Mining Citywide Dengue Spread Patterns in Singapore Through Hotspot Dynamics from Open Web Data

2026-01-19

AI Summary

This study addresses the challenge of proactively identifying urban dengue fever hotspots by leveraging publicly available case data to enable timely public health interventions. We propose a novel approach that first transforms case reports into an interpretable implicit transmission network, integrating both epidemiological influence from neighboring regions and human commuting patterns. A gradient descent–optimized transmission model is then constructed, with temporal consistency of the inferred transmission dynamics validated through time-series stability analysis. Experiments on dengue incidence data from Singapore (2013–2018 and 2020) demonstrate that our method achieves an average F-score of 0.79 using only four weeks of historical hotspot data. The learned transmission network exhibits strong alignment with actual commuting flows, revealing an intrinsic link between human mobility and epidemic spread.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Content-based information diffusion
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Dengue, a mosquito-borne disease, continues to pose a persistent public health challenge in urban areas, particularly in tropical regions such as Singapore. Effective and affordable control requires anticipating where transmission risks are likely to emerge so that interventions can be deployed proactively rather than reactively. This study introduces a novel framework that uncovers and exploits latent transmission links between urban regions, mined directly from publicly available dengue case data. Instead of treating cases as isolated reports, we model how hotspot formation in one area is influenced by epidemic dynamics in neighboring regions. While mosquito movement is highly localized, long-distance transmission is often driven by human mobility, and in our case study, the learned network aligns closely with commuting flows, providing an interpretable explanation for citywide spread. These hidden links are optimized through gradient descent and used not only to forecast hotspot status but also to verify the consistency of spreading patterns, by examining the stability of the inferred network across consecutive weeks. Case studies on Singapore during 2013-2018 and 2020 show that four weeks of hotspot history are sufficient to achieve an average F-score of 0.79. Importantly, the learned transmission links align with commuting flows, highlighting the interpretable interplay between hidden epidemic spread and human mobility. By shifting from simply reporting dengue cases to mining and validating hidden spreading dynamics, this work transforms open web-based case data into a predictive and explanatory resource. The proposed framework advances epidemic modeling while providing a scalable, low-cost tool for public health planning, early intervention, and urban resilience.

Problem

• dengue spread
• hotspot dynamics
• urban transmission
• human mobility
• epidemic modeling

Innovation

• hotspot dynamics
• human mobility
• epidemic modeling
• open web data
• transmission network