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A Bayesian spatially-clustered coefficient model with temporal structures for hepatitis A data in South Korea.
Jaeseon Lee1,2, Jungsoon Choi3,4
1Department of Statistics, Texas A&M University, College Station, Texas, USA.
Journal of Applied Statistics
|June 4, 2026
Summary
This study analyzed hepatitis A infections in South Korea using a Bayesian spatio-temporal model. It revealed how risk factors vary across regions and time, aiding targeted public health interventions.
Area of Science:
- Epidemiology
- Biostatistics
- Public Health
Background:
- Hepatitis A is a widespread, contagious viral liver infection.
- Infectious disease risk factors can vary significantly across different geographic regions and time periods.
- Understanding these spatio-temporal dynamics is crucial for effective disease control.
Purpose of the Study:
- To analyze the spatio-temporal patterns of hepatitis A infections in the Republic of Korea.
- To investigate how demographic and socioeconomic factors influence hepatitis A risk over space and time.
- To apply a Bayesian spatio-temporal model to uncover region-specific, time-varying risk effects.
Main Methods:
- Analysis of monthly hepatitis A infection counts from January 2020 to December 2021.
- Utilized a Bayesian spatially-clustered coefficient model with temporal structures.
- Employed a two-stage framework to mitigate spatial confounding bias in spatio-temporal models.
Main Results:
- Identified sub-regions with distinct, temporally varying risk effects for hepatitis A.
- Demonstrated the utility of Bayesian spatio-temporal modeling for understanding disease dynamics.
- Quantified the influence of covariates on hepatitis A outcomes across different space-time units.
Conclusions:
- Spatio-temporal variations in hepatitis A risk factors are significant and can be modeled effectively.
- The Bayesian approach provides valuable insights into localized and time-dependent disease transmission.
- Findings can inform targeted public health strategies for hepatitis A prevention and control.
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