Academic profile
Research
Completed M.Sc. work, the evidence behind it, and the doctoral directions ahead — with documented outputs kept distinct from forward-looking plans.
Themes
Completed M.Sc. research, ongoing doctoral work, and the longer-term research agenda grouped by theme.
Recent M.Sc. work centered on multimodal forecasting, non-stationary time-series analysis, and early-warning-oriented modelling for infectious diseases, especially respiratory disease activity and influenza-related scenarios.
A central line of work studies co-circulation patterns, synchronous and lagged relationships, and feature extraction for influenza-related signals in China, using interpretable time-series and multiscale analytical methods.
Recent work on the 2025 Foshan chikungunya outbreak combines ODE and Petri Net formulations to compare transmission interpretation, intervention effects, and uncertainty under data-limited conditions.
My broader agenda is to link macroscopic epidemic patterns with microscopic viral evolution, integrating epidemiological signals, phylodynamic evidence, and early-warning models within a single framework.
Current focus
My research combines completed M.Sc. modelling work with the next doctoral direction in epidemiology and health statistics.
My current doctoral-stage work centres on multimodal infectious-disease forecasting and early warning, including the MAESTRO framework for respiratory-disease activity, influenza co-circulation analysis, and dual-framework ODE / Petri Net modelling for mechanistic-modelling outbreak settings such as the 2025 Foshan chikungunya outbreak. Alongside these lines, I maintain an infectious-disease intelligence platform for data collection and analytical support. The longer-term agenda links macroscopic epidemic patterns with microscopic viral evolution, integrating epidemiological signals with phylodynamic evidence toward a coherent early-warning framework.
Methodology
Methodologically, I connect data sources, model structure, interpretation, and reproducible implementation, with strong baselines, sensitivity analysis, and transparent reporting of model limitations.
I work with surveillance records, environmental information, behavioral or news-derived signals, and, in longer-term plans, genomic data, aiming to turn heterogeneous public-health information into coherent analytical inputs.
My current work combines decomposition-based forecasting, state-space reasoning, and mechanistic models such as ODEs and Petri Nets so that predictive results remain tied to epidemiological interpretation.
I emphasise reproducible pipelines, strong baselines, sensitivity analysis, and transparent reporting of model limitations.