2023  

• Lee, J., & Koh, K. (2023). Scheduling Complex Cyber-Physical Systems with Mixed-Criticality Components. Systems, 11(6), 281. https://www.mdpi.com/2079-8954/11/6/281 
   

• Loo, B. P. Y., Tsoi, K. H., Axhausen, K. W., Cao, M., Lee, Y., & Koh, K. P. (2023). Spatial risk for a superspreading environment: Insights from six urban facilities in six global cities across four continents. Frontiers in Public Health. https://doi.org/10.3389/fpubh.2023.1128889 
   

• Hur, H., & Koh, K. (2023). Why and Where Do Highly Educated Workers Relocate? A National-Level Analysis across US Census Regions. Sustainability, 15(2), 1423. 
   

• Koh, K., Boulos, M. N. K., Zheng, G., Zhang, H., Iyyanki, M. V., Bwambale, B., & Dewan, A. (2023). A proof-of-concept online metadata catalogue service of Earth observation datasets for human health research in exposomics. arXiv e-prints, arXiv:2311.08770. https://doi.org/10.48550/arXiv.2311.08770
   

• Ng, K. Y., & Koh, K. (2023). Toward equitable public transportation with pets: Accessing veterinary care under mobility constraints in Hong Kong through taxi fare analysis. International Journal of Sustainable Transportation, 1-11. https://doi.org/10.1080/15568318.2023.2295858

  2022  

• Koh, K., Hyder, A., Karale, Y., & Kamel Boulos, M. N. (2022). Big Geospatial Data or Geospatial Big Data? A Systematic Narrative Review on the Use of Spatial Data Infrastructures for Big Geospatial Sensing Data in Public Health. Remote Sensing, 14(13). https://doi.org/10.3390/rs14132996
   

• Chui, S., Li, T., & Koh, K. (2022). Impact of Covid-19 on Food Environment in Hong Kong a Cross-sectional Qualitative Study. 
   

• Koh, K., Lam, Y. F., & Hur, H. (2022). Lessons from COVID-19 Vaccine Rollout in Hong Kong and South Korea. In Coronavirus (COVID-19) Outbreaks, Vaccination, Politics and Society (pp. 99-115). Springer. https://doi.org/10.1007/978-3-031-09432-3_7
   

• Koh, K., Lee, S., Park, S., & Lee, J. (2022). Media Reports on COVID-19 Vaccinations: A Study of Topic Modeling in South Korea. Vaccines, 10(12), 2166. 
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• Koh, K., Tang, K. C., Axhausen, K., & Loo, B. P. Y. (2022). A metropolitan-scale, three-dimensional agent-based model to assess the effectiveness of the COVID-19 Omicron wave interventions in a hyperdense city: a case study of Hong Kong. International Journal of Infectious Diseases, 122, 534-536. https://doi.org/10.1016/j.ijid.2022.06.042
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• Ng, K. Y., Ho, C. L., & Koh, K. (2022). Spatial-Temporal Accessibility and Inequality of Veterinary Service in Hong Kong: A Geographic Information System-Based Study. Frontiers in Veterinary Science, 9, 857914-857914. https://doi.org/10.3389/fvets.2022.857914

  2019  

• Koh, K., Reno, R. and Hyder, A. (2019). "Examining Disparities in Food Accessibility among Households in Columbus, Ohio: An Agent-Based Model.” Food Security 11(2): 317-331. DOI: 10.1007/s12571-019-00900-7 (IF2018=2.153).

  2018  

• Koh, K., Elder, T., Grady, S. C., Darden, J. and Vojnovic, I. (2018) “Explained and Unexplained Racial and Regional Inequality in Obesity Prevalence in the United States.” Ethnicity & Health 1-14. DOI: 10.1080/13557858.2018.1442559 (IF2018=2.671).
   

• Koh, K., Grady, S. C., Darden, J. and Vojnovic, I. (2018) “Adult Obesity Prevalence at the county level in the United States, 2000-2010: Downscaling Public Health Survey Data using a Spatial Microsimulation Approach.” Spatial and Spatio-temporal Epidemiology 26: 153-164. DOI: 10.1016/j.sste.2017.10.001
   

• Koh, K., Grady, S. C., Vojnovic, I. and Darden, J. (2018) “Impacts of Federally Funded State Obesity Programs on Adult Obesity Prevalence in the United States, 1998-2010.” Public Health Reports 133(2): 169-176. DOI: 10.1177/0033354917751128 (IF2018=2.039).
   

• Koh, K., Reno, R. and Hyder, A. (2018) “Designing an Agent-based Model using Group Model Building: Application to Food Insecurity Patterns in a U.S. Midwestern Metropolitan City.” Journal of Urban Health 95(2): 278-289. DOI: 10.1007/s11524-018-0230-1 (IF2018=2.154).

  2015  

• Koh, K., Grady, S. C. and Vojnovic, I. (2015) “Using Simulated Data to Investigate the Spatial Patterns of Obesity Prevalence at the Census Tract Level in Metropolitan Detroit.” Applied Geography 62: 19-28. DOI: 10.1016/j.apgeog.2015.03.016 (IF2018=3.068).