arXiv Machine Learning

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.

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 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

Jun 16

Forecasting Bacterial Antimicrobial Resistance Trends Using Machine Learning on WHO GLASS Surveillance Data: A Retrieval-Augmented Generation Approach for Policy Decision Support

arXiv:2602. 22673v2 Announce Type: replace Abstract: Background: Antimicrobial resistance (AMR) is a global health threat.

By Md Tanvir Hasan Turja

rag safety

arXiv AI

Jun 6

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.

By Yiming Lu, Sihang Zeng, Zhengxu Tang, Max Lau, Fei Liu, Wei Jin

llms agents

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

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 Machine Learning

Jul 23

Air Quality Arena: A Large-Scale Multi-Region Ground Monitoring Dataset and Benchmark for Air Quality Forecasting with Time-Series Foundation Models

arXiv:2607. 19381v1 Announce Type: new Abstract: Air pollution causes an estimated 7.

By Rishi Bharadwaj, Manik Gupta, Pandarasamy Arjunan

multimodal benchmarks

Hugging Face Trending Papers

Jun 4

Benchmarking Counterfactual Prediction in Epidemic Time Series with Time-Varying Interventions

Deep learning has enabled significant advances in time-series causal inference, yet progress remains constrained by the lack of realistic benchmarks with observable counterfactual outcomes. Existing datasets either rely on real-world observations without ground-truth counterfactuals or on simplified simulations that fail to capture complex causal dynamics.

agents benchmarks

arXiv Machine Learning

Jun 5

Benchmarking Counterfactual Prediction in Epidemic Time Series with Time-Varying Interventions

arXiv:2606. 05692v1 Announce Type: new Abstract: Deep learning has enabled significant advances in time-series causal inference, yet progress remains constrained by the lack of realistic benchmarks with observable counterfactual outcomes.

By Wenhao Mu, Facundo Yan, Anik Mumssen, Marisa Eisenberg, Alexander Rodr\'iguez

agents benchmarks

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

Jul 24

Improving Access to Essential Medicines via Decision-Aware Machine Learning

arXiv:2607. 20542v1 Announce Type: cross Abstract: A critical challenge in healthcare systems in low- and middle-income countries (LMICs) is the efficient and equitable allocation of scarce resources, particularly essential medicines.

By Angel Tsai-Hsuan Chung, Jatu Abdulai, Patrick Bayoh, Lawrence Sandi, Francis Smart, Hamsa Bastani, Osbert Bastani

arXiv AI

Jun 2

Hybrid Probabilistic Forecasting of Under-Five Malaria Admissions in Ghana: A Gaussian Process Regression with Holt-Winters Smoothing

arXiv:2606. 00834v1 Announce Type: cross Abstract: Accurate malaria forecasting remains a major challenge in sub-Saharan Africa, where strong seasonality, reporting uncertainty, and non-stationary transmission dynamics reduce the reliability of conventional models.

By T. Ansah-Narh, Y. Asare Afrane, J. Bremang Tandoh

Related stories