![[2507.00031] Enhancing Spatio-Temporal Forecasting with Spatial Neighbourhood Fusion:A Case Study on COVID-19 Mobility in Peru](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2507.00031] Enhancing Spatio-Temporal Forecasting with Spatial Neighbourhood Fusion:A Case Study on COVID-19 Mobility in Peru
Summary
This paper presents a novel Spatial Neighbourhood Fusion technique to enhance spatio-temporal forecasting of COVID-19 mobility in Peru, demonstrating improved predictive accuracy.
Why It Matters
Understanding human mobility is crucial for managing epidemic responses. This study offers a new method to improve forecasting accuracy, which can inform public health strategies during crises. The findings could be applied to other regions and contexts, enhancing global epidemic preparedness.
Key Takeaways
- Introduces Spatial Neighbourhood Fusion (SPN) to enhance mobility forecasting.
- Achieves up to 9.85% reduction in mean squared error (MSE) for forecasts.
- Demonstrates the importance of spatial smoothing in predictive modeling.
Computer Science > Machine Learning arXiv:2507.00031 (cs) COVID-19 e-print Important: e-prints posted on arXiv are not peer-reviewed by arXiv; they should not be relied upon without context to guide clinical practice or health-related behavior and should not be reported in news media as established information without consulting multiple experts in the field. [Submitted on 17 Jun 2025 (v1), last revised 25 Feb 2026 (this version, v2)] Title:Enhancing Spatio-Temporal Forecasting with Spatial Neighbourhood Fusion:A Case Study on COVID-19 Mobility in Peru Authors:Chuan Li, Jiang You, Hassine Moungla, Vincent Gauthier, Miguel Nunez-del-Prado, Hugo Alatrista-Salas View a PDF of the paper titled Enhancing Spatio-Temporal Forecasting with Spatial Neighbourhood Fusion:A Case Study on COVID-19 Mobility in Peru, by Chuan Li and 5 other authors View PDF HTML (experimental) Abstract:Accurate modeling of human mobility is critical for understanding epidemic spread and deploying timely interventions. In this work, we leverage a large-scale spatio-temporal dataset collected from Peru's national Digital Contact Tracing (DCT) application during the COVID-19 pandemic to forecast mobility flows across urban regions. A key challenge lies in the spatial sparsity of hourly mobility counts across hexagonal grid cells, which limits the predictive power of conventional time series models. To address this, we propose a lightweight and model-agnostic Spatial Neighbourhood Fusion (SPN) technique that...
