Research

The research focuses on identifying regional disparities in resource allocation within global public health systems and understanding the underlying causes and consequences. Various analytical methodologies, including causal inference, machine learning, and geographic information systems (GIS), are utilized to explore these disparities. The research is framed around three key perspectives: Public Health Crisis Management, Public Health Services Planning, and the Social Impact of Public Health Crises.

1. Public Health Crisis Management

Epidemic patterns and regional impacts of disparities are central areas of study. A collaborative study in Nigeria, funded by the United States Agency for International Development, identified tuberculosis hotspots (published in JMIR Public Health and Surveillance). This study demonstrated the importance of identifying spatial patterns for early detection and resource allocation in TB cases.

A follow-up study investigated the influence of geographical accessibility and supply-demand dynamics on the initiation and timing of TB treatment. Results indicated that proximity to TB treatment facilities and a higher supply-to-demand ratio significantly improved timely treatment initiation, while gender-specific barriers contributed to delays (Dissertation Chapter 1).

2. Public Health Services Planning

Another key research area focuses on optimizing demand forecasting and resource allocation strategies for emergency medical services (EMS). Research on traffic accident patterns and emergency response times in Nigeria, funded by the National Institutes of Health (NIH), revealed regional disparities and proposed solutions to improve emergency response efficiency (published in BMC Public Health).

Further research on EMS demand forecasting in Busan, South Korea, utilized machine learning techniques such as Graph Convolutional Networks (GCN) to predict fluctuations in EMS demand. This work emphasized the need for dynamic resource allocation strategies for regional and temporal demand variations (Dissertation Chapter 2).

4. Ongoing Paper and Future Research Directions

1) Community-Based Health Disparities

Future research will explore health disparities at the community level, focusing on the impact of regional characteristics, infrastructure, and access to resources on health outcomes. Projects in this area include:

Disaster Shelter Accessibility: An ongoing study examines the accessibility of national shelters in Florida during disaster situations. The research aims to identify vulnerable areas and propose strategies for ensuring equitable access to shelters during emergencies by analyzing regional factors such as population density, infrastructure, and socioeconomic status.
Regional Disparities in Education: Another project focuses on the regional disparities in student performance following the transition to online education during the COVID-19 pandemic. The study highlights how these factors exacerbate educational inequalities by examining the variability in digital infrastructure and access to educational resources, providing important policy insights for addressing these gaps.

These efforts aim to address disparities arising from community-level differences, offering actionable public health and educational policy solutions.

2) Machine Learning and Predictive Modeling in Public Health

This area of research applies machine learning techniques to predict health outcomes and improve healthcare strategies, particularly for vulnerable populations. Current projects include:

Kidney Transplant Readmission Rates: In collaboration with a nursing professor in South Korea, a machine learning model is being developed to analyze factors contributing to readmission rates among kidney transplant patients. This study emphasizes the role of individual patient characteristics—such as age, gender, and comorbidities—in predicting readmission, to improve personalized healthcare strategies and reduce readmission rates.
Cancer Incidence in Kidney Transplant Patients: A matching study is being conducted to explore cancer incidence disparities among kidney transplant patients by gender. By identifying underlying factors contributing to these disparities, the research aims to inform tailored medical interventions that can improve outcomes for this population.

3) Spatial Analysis and Geographical Accessibility

Spatial analysis is key in understanding how geographic factors affect healthcare access and resource allocation. Current projects utilizing spatial methodologies include:

Disaster Shelter Accessibility: As mentioned above, this project uses spatial analysis to assess shelter accessibility in Florida. The study evaluates regional factors like population density and infrastructure to pinpoint areas with limited access, ultimately guiding more equitable disaster preparedness strategies.
Healthcare Resource Allocation: Future research will continue to employ spatial statistics to analyze patterns of resource allocation and healthcare accessibility. The goal is to understand how geographical disparities contribute to health inequalities and develop targeted interventions that address these challenges.

Publications

[5] DOHYO JEONG, Jessi Hanson-DeFusco, and Dohyeong Kim. (2022). Digital Mass Hysteria during Pandemics: A Case Study of Twitter Communication Patterns in the US during COVID-19 Period. Behavioral Sciences, 14(5), 389. https://doi.org/10.3390/bs14050389

Research Topic: Analysis of changes in public sentiment regarding vaccine supply policy
Design/Method: Text Sentiment Analysis and Interrupted Time Series Analysis.

[4] Hong, S., DOHYO JEONG, & Kim, P. (2024). Have offender demographics changed since the COVID-19 Pandemic? Evidence from money mules in South Korea. Journal of Criminal Justice, 91, 102156. https://doi.org/10.1016/j.jcrimjus.2024.102156 (IF: 5.5)

Research Topic: Exploring the Influence of the COVID-19 Pandemic on Crime Patterns
Design/Method: Interrupted Time Series Analysis.

[3] DOHYO JEONG., Kim, D., Mohiuddin, H., Kang, S., & Kim, S. (2023). Regional Disparity in the Educational Impact of COVID-19: A Spatial Difference-in-Difference Approach. Sustainability, 15(16), 12514. https://doi.org/10.3390/su151612514 (IF: 3.9)

Research Topic: Analyzing regional disparities in transitioning to online classes.
Design/Method: Spatial Difference-in-Difference

[2] Odusola, A. O., DOHYO JEONG, Malolan, C., Kim, D., Venkatraman, C., Kola-Korolo, O., … & Nwariaku, F. E. (2023). Spatial and temporal analysis of road traffic crashes and ambulance responses in Lagos state, Nigeria. BMC public health, 23(1), 2273. https://doi.org/10.1186/s12889-023-16996-8 (IF: 4.7)

Research Topic: Comparing traffic accident patterns based on time, and regional characteristics.
Design/Method: Geospatial mapping and Hotspot Analysis.

[1] Ogbudebe Chidubem, DOHYO JEONG, OdumeBethrand ….. (2023). Editorial Decision/Comments on “Identifying Hotspots of Tuberculosis in Nigeria using Early Warning Outbreak Recognition System: Retrospective Analysis of Implications for Active Case Finding Interventions. JMIR Public Health and Surveillance, 9 (1), e40311. https://publichealth.jmir.org/2023/1/e40311/ (IF: 8.5)

Research Topic: Evaluation of the effectiveness of tuberculosis early warning program
Design/Method: Kernel density and Getis-Ord Gi* Hot Spot Analyses.

Publications in Korea

[6] CHANG-JIN KIM, DOHYO JEONG, (2022). Factors Influencing Public Officials Innovative Behavior for Platform Governance. The Journal of Korea Policy Research. Vol.22 No.3: 141-171.

Research Topic: Analyzing innovation factors in public sector platform governance.
Design/Method: Platform governance and Factors influencing innovative behavior.

[5] DOHYO JEONG, Sangho Moon, SUHO BAE. (2019). Factors Affecting the Distribution of National Subsidies in Korean Local Governments: Focusing on Rhodes’ Power-Dependence Model. The Korea Journal of Policy Analysis and Management, Vol.29 No3: 21-53.

Research Topic: Factors in central subsidy allocation to local governments
Design/Method: Panel Corrected Standard Errors (PCSE) model and Prais-Winsten procedure.

[4] DOHYO JEONG, CHANG-JIN KIM, SUHO BAE. (2019). A Study on Determinants of Tax Attitude: Focusing on Slippery Slope Framework, Public Policy Review, Vol.33 No.3: 43-72.

Research Topic: Examining the Impact of Tax Compliance and Taxpayer Attitude
Design/Method: Decision tree model and neural network model.

[3] CHANG-JIN KIM, DOHYO JEONG, SUNG-WOO HONG. (2019). The Effects of Decentralization Perception on the Recognition of Intergovernmental Relationship: Focusing on Mediating Effect of Dispute Settlement System - Journal of Local Government Studies.Vol.31 No.3: 1-35.

Research Topic: Government Perception and Decentralization
Design/Method: Ordered Logit Model and Structural Equation Model (SEM).

[2] DOHYO JEONG, YOUNGKYU LEE, SEONGYOUNG JEONG. (2018). An Analysis of the Effect of the Tax Rate on the Financial Efficiency of Local Governments. The Korea Journal of Local Government Studies, Vol.22 No.3: 415-443.

Research Topic: Local Tax Flexibility and Fiscal Efficiency
Design/Method: Difference-in-Difference analysis, Panel Corrected Standard Errors model.

[1] Dae-yong Hyun, DOHYO JEONG, (2017). Analysis of differences in perception of administrative values among civil servants and general civil servants: Focused on Suwon City Government Officials. Suwon Research Institute. No. 12: 119-141.

Research Topic: Comparing administrative values between City officials and general civil servants.
Design/Method: Ordered Logit Model.

Ongoing Paper

[6] DOHYO JEONG, Dohyeong Kim, Okey Okuzu, Chidubem Ogbudebe. (2024). Beyond Distance: Does Geographical Accessibility for Tuberculosis Treatment Follow Supply, Demand or Both?

Research Topic: The impact of geographical accessibility on tuberculosis treatment initiation
Design/Method: Calculating the regional geographic accessibility, Generalized Linear Mixed Models

[5] DOHYO JEONG, Dohyeong Kim. (2024). Medical Resource Optimization Model Analysis for Vulnerable Areas of Emergency Medical Service Using Spatial Machine Learning: A Case Study on Korea.

Research Topic: Identification of areas with imbalanced medical resources and optimal allocation
Design/Method: T-Graph Convolutional Network and Maximal Covering Location Problem

[4] DOHYO JEONG. (2024). The Patterns of COVID-19 Therapeutics Supply and Demand in Texas: A Spatial-Temporal INLA Approach.

Research Topic: Identifying medical resource supply and demand imbalance and determining factors
Design/Method: Spatial-Temporal Integrated Nested Laplace Approximations (INLA)

[3] Hye Jin Chong, DOHYO JEONG, Ji-hyun Yeom, Dohyeong Kim. (2024). Machine Learning-Based Prediction Model for Early Hospital Readmission After Kidney Transplantation.

Research Topic: Classification models to predict readmission patients after kidney transplantation
Design/Method: Decision tree, Random Forest, XGBoost, Support Vector Machine

[2] DOHYO JEONG, Chang-jin Kim. (2024). Fiscal Forecast Errors in Public Health Expenditure: Based on a Spatial-Temporal Approach.

Research Topic: Identifying medical resource supply and demand imbalance and determining factors
Design/Method: Calculating Regional Variability, Spatial Regression Model.

[1] DOHYO JEONG, Younghyun Cho. (2024). Evaluating the Distribution and Accessibility of National Shelters in the United States for Natural Disaster Preparedness.

Research Topic: Evaluating National shelter distribution for optimal accessibility during disasters
Design/Method: Generalization Multilevel Regression Model with Spatial Concepts

Conference Presentation

[4] DOHYO JEONG. (2023). Differential Side Effects of COVID-19 Response Policies on the U.S. Labor Market: A Spatial-Temporal Analysis. APPAM (Association for Public Policy Analysis and Management) 2023 Annual Conference. Nov. 2023.

[3] DOHYO JEONG. (2023). The Patterns of COVID-19 Therapeutics Supply and Demand in Texas: A Spatial-Temporal INLA Approach. APHA (American Public Health Association) 2023 Annual Conference. Nov. 2023.

[2] DOHYO JEONG. (2023). Regional disparity in the uninsurance rate impact of COVID-19: a spatial machine learning approach. ASPA (American Society for Public Administration) 2023 Annual Conference. 21. March. 2023.

[1] DOHYO JEONG. (2023). Digital Mass Hysteria? during Pandemics: A Case Study of Twitter Communication Patterns in the US during COVID-19 Period. Conference On Public Process Research. 12. Jan. 2023.

Conference Presentation in Korea

[7] DOHYO JEONG. (2022). A comparison of the spread trend prediction model according to the government’s COVID-19 response policy change and its influence, 2022 Korean Public Administration International Conference. Korea. 22 June. 2022.

[6] DOHYO JEONG. (2021). The effect of the government’s vaccination management plan on the change of sentiment toward vaccines, 2021 Global Disastronomy Workshop. Texas. USA. 17 Dec. 2021.

[5] DOHYO JEONG, Chang-jin Kim. SUHO BAE. (2019). A Study on the Factors Affecting the Taxation Attitude of General Taxpayers. Korean Association for Local Government Studies Winter Conference. Seoul. KOREA. 14 Feb. 2019

[4] Chang-jin Kim. DOHYO JEONG, SUHO BAE. (2019). The Mediation Effect of Dispute Settlement System in the Perception of Regional Dispersion and Intergovernmental Relations. Korean Association for Local Government Studies Winter Conference. Seoul. KOREA. 14 Feb. 2019.

[3] DOHYO JEONG, YOUNGKYU LEE, SUHO BAE. (2018) An Analysis of the Effect of the Tax Rate on the Financial Efficiency of Local Governments. Korea Association of Local Administration Summer Joint Conference Chungcheong-do. KOREA. 20 Jul. 2018

[2] DOHYO JEONG. (2017). The Effects of Tax Recognition on the pros and cons of Welfare Policy. Seoul Association of Public Administration Fall Conference. Seoul. KOREA. 3 Nov. 2017

[1] Dae-yong Hyun, DOHYO JEONG. (2017). A Study on the Policy Diffusion of Local Government in Korea: focusing on Resident Participation Budget System. Korea Association of Local Administration Summer Joint Conference. Gyeonggi-do. KOREA. 18 Aug. 2017