Prior Knowledge-enhanced Spatio-temporal Epidemic Forecasting

TL;DR

This paper introduces STOEP, a hybrid spatio-temporal epidemic forecasting framework that enhances prediction accuracy by integrating implicit priors, explicit expert knowledge, and dynamic regional dependency modeling.

Contribution

The paper presents a novel hybrid framework combining implicit and explicit priors with dynamic dependency adjustment for improved epidemic forecasting accuracy.

Findings

STOEP outperforms baseline models by 11.1% in RMSE on COVID-19 and influenza datasets.

The system has been successfully deployed at a provincial CDC in China.

Extensive experiments validate the effectiveness of the proposed components.

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.