arXiv:2606. 19560v1 Announce Type: new Abstract: Seasonal influenza infects millions of people and causes substantial morbidity and mortality in the United States each year, making accurate short-term forecasting a core public-health need.
By Alireza Jafari, Judy Fox, Geoffrey C. Fox, Madhav Marathe, Aniruddha Adiga
arXiv:2601. 20771v2 Announce Type: replace-cross Abstract: Accurate forecasting of infectious disease incidence is critical for public health planning and timely intervention.
By Zacharias Komodromos, Kleanthis Malialis, Artemis Kontou, Panayiotis Kolios
arXiv:2602. 06323v2 Announce Type: replace Abstract: Epidemiological forecasting from surveillance data is a hard problem and hybridizing mechanistic compartmental models with neural models is a natural direction.
By Yiqi Su, Ray Lee, Jiaming Cui, Naren Ramakrishnan
arXiv:2605. 26704v2 Announce Type: replace-cross Abstract: Epidemic forecasting faces a fundamental challenge: human behavior dynamically responds to disease spread, creating feedback loops that induce distribution shifts at policy intervention points.
By Haochun Wang, Sendong Zhao, Jingbo Wang, Yanrui Du, Ting Liu, Bing Qin
The paper introduces TERN, a forecasting model that uses a delta‑rule fast‑weight memory with channel‑wise decay and learned erasure gates, combined with a seasonal reference and online adaptation. It addresses challenges in influenza forecasting such as limited seasonal data, shifting wave patterns, and misleading information after peaks. On three Cola‑GNN influenza benchmarks, TERN outperformed existing epidemic graph models and general forecasters, matching or exceeding seasonal references and demonstrating the value of its memory component.
By Shunya Nagashima, Yuta Funayama
arXiv:2606. 05513v1 Announce Type: new Abstract: Epidemic LLM forecasters are usually trained and evaluated as static supervised models, whereas operational pandemic forecasting is a streaming process in which labels arrive after predictions and disease regimes shift over time.
By Yiming Lu, Sihang Zeng, Zhengxu Tang, Max Lau, Fei Liu, Wei Jin