Prior Knowledge-enhanced Spatio-temporal Epidemic Forecasting

TL;DR

The proposed Spatio-Temporal priOr-aware Epidemic Predictor (STOEP), a novel hybrid framework that integrates implicit spatio-temporal priors and explicit expert priors, outperforms the best baseline by 11.1% in RMSE.

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.

Figures (10)

• Figure 1: (a) Illustrations of hybrid models; (b) Insensitivity to weak epidemic signals.; (c) Over-simplified adjacency relation; (d) Unstable parameter estimation.
• Figure 2: Framework of proposed epidemic forecasting model STOEP.
• Figure 3: Ablation on the COVID-19 and Flu dataset.
• Figure 4: Different number of patterns $P$.
• Figure 5: Predicted daily confirmed cases of COVID-19 with horizon = 7.

Theorems & Definitions (3)

• Definition 1: Daily Confirmed Cases
• Definition 2: Historical Observations
• Definition 3: Historical Mobility Data