Jungsoon Choi
Hanyang University · Medicine
About the Lab
Professor Jungsoon Choi's research lab specializes in spatial and environmental health analytics, focusing on the intersection of epidemiology, environmental factors, and statistical modeling. The lab investigates the spatio-temporal dynamics of disease spread, particularly infectious diseases like COVID-19 and chronic conditions such as cardiovascular diseases, by integrating geographic information systems (GIS), Bayesian hierarchical models, and advanced luminescence techniques. Key research directions include developing innovative statistical models for identifying spatial clusters with homogeneous health risk patterns and examining how built and environmental factors influence health outcomes across different populations and regions. The lab also contributes to environmental science through the analysis of luminescence signals in quartz for dating and environmental reconstruction.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15Spatial analysis is of great help in understanding the spread of infectious diseases, and spatial association was the key to the spatial spread during the early stages of the COVID-19 pandemic in mainland China.
Measurement of the optically stimulated luminescence signal during linear ramping of the stimulation power (LM-OSL) is a useful and efficient method for separating constituent OSL components, and for detailed investigation of the luminescence properties of each component. However, the practical procedures for the analysis of the quartz LM-OSL signal have been largely ambiguous in previous publications. In this study, we discuss various aspects of the analysis of quartz LM-OSL curves. This includ
Health outcomes are linked to air pollution, demographic, or socioeconomic factors which vary across space and time. Thus, it is often found that relative risks in space-time health data have locally different temporal patterns. In such cases, latent modeling is useful in the disaggregation of risk profiles. In particular, spatio-temporal mixture models can help to isolate spatial clusters each of which has a homogeneous temporal pattern in relative risks. In mixture modeling, various weight str
In spatial epidemiology studies, the effects of covariates on adverse health outcomes could vary over space and time so examining the spatio-temporally varying effects is useful. In particular, the association between covariates and health outcomes could have locally different temporal patterns. In this article, we develop a Bayesian spatio-temporal latent model to identify spatial clusters in each of which covariate effects have homogeneous temporal patterns as well as estimate heterogeneous te
This study assesses the association between the objectively measured built environment and cardiovascular diseases (CVDs) in 50,741 adults from the Korean Community Health Survey. The CVD outcomes of hypertension, diabetes, dyslipidemia, stroke, and myocardial infarction (MI) or angina were derived from self-reported histories of physician diagnoses. Using ArcGIS software and Korean government databases, this study measured the built environment variables for the 546 administrative areas of Gyeo
The first case of coronavirus disease 2019 (COVID-19) in South Korea was confirmed on January 20, 2020, approximately three weeks after the report of the first COVID-19 case in Wuhan, China. By September 15, 2021, the number of cases in South Korea had increased to 277,989. Thus, it is important to better understand geographical transmission and design effective local-level pandemic plans across the country over the long term. We conducted a spatiotemporal analysis of weekly COVID-19 cases in So
BACKGROUND: With more than 1.2 million illnesses and 29,000 deaths in sub-Saharan Africa in 2017, typhoid fever continues to be a major public health problem. Effective control of the disease would benefit from an understanding of the subnational geospatial distribution of the disease incidence. METHOD: We collated records of the incidence rate of typhoid fever confirmed by culture of blood in Africa from 2000 to 2022. We estimated the typhoid incidence rate for sub-Saharan Africa on 20 km × 20
Previous studies have shown an association between mortality and ambient air pollution in South Korea. However, these studies may have been subject to bias, as they lacked adjustment for spatio-temporal structures. This paper addresses this research gap by examining the association between air pollution and cause-specific mortality in South Korea between 2012 and 2015 using a two-stage Bayesian spatio-temporal model. We used 2012–2014 mortality and air pollution data for parameter estimation (i.
Previous studies using spatial statistical modeling that account for spatial associations between geographic areas are scarce. Therefore, this study examines the association between neighborhood environment and obesity using a Bayesian spatial multilevel model. Data from 78,014 adults living in Gyeonggi province in Korea were drawn from the 2013-2014 Korean Community Health Survey. Korean government databases and ArcGIS software (version 10.1, ESRI, Redlands, CA) were used to measure the neighbo
Hepatitis A is a water-borne infectious disease that frequently occurs in unsanitary environments. However, paradoxically, those who have spent their infancy in a sanitary environment are more susceptible to hepatitis A because they do not have the opportunity to acquire natural immunity. In Korea, hepatitis A is prevalent because of the distribution of uncooked seafood, especially during hot and humid summers. In general, the transmission of hepatitis A is known to be dynamically affected by so
Railway accidents are critical issues characterized by a large number of injuries and fatalities per accident due to massive public transport systems. This study proposes a new approach for evaluating the damages resulting from railway accidents using the two-part models (TPMs) such as the zero-inflated Poisson regression model (ZIP model) and the zero-inflated negative-binomial regression model (ZINB model) for the non-negative count measurements and the zero-inflated gamma regression model (ZI
Research Areas
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