Prior Knowledge-enhanced Spatio-temporal Epidemic Forecasting

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.

Effective and Acceptable Eco-Driving Guidance for Human-Driving Vehicles: A Review

Ecodriving guidance includes courses or suggestions for human drivers to improve driving behaviour, reducing energy use and emissions. This paper presents a systematic review of existing eco-driving guidance studies and identifies challenges to tackle in the future. A standard agreement on the guidance design has not been reached, leading to difficulties in designing and implementing eco-driving guidance for human drivers. Both static and dynamic guidance systems have a great variety of guidance results. In addition, the influencing factors, such as the suggestion content, the displaying methods, and drivers socio-demographic characteristics, have opposite effects on the guidance result across studies, while the reason has not been revealed. Drivers motivation to practice eco behaviour, especially long-term, is overlooked. Besides, the relationship between users acceptance and system effectiveness is still unclear. Adaptive driving suggestions based on drivers habits can improve the effectiveness, while this field is under investigation.