arXiv AI

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.

arXiv AI

Jun 2

Bayesian Inference of Nonlinear Malaria Dynamics in Ghana via an Ensemble Markov Chain Monte Carlo Sampler

arXiv:2606. 00783v1 Announce Type: cross Abstract: Reliable quantification of malaria dynamics in sub-Saharan Africa is hindered by short, noisy, and spatially heterogeneous surveillance records.

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

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

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 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 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 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 21

Lightweight Wrappers for Adapting Time Series Foundation Models to Regional Drought Forecasting

arXiv:2607. 17511v1 Announce Type: new Abstract: Large \emph{Time Series Foundation Models} (TSFMs) demonstrate strong zero-shot forecasting capabilities across diverse domains.

By Wentao Gao, Jiuyong Li, Lin Liu, Thuc Duy Le, Jixue Liu, Yanchang Zhao, Yun Chen

fine-tuning

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

Jun 2

Atmospheric Predictability Beyond 30 Days with Machine Learning

arXiv:2504. 20238v2 Announce Type: replace-cross Abstract: Atmospheric predictability research has long held that rapid error growth at small spatial scales imposes an intrinsic limit of roughly two weeks on deterministic weather forecast skill.

By P. Trent Vonich, Gregory J. Hakim

arXiv AI

Jul 28

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS

arXiv:2602. 16579v2 Announce Type: replace-cross Abstract: Reliable global streamflow forecasting is essential for flood preparedness and water resource management, yet data-driven models often suffer from a performance gap when transitioning from historical reanalysis to operational forecast products.

By Maria Luisa Taccari, Kenza Tazi, Ois\'in M. Morrison, Andreas Grafberger, Juan Colonese, Corentin Carton de Wiart, Christel Prudhomme, Cinzia Mazzetti, Matthew Chantry, Florian Pappenberger

fine-tuning 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

Jun 26

CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting

arXiv:2510. 20769v2 Announce Type: replace-cross Abstract: Accurate medium-range precipitation forecasting is essential for hydrometeorological risk management but remains challenging for both numerical weather prediction (NWP) systems and data-driven models.

By Tianyi Xiong, Haonan Chen, Kelly Mahoney, Jingyin Tang, Tim Smith, Janice Bytheway

llms rag safety

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