arXiv Machine Learning

Hybrid SEIR Temporal Fusion Transformer Framework with SHAP Analysis for Interpretable Pandemic Forecasting

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

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 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

llms agents

arXiv Machine Learning

Aug 5

Cross-Country Learning for National Infectious Disease Forecasting Using European Data

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 AI

Oct 16

Prospective multi-pathogen disease forecasting using autonomous LLM-guided tree search

The paper introduces Empirical Research Assistance (ERA), an autonomous system that employs Large Language Model‑guided tree search to create, evaluate, and refine executable forecasting software for infectious diseases. In a real‑time evaluation during the 2025‑26 U.S. respiratory season, ERA produced diverse models for influenza, COVID‑19, and RSV, and an ensemble of these models outperformed the CDC’s gold‑standard forecast ensembles across all three pathogens. ERA also demonstrated robustness in data‑scarce RSV scenarios and showed that higher‑tier language models and specific optimization metrics can improve forecast accuracy.

By Sarah Martinson, Michael P. Brenner, Martyna Plomecka, Brian P. Williams, Nicholas G. Reich, Zahra Shamsi

llms agents

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 Computation and Language

Oct 5

EpiWorld: Grounding LLM Policy Agents in Epidemiological World Models

arXiv:2610.02744v1 Announce Type: new Abstract: Epidemic intervention policies are textual artefacts that human decision-makers interpret, justify, and revise through natural language, making large l...

By Zeeshan Memon, Yiqi Su, Kai Shu, Naren Ramakrishnan, Liang Zhao

llms agents safety

arXiv Machine Learning

Jul 7

Deep Generative Spatiotemporal Engression for Probabilistic Forecasting of Epidemics

arXiv:2603. 07108v2 Announce Type: replace-cross Abstract: Accurate and reliable forecasting of epidemic incidences is critical for public health preparedness, yet it remains a challenging task due to complex nonlinear temporal dependencies and heterogeneous spatial interactions.

By Rajdeep Pathak, Tanujit Chakraborty

benchmarks

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 AI

Aug 18

Foresight-England: Development of a National-Scale Generative AI Model of Electronic Health Records for Medical Event Prediction across the COVID-19 Pandemic

arXiv:2608. 16273v1 Announce Type: cross Abstract: Foresight-England (Foresight-E) is the first national-scale generative foundation model of electronic health records (EHRs), developed as a research pilot strictly for COVID-19 research.

By Simon Ellershaw, Christopher Tomlinson, Zeljko Kraljevic, Spiros Denaxas, Harry Hemingway, Cathie Sudlow, Angela M. Wood, Anoop D. Shah, Richard Dobson

llms benchmarks

arXiv Machine Learning

Jul 30

Hierarchical Spatio-Temporal Transformer for Coherent Emergency Department Forecasting

arXiv:2607. 27106v1 Announce Type: new Abstract: Emergency Departments (EDs) are critical access points in healthcare systems, yet they face persistent pressure from unpredictable patient demand, seasonal surges, and non-urgent visits.

By Filipa Lino, B\'arbara Tavares, Carlos Santiago, Cl\'audia Soares, Manuel Marques

llms

arXiv Machine Learning

Aug 10

EpiFlow: A framework for improving the utility of wastewater signals for disease forecasting

arXiv:2608. 06671v1 Announce Type: new Abstract: Wastewater-based surveillance is an effective tool for disease monitoring and can provide early warning of outbreaks.

By Aniruddha Adiga, Jingyuan Chou, Gursharn Kaur, Andrew Warren, Srinivasan Venkatramanan, Baltazar Espinoza, Bryan Lewis, Justin Crow, Alexandra Lorentz, Rekha Singh, Madhav Marathe

Related stories