3
First-author studies
Academic homepage
Hong Liu
Infectious Disease Forecasting Spatiotemporal Dynamics Mechanistic Outbreak Modelling
I am an incoming Ph.D. student in Epidemiology and Health Statistics at the School of Public Health, Xiamen University (starting September 2026, supervised by Prof. Tianmu Chen). I hold an M.Sc. in Intelligent Technology from Macau University of Science and Technology (2024–2026) and a B.Med. in Preventive Medicine with a minor in Statistics from Xiamen University. My research focuses on infectious-disease forecasting, influenza co-circulation, outbreak modelling, and integrating transmission dynamics with viral evolution. On the engineering side, I also maintain [openscience](https://github.com/Hylouis233/openscience) (an evidence-first research workflow harness with a `science-epi` skill) and [mcodeforlegal](https://github.com/Hylouis233/mcodeforlegal) (a Mainland-China-first legal workflow plugin pack for Claude Code), and continue to develop [epic-intel-harness](https://github.com/Hylouis233/epic-intel-harness) and [hanta-scientific-data-resource](https://github.com/Hylouis233/hanta-scientific-data-resource) as shared infrastructure for outbreak-intelligence and viral-outbreak research.
Current work links multimodal forecasting, epidemic dynamics, and interpretable analytical workflows, with longer-term research extending toward viral evolution and early-warning systems.
7
Published papers
14
Public repositories
108
Cumulative stars
Profiles and quick routes
Academic identity
Current
Xiamen University · Ph.D. student in Epidemiology and Health Statistics (2026–present)
Master’s degree
Macau University of Science and Technology · M.Sc. in Intelligent Technology, Faculty of Innovation Engineering
Undergraduate
Xiamen University · B.Med. in Preventive Medicine (School of Public Health) & B.Sc. in Mathematical Statistics (Wang Yanan Institute for Studies in Economics)
Evidence
Selected research outputs
First-author publications, peer-reviewed papers, and the open-source code that supports them.
10
Research outputs
3
First-author studies
5
Honors and awards
Preprint 2025
Multi-modal Adaptive Estimation for Temporal Respiratory Disease Outbreak
First-author MAESTRO study; reports R² = 0.956 on over 11 years of Hong Kong influenza data (excluding COVID-19).
Published 2026
Dual-Model Framework for CHIKV Transmission Modeling: ODE and Petri Net Analysis of the 2025 Foshan Outbreak
First-author dual-framework outbreak modelling study for the 2025 Foshan chikungunya outbreak, published in BMC Public Health on 29 June 2026. An arXiv preprint (arXiv:2512.12577) is also openly accessible.
Published 2026
Spatiotemporal instability of influenza seasonality during viral co-circulation
First-author npj Systems Biology and Applications article. Accepted on 17 April 2026 and published online on 04 May 2026.
Research agenda
Research focus
Current work is organized around a compact set of research themes.
Infectious disease forecasting and early warning
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.
Influenza co-circulation and spatiotemporal dynamics
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.
Mechanistic Outbreak Modelling
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.
Epidemic dynamics and viral evolution
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.
Now
Current snapshot
A concise summary of the current research stage, active methods, and outward-facing profiles.
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.
Infectious disease forecasting and early warningEarly warningInfluenza co-circulationODE / Petri Net modellingMultimodal data fusionViral evolution
Tooling
Research software and analytical systems
Software artifacts supporting modelling, forecasting, data engineering, and day-to-day research workflows.
Research code
GitHub →MAESTRO
Multimodal time-series forecasting framework for respiratory disease activity and early-warning scenarios, reporting R² = 0.956 on over 11 years of Hong Kong influenza data (excluding COVID-19).
Research framework connecting multimodal respiratory-disease forecasting with transparent model-evaluation and published output.
ForecastingEarly warningMultimodal modellingTime series
Research code
GitHub →ODE-Petri-Chikungunya
ODE and Petri Net dual-model research workflow for the 2025 Foshan chikungunya outbreak, published in BMC Public Health 26 (2026).
Dual-model outbreak analysis workflow linking mechanistic interpretation, intervention phases, and manuscript development.
Mechanistic modellingODEPetri NetOutbreak analysis
Research code
GitHub →MultiCoPat
Multi-region and multi-window influenza co-circulation analysis workflow, published in npj Systems Biology and Applications (2026).
Analytical pipeline supporting work on influenza co-circulation, multi-subtype interaction, and non-stationary time-series modelling.
InfluenzaCo-circulationPredictionTime series
Software
GitHub →bibverify
Cross-platform BibTeX verification and metadata-completion tool for researchers and AI assistants. CLI, Python API, MCP server, and explainable multi-source matching. Published on PyPI.
Utility for reference verification, metadata completion, deduplication, and citation cleanup across writing workflows.
Bibliographic toolingDOI validationMetadata completion
Notes
Selected highlights
Brief notes that place the outputs in their research context.
Developed the MAESTRO multimodal forecasting framework for respiratory disease activity; reported R² = 0.956 on over 11 years of Hong Kong influenza data (excluding COVID-19).
Built an ODE and Petri Net dual-model workflow for the 2025 Foshan chikungunya outbreak to compare intervention phases, transmission indicators, and sensitivity under small-sample conditions.
Designed an analytical pipeline for influenza co-circulation and subtype interaction signals, leveraging interpretable time-series decomposition and multiscale frequency-domain coupling patterns.
Independently developed and continuously maintained an infectious-disease news collection, database, and visualization platform that supports research-oriented data acquisition and monitoring workflows.
Site map
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