Prior Knowledge-enhanced Spatio-temporal Epidemic Forecasting

Ruan, Sijie; Li, Jinyu; Wei, Jia; Xu, Zenghao; Bao, Jie; Xu, Junshi; Qiu, Junyang; Wang, Shuliang; Wang, Xiaoxiao; Yuan, Hanning

doi:10.24963/ijcai.2026/764

Prior Knowledge-enhanced Spatio-temporal Epidemic Forecasting

Sijie Ruan, Jinyu Li, Jia Wei, Zenghao Xu, Jie Bao, Junshi Xu, Junyang Qiu, Shuliang Wang, Xiaoxiao Wang, Hanning Yuan

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Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. AI and Health. Pages 6868-6876.

https://doi.org/10.24963/ijcai.2026/764

Keywords

Public health: Public health
Timeseries prediction: Timeseries prediction

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.

Sijie Ruan, Jinyu Li, Jia Wei, Zenghao Xu, Jie Bao, Junshi Xu, Junyang Qiu, Shuliang Wang, Xiaoxiao Wang, and Hanning Yuan. 2026. Prior Knowledge-enhanced Spatio-temporal Epidemic Forecasting. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 6868–6876. https://doi.org/10.24963/ijcai.2026/764

@inproceedings{ijcai2026p764,
  title     = {Prior Knowledge-enhanced Spatio-temporal Epidemic Forecasting},
  author    = {Ruan, Sijie and Li, Jinyu and Wei, Jia and Xu, Zenghao and Bao, Jie and Xu, Junshi and Qiu, Junyang and Wang, Shuliang and Wang, Xiaoxiao and Yuan, Hanning},
  booktitle = {Proceedings of the Thirty-Fifth International Joint Conference on
               Artificial Intelligence, {IJCAI-26}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Diego Calvanese},
  pages     = {6868--6876},
  year      = {2026},
  month     = {8},
  note      = {AI and Health},
  doi       = {10.24963/ijcai.2026/764},
  url       = {https://doi.org/10.24963/ijcai.2026/764},
}

TY  - CPAPER
TI  - Prior Knowledge-enhanced Spatio-temporal Epidemic Forecasting
AU  - Ruan, Sijie
AU  - Li, Jinyu
AU  - Wei, Jia
AU  - Xu, Zenghao
AU  - Bao, Jie
AU  - Xu, Junshi
AU  - Qiu, Junyang
AU  - Wang, Shuliang
AU  - Wang, Xiaoxiao
AU  - Yuan, Hanning
T2  - Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2026
SP  - 6868
EP  - 6876
DO  - 10.24963/ijcai.2026/764
UR  - https://doi.org/10.24963/ijcai.2026/764
ER  -