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- Dual-model framework for chikungunya transmission modelling: ODE and Petri Net analysis of the 2025 Foshan outbreak. BMC Public Health 26 (2026). DOI 10.1186/s12889-026-28110-9.
- Spatiotemporal instability of influenza seasonality during viral co-circulation. npj Systems Biology and Applications (2026). DOI 10.1038/s41540-026-00729-9.
- MAESTRO (Multi-modal Adaptive Estimation for Temporal Respiratory Disease Outbreak). arXiv preprint arXiv:2509.08578 (2025); reports R² = 0.956 on an 11-year Hong Kong influenza dataset (excluding the COVID-19 period).
💡 My Passion & What Drives Me
I am currently an M.Sc. student specializing in infectious disease forecasting, spatiotemporal dynamics, and viral evolution, with an interdisciplinary background in public health and preventive medicine. I am deeply passionate about bridging the gap between data science, machine learning, and epidemiology.
What drives me every day is the belief that by deciphering complex health data and building robust prediction models, we can provide earlier warnings for public health challenges. I am a strong advocate for Open Science and making research accessible to everyone.
🛠️ What I Do
Through my research and GitHub repositories, I work on translating complex epidemiological models, non-stationary time-series analyses, and multimodal forecasting algorithms into open-source, reproducible code.
Whether it's analyzing the co-circulation of influenza or modeling small-sample outbreaks, I strive to share my workflows and tools to help fellow researchers and developers in the community. Active research engineering also lives in epic-intel-harness (deterministic-safety outbreak-intelligence agent benchmark), hanta-scientific-data-resource (DVC + Snakemake ResearchOps for hantavirus / ebola), openscience (evidence-first research workflow harness with a science-epi skill), and mcodeforlegal (Mainland-China-first legal workflow plugins for Claude Code).
🌱 How Your Contributions Make a Difference
Balancing academic research and open-source development comes with its own set of challenges and costs. Your generous sponsorship will directly help with:
- Computing Resources: Covering server and GPU costs needed to train machine learning models and process large-scale epidemiological datasets.
- Research & Maintenance: Supporting data acquisition, publication costs, and the time required to continuously maintain and update my open-source repositories.
- Fueling the Late Nights: Buying me a cup of coffee during those long nights of debugging code and writing manuscripts.
🤝 Let's Connect
Science and open-source thrive on collaboration. Whether you've cited my papers, used my code, or simply share an interest in infectious disease modeling, I deeply appreciate your presence here.
Every contribution, no matter the size, reminds me that I am not working in isolation. Thank you for supporting my journey in advancing public health research through data and code.