{"type":"ScholarlyArticle","title":"Prior Knowledge-enhanced Spatio-temporal Epidemic Forecasting","authors":[{"name":"Sijie Ruan","slug":"sijie-ruan","given":"Sijie","family":"Ruan","affiliation":""},{"name":"Jinyu Li","slug":"jinyu-li","given":"Jinyu","family":"Li","affiliation":""},{"name":"Jia Wei","slug":"jia-wei","given":"Jia","family":"Wei","affiliation":""},{"name":"Zenghao Xu","slug":"zenghao-xu","given":"Zenghao","family":"Xu","affiliation":""},{"name":"Jie Bao","slug":"jie-bao","given":"Jie","family":"Bao","affiliation":""},{"name":"Junshi Xu","slug":"junshi-xu","given":"Junshi","family":"Xu","affiliation":""},{"name":"Junyang Qiu","slug":"junyang-qiu","given":"Junyang","family":"Qiu","affiliation":""},{"name":"Shuliang Wang","slug":"shuliang-wang","given":"Shuliang","family":"Wang","affiliation":""},{"name":"Xiaoxiao Wang","slug":"xiaoxiao-wang","given":"Xiaoxiao","family":"Wang","affiliation":""},{"name":"Hanning Yuan","slug":"hanning-yuan","given":"Hanning","family":"Yuan","affiliation":""}],"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 a provincial CDC in China to facilitate downstream applications.","doi":"10.24963/ijcai.2026/764","url":"https://ijcai.org/proceedings/2026/764","pdf":"https://ijcai.org/proceedings/2026/0764.pdf","markdown":"https://ijcai.org/proceedings/2026/764.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/764","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"AI and Health","track":"AI and Health","number":764,"pages":{"first":6868,"last":6876},"keywords":["Public health: Public health","Timeseries prediction: Timeseries prediction"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}