arXiv AI June 6, 2026 By Yiming Lu, Sihang Zeng, Zhengxu Tang, Max Lau, Fei Liu, Wei Jin

EpiEvolve: Self-Evolving Agents for Streaming Pandemic Forecasting under Regime Shifts

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 19

Understanding Key Features of Time Series Foundation Models from Epidemic Forecasting

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

llms

arXiv Machine Learning

22h ago

Continuous Evolution Pool: Taming Recurring Concept Drift in Online Time Series Forecasting

arXiv:2506. 14790v3 Announce Type: replace Abstract: Recurring concept drift is pervasive in real-world online time series, where the underlying data-generating process repeatedly alternates between a small set of regimes, most notably daily or seasonal cycles that dominate energy, traffic, and weather patterns, and is therefore a central obstacle to reliable long-horizon forecasting.

By Tianxiang Zhan, Ming Jin, Yuanpeng He, Yuxuan Liang, Shirui Pan

benchmarks

arXiv Machine Learning

Jul 30

TREA-Net: A Transferable Residual Epidemiological Adaptation Network for Dengue Incidence Forecasting

arXiv:2607. 26854v1 Announce Type: new Abstract: Accurate multi-week dengue forecasting supports timely vector-control interventions, outbreak preparedness, and healthcare resource allocation.

By Inesh Shukla, Madhurima Panja, Tanujit Chakraborty, Chittaranjan Hens

arXiv Machine Learning

Jul 1

How (Not) to Hybridize Neural and Mechanistic Models for Epidemiological Forecasting

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 AI

Jun 3

SL-BiLEM: Structured Learnable Behavior-in-the-Loop Epidemic Modeling for Forecasting and Policy Evaluation

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

benchmarks

arXiv Machine Learning

Jul 15

Auditable Context-Aware HFMD Forecasting with Structured LLM Agents

arXiv:2511. 23276v2 Announce Type: replace Abstract: Effective HFMD surveillance requires forecasts capturing both time-series patterns and contextual drivers such as school calendars, weather, and policy or surveillance reports.

By Joongwon Chae, Runming Wang, Chen Xiong, Gong Yunhan, Lian Zhang, Ji Jiansong, Dongmei Yu, Peiwu Qin

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Understanding Key Features of Time Series Foundation Models from Epidemic Forecasting

Continuous Evolution Pool: Taming Recurring Concept Drift in Online Time Series Forecasting

TREA-Net: A Transferable Residual Epidemiological Adaptation Network for Dengue Incidence Forecasting

How (Not) to Hybridize Neural and Mechanistic Models for Epidemiological Forecasting

SL-BiLEM: Structured Learnable Behavior-in-the-Loop Epidemic Modeling for Forecasting and Policy Evaluation

Auditable Context-Aware HFMD Forecasting with Structured LLM Agents