time. Consequently, the association between infectious disease outcomes and risk factors may differ across space and time. Some sub-regions may have a heterogeneous association with others, while a homogeneous temporal structure may exist within certain sub-regions. Acknowledging the potential variability in these associations, this study focused on comprehending the spatio-temporal dynamics of hepatitis A through a statistical model.
We analyzed monthly hepatitis A counts in South Korea from January 2020 to December 2021 using a Bayesian spatio-temporal model. Employing a Bayesian spatially-clustered coefficient model with temporal structures, we estimated sub-regions with temporally varying risk effects. Our goal is to use proposed model to uncover insights into the spatio-temporally varying relationships between covariates and hepatitis A outcomes. Additionally, we addressed spatial confounding bias by incorporating a two-stage framework in our analysis.
Keywords:
Hepatitis A|spatio-temporal model|spatially-clustered coefficient|spatial confounding bias|Bayesian inference|
Sponsors:
Section on Statistics in Epidemiology
Tracks:
Miscellaneous
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