Communicable diseases “flow” between locations. These flows dictate where and when certain communities will be affected. While the prediction of disease flows is essential for the timely intervention of epidemics, few studies have addressed this critical issue. This study predicts disease flows during an epidemic by considering the epidemiological, network, and temporal contextual factors using a deep learning approach. A series of scenario analyses helps identify the effects of these contextual factors on disease flows. Results show that the extended spatial–temporal effect of the epidemiological factors stimulates disease flows. The compound effects of the network factors enhance the transmission efficiency of these flows. Lastly, the temporal effect accelerates the combined effects of epidemiological and network factors on the flows. The findings of this study reveal the intricate nature of disease flows and lay a solid foundation for real‐time surveillance of epidemics and pandemics to inform timely interventions for a broad range of communicable diseases.

中文翻译：

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是什么推动了疾病在不同地点之间的流动？

传染病在不同地点之间“流动”。这些流动决定了某些社区将在何时何地受到影响。虽然疾病流动的预测对于及时干预流行病至关重要，但很少有研究解决这个关键问题。本研究使用深度学习方法通​​过考虑流行病学、网络和时间背景因素来预测流行期间的疾病流动。一系列情景分析有助于确定这些背景因素对疾病流动的影响。结果表明，流行病学因素的扩展时空效应刺激了疾病流动。网络因素的复合效应提高了这些流量的传输效率。最后，时间效应加速了流行病学和网络因素对流量的综合影响。