3
First-author studies
Academic homepage
Hong Liu
Infectious Disease Forecasting Spatiotemporal Dynamics Viral Evolution
I am a Ph.D. student in Epidemiology and Health Statistics at the School of Public Health, Xiamen University (admitted 2026, supervised by Prof. Tianmu Chen). I hold an M.Sc. in Computer Science and Technology from Macau University of Science and Technology and completed undergraduate training at Xiamen University. My research focuses on infectious disease forecasting, influenza co-circulation analysis, outbreak modelling, and integrating epidemic dynamics with viral evolution.
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
8
Public repositories
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 Computer Science and 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
Representative first-author and lead work appears first, so the landing page speaks through documented evidence before secondary context.
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. In the documented evaluation context, the reported R² reached 0.956 on a 10-year Hong Kong influenza dataset.
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 rather than a long inventory of activities.
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.
Small-sample 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 small-sample conditions.
Epidemic dynamics and viral evolution
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.
Now
Current snapshot
A concise summary of the current research stage, active methods, and outward-facing profiles.
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.
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 forecasting framework for respiratory disease activity and early-warning-oriented time-series analysis.
Research framework connecting multimodal respiratory disease forecasting with documented evaluation and paper output.
ForecastingEarly warningMultimodal modellingTime series
Research code
GitHub →ODE-Petri-Chikungunya
Research workflow comparing ODE and Petri Net perspectives for the 2025 Foshan chikungunya outbreak.
Dual-model outbreak analysis workflow linking mechanistic interpretation, intervention phases, and manuscript development.
Mechanistic modellingODEPetri NetOutbreak analysis
Platform
Infectious disease intelligence platform
Crawler, database, ETL, and visualization workflow for infectious-disease news collection, monitoring, and research-oriented data support.
Operational data pipeline for infectious-disease intelligence, from collection and cleaning to monitoring-oriented visualization.
ETLCrawlerDatabaseVisualization
Software
GitHub →bibverify
BibTeX validation and metadata completion tool for DOI, author, year, deduplication, and citation normalization workflows.
Utility for reference verification, metadata completion, deduplication, and citation cleanup across writing workflows.
Bibliographic toolingDOI validationMetadata completion
Notes
Selected highlights
Short evidence-led notes that reinforce the main outputs without repeating the full narrative.
Developed the MAESTRO multimodal forecasting framework for respiratory disease activity; in the documented evaluation context, the reported R² reached 0.956 on a 10-year Hong Kong influenza dataset.
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 co-infection 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
Navigate the site
Jump to fuller research themes, publication records, CV material, and project context.