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
These themes summarize completed M.Sc. work and the doctoral directions I will continue at Xiamen University, while keeping documented outputs distinct from forward-looking research plans.
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 small-sample conditions.
My broader research narrative links macroscopic epidemic patterns with microscopic viral evolution, and the longer-term agenda extends toward connecting epidemiological signals, phylodynamic evidence, and future early-warning models within a coherent framework.
Current focus
The current phase connects completed M.Sc. modelling work, representative evidence, and the next doctoral stage in epidemiology and health statistics.
My current doctoral-stage work centers on multimodal infectious disease forecasting and early warning, including the MAESTRO framework for respiratory disease activity, influenza co-circulation and co-infection analysis, and dual-framework ODE/Petri Net modelling for small-sample 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 that offers reliable, multi-scale insights for public health preparedness and decision-making.
Methodology
Methodologically, I try to connect data sources, model structure, interpretation, and reproducible implementation while distinguishing documented evidence from forward-looking doctoral agenda items.
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 distinguish completed outputs, work under review, work in preparation, software artifacts, and forward-looking doctoral plans, so that the research narrative stays aligned with documented evidence.