Prior Knowledge-enhanced Spatio-temporal Epidemic Forecasting

• URL: http://arxiv.org/abs/2602.22270v1
• Date: Wed, 25 Feb 2026 07:52:11 GMT
• Title: Prior Knowledge-enhanced Spatio-temporal Epidemic Forecasting
• Abstract summary: New framework integrates implicit priors and expert priors to predict epidemics.<n>Experiments on real-world datasets demonstrate STOEP outperforms best baseline CO by 11.1% in RMSE.<n>System has been deployed at one provincial CDC in China to facilitate downstream applications.
• Score: 15.779777731878228
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spatio-temporal epidemic forecasting is critical for public health management, yet existing methods often struggle with insensitivity to weak epidemic signals, over-simplified spatial relations, and unstable parameter estimation. To address these challenges, we propose the Spatio-Temporal priOr-aware Epidemic Predictor (STOEP), a novel hybrid framework that integrates implicit spatio-temporal priors and explicit expert priors. STOEP consists of three key components: (1) Case-aware Adjacency Learning (CAL), which dynamically adjusts mobility-based regional dependencies using historical infection patterns; (2) Space-informed Parameter Estimating (SPE), which employs learnable spatial priors to amplify weak epidemic signals; and (3) Filter-based Mechanistic Forecasting (FMF), which uses an expert-guided adaptive thresholding strategy to regularize epidemic parameters. Extensive experiments on real-world COVID-19 and influenza datasets demonstrate that STOEP outperforms the best baseline by 11.1% in RMSE. The system has been deployed at one provincial CDC in China to facilitate downstream applications.

Related papers

• Beyond Time Series: Spatial Reasoning for Epidemic Forecasting via Multimodal Learning [50.948271066372826]
  We introduce a spatially structured multimodal epidemic forecasting setting that integrates region-level temporal surveillance data with spatially localized auxiliary signals.<n>We evaluate our approach on real-world COVID-19, influenza, and influenza-like illness (IIL) forecasting tasks under realistic real-time evaluation protocols.
  arXiv  Detail & Related papers  (2026-06-20T17:58:17Z)
• SpatialEpiBench: Benchmarking Spatial Information and Epidemic Priors in Forecasting [14.31847187460321]
  Accurate forecasting is crucial for public health response, resource allocation, and intervention.<n>Because epidemics unfold across regions, realistic epidemiological methods are natural candidates for improving forecasts.<n>Despite growing interest in spatial information, no standardized benchmark exists, and current evaluations often use simple train chronological-test that do not reflect real-time forecasting practice.
  arXiv  Detail & Related papers  (2026-05-07T16:31:43Z)
• EviCare: Enhancing Diagnosis Prediction with Deep Model-Guided Evidence for In-Context Reasoning [62.61394722212386]
  EviCare is an in-context reasoning framework that integrates deep model guidance into large language models.<n>It consistently outperforms both LLM-only and deep model-only baselines in experiments on two real-world EHR benchmarks.
  arXiv  Detail & Related papers  (2026-04-12T04:35:14Z)
• PREBA: Surgical Duration Prediction via PCA-Weighted Retrieval-Augmented LLMs and Bayesian Averaging Aggregation [51.96735866702332]
  PREBA is a retrieval-augmented framework that integrates PCA-weighted retrieval and Bayesian averaging aggregation.<n> PREBA significantly improves performance-for instance, reducing MAE by up to 40% and raising R2 from -0.13 to 0.62 over zero-shot inference.
  arXiv  Detail & Related papers  (2026-02-27T07:19:23Z)
• Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning [70.56067503630486]
  We argue that sixth-generation (6G) intelligence is not fluent token prediction but calibrated the capacity to imagine and choose.<n>We show that WM-MS3M cuts mean absolute error (MAE) by 1.69% versus MS3M with 32% fewer parameters and similar latency, and achieves 35-80% lower root mean squared error (RMSE) than attention/hybrid baselines with 2.3-4.1x faster inference.
  arXiv  Detail & Related papers  (2025-11-04T17:22:22Z)
• MEET-Sepsis: Multi-Endogenous-View Enhanced Time-Series Representation Learning for Early Sepsis Prediction [50.74903497515021]
  Sepsis is a life-threatening infectious syndrome associated with high mortality in intensive care units (ICUs)<n>Early and accurate sepsis prediction (SP) is critical for timely intervention, yet remains challenging due to subtle early manifestations and rapidly escalating mortality.<n>This paper introduces a Multi-Endogenous-view Representation Enhancement (MERE) mechanism to construct enriched feature views, coupled with a Cascaded Dual-convolution Time-series Attention (CDTA) module for temporal representation learning.<n>The proposed MEET-Sepsis framework achieves competitive prediction accuracy using only 20% of the ICU monitoring time required by SOTA methods, significantly advancing
  arXiv  Detail & Related papers  (2025-10-13T13:38:29Z)
• EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting [2.9557992468454533]
  Large Language Models (LLMs) have demonstrated effectiveness as foundation models for domain-specific tasks.<n>Recent advances in EpiLLM outperforms existing baselines on real-world COVID-19 datasets.
  arXiv  Detail & Related papers  (2025-05-19T05:53:25Z)
• Epidemic-guided deep learning for spatiotemporal forecasting of Tuberculosis outbreak [0.0]
  We propose an Epidemic-Guided Deep Learning (EGDL) approach that fuses mechanistic epidemiological principles with advanced deep learning techniques.<n>Our framework is built upon a networked Susceptible-Infectious-Recovered (MN-SIR) model augmented with a saturated incidence rate and graph Laplacian diffusion.<n> Experiments conducted on TB incidence data from 47 prefectures in Japan and 31 provinces in mainland China demonstrate that our approach delivers robust and accurate predictions.
  arXiv  Detail & Related papers  (2025-02-15T12:39:42Z)
• GST-UNet: A Neural Framework for Spatiotemporal Causal Inference with Time-Varying Confounding [46.46135774964818]
  Estimating causal effects from data is essential in public health, environmental science, and policy evaluation.<n>We introduce GST-UNet, a neural framework that combines a U-Net-basedtemporal encoder with regression-based iterative G-mputation.<n>We validate its effectiveness in synthetic experiments and in a real-world analysis of wildfire smoke exposure and respiratory hospitalizations during California Camp Fire.
  arXiv  Detail & Related papers  (2025-02-07T19:56:01Z)
• Towards the efficacy of federated prediction for epidemics on networks [5.719600613434056]
  We develop a general privacy-level framework for node-level prediction on epidemic networks based on federated learning (FL)<n>This study offers practical insights into the efficacy of FL scenario in epidemic management.
  arXiv  Detail & Related papers  (2024-12-03T04:37:28Z)
• A Strategy Transfer and Decision Support Approach for Epidemic Control in Experience Shortage Scenarios [0.0]
  Strategy Transfer and Decision Support Approach (STDSA) is proposed based on the profile similarity evaluation. This study will provide new insights into preventing and controlling epidemics in regions that lack experience.
  arXiv  Detail & Related papers  (2024-04-10T02:25:27Z)
• Data-Centric Epidemic Forecasting: A Survey [56.99209141838794]
  This survey delves into various data-driven methodological and practical advancements. We enumerate the large number of epidemiological datasets and novel data streams that are relevant to epidemic forecasting. We also discuss experiences and challenges that arise in real-world deployment of these forecasting systems.
  arXiv  Detail & Related papers  (2022-07-19T16:15:11Z)
• STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological Regularization [76.57716281104938]
  We develop a tensor method to predict the evolution of epidemic trends for many regions simultaneously. STELAR enables long-term prediction by incorporating latent temporal regularization through a system of discrete-time difference equations. We conduct experiments using both county- and state-level COVID-19 data and show that our model can identify interesting latent patterns of the epidemic.
  arXiv  Detail & Related papers  (2020-12-08T21:21:47Z)
• An Optimal Control Approach to Learning in SIDARTHE Epidemic model [67.22168759751541]
  We propose a general approach for learning time-variant parameters of dynamic compartmental models from epidemic data. We forecast the epidemic evolution in Italy and France.
  arXiv  Detail & Related papers  (2020-10-28T10:58:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.