A selection of research spanning infectious disease dynamics, environmental health, clinical prediction, and hybrid statistical–AI methods.
Investigating measles transmission patterns under migration at the subnational level at Ahmedabad University, through a Catalyst Grant supported by the Measles Analytics Hub, Imperial College London.
Studying post-pandemic influenza subtype replacement and seasonal timing in Hong Kong using Bayesian hierarchical and state-space models. Ongoing work explores hybrid forecasting with LSTM and Transformer architectures.
Modelling nonlinear environmental effects on COVID-19 transmission in Daegu and Seoul using GAMs and machine learning to investigate region-specific risk drivers.
Predicting diabetic lower limb amputation risk with machine learning and interpreting clinical predictors with SHAP analysis.
A hybrid anomaly-detection and Markov-chain framework for dynamic malware classification, validated with XGBoost and multivariate outlier techniques.
Selected peer-reviewed work.
Research continues.
Bayesian Evidence for Post-Pandemic Influenza Subtype Replacement and Phenological Reorganization in Hong Kong
Manuscript submitted to BMC Medicine; listed as under review in the supplied CV.
Understanding Clinical Predictors of Diabetic Wound Healing Using Integrated Analytical Approaches
Manuscript in preparation.