Welcome to my website!
Hi! My name is Jong-Hoon Kim. I am a theoretical epidemiologist who uses mathematical, statistical, and machine-learning models to study infectious disease epidemiology. My work involves developing theories about transmission, learning from imperfect data, and assessing intervention strategies to improve public health. My research has explored typhoid fever, non-typhoidal Salmonella, cholera, and COVID-19.
Research interests
My research focuses on using disease models to generate actionable insights for controlling infectious diseases. I study which details matter for vaccination strategies for cholera and typhoid fever in different places and at different times. I am also interested in how human behavior can enhance or hinder disease control, and how mathematical models, statistical inference, and machine learning can inform one another.
This blog is also my open notebook: a place to work through ideas, share reproducible examples, and invite questions and alternative approaches.
Selected work
- Published research: Mapping the incidence rate of typhoid fever in sub-Saharan Africa. A collaborative study connecting spatial evidence to disease burden. Explore the code.
- Outbreak inference: What can a handful of exported cases tell us? A reconstruction of early outbreak estimates, with explicit assumptions, uncertainty intervals, and worked R examples.
- Methods tutorial: How should a model learn as new data arrive? An introduction to the Ensemble Kalman Filter with an implementation in R.
Browse publications for more context.
Recent writing
- From Outbreak Size to Transmissibility: Imperial’s Third COVID-19 Report Oct 2026
- From Exported Cases to Outbreak Size: Imperial’s First Two COVID-19 Reports Sep 2026
- Nash Equilibrium, Starting Simple Sep 2026
Get in touch
Research ideas, difficult datasets, potential collaborations, and research roles are all welcome topics. Email me with the question, the context, and what you would like to explore, or connect on LinkedIn.