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.
Abstract (translated)
登革热，一种由蚊子传播的疾病，在城市地区尤其是新加坡这样的热带区域持续构成一个持久的公共卫生挑战。有效的控制措施需要预测病原体在不同地区的传播风险，以便主动部署干预手段而不是被动应对。本研究提出了一种新颖框架，通过挖掘公开可用的登革热病例数据来发现并利用城市地区之间的潜在传播联系。不同于将个案视为孤立报告，我们的模型探讨了在一个区域内热点形成如何受邻近区域疫情动态的影响。虽然蚊子活动范围有限，但远距离传播往往由人类移动驱动，在我们案例研究中，学习到的网络与通勤流动高度一致，为城市范围内疾病的扩散提供了一个解释性的说明。 这些隐含联系通过梯度下降方法进行优化，并不仅用于预测热点状态，还通过检查连续几周内推断出的网络传播模式稳定性来验证传播模式的一致性。2013年至2018年以及2020年的新加坡案例研究显示，只需利用四周的历史数据便足以达到平均F值（即准确率和召回率的调和平均数）为0.79。尤为重要的是，所学习到的传播联系与通勤流动相吻合，强调了隐含流行病扩散和人类移动之间的可解释互动。 通过从单纯报告登革热病例转变为挖掘并验证隐藏的传播动态，这项工作将开放式的网络案例数据转化为预测性和说明性的资源。提出的框架不仅推进了疫情建模，还为公共卫生规划、早期干预和城市韧性提供了一种规模大且成本低的工具。
URL
https://arxiv.org/abs/2601.12856
