From an epidemiological perspective, previous research on COVID-19 has generally been based on classical statistical analyses. As a result, spatial information is often not used effectively. This paper uses image-based neural networks to explore the relationship between urban spatial risk and the distribution of infected populations, and the design of urban facilities. To achieve this objective, we use spatio-temporal data of people infected with new coronary pneumonia prior to 28 February 2020 in Wuhan. We then use kriging, which is a method of spatial interpolation, as well as core density estimation technology to establish the epidemic heat distribution on fine grid units. We further evaluate the influence of nine major spatial risk factors, including the distribution of agencies, hospitals, park squares, sports fields, banks and hotels, by testing them for significant positive correlation with the distribution of the epidemic. The weights of these spatial risk factors are used for training Generative Adversarial Network (GAN) models, which predict the distribution of cases in a given area. The input image for the machine learning model is a city plan converted by public infrastructures, and the output image is a map of urban spatial risk factors in the given area. The results of the trained model demonstrate that optimising the relevant point of interests (POI) in urban areas to effectively control potential risk factors can aid in managing the epidemic and preventing it from dispersing further.

从流行病学的角度来看，以往对 COVID-19 的研究通常基于经典的统计分析。因此，空间信息常常得不到有效利用。本文使用基于图像的神经网络来探索城市空间风险与感染人群分布之间的关系，以及城市设施的设计。为实现这一目标，我们使用了 2020 年 2 月 28 日之前武汉市新冠肺炎感染者的时空数据。然后，我们使用克里金法（一种空间插值方法）以及核心密度估计技术来建立精细网格单元上的流行热分布。我们进一步评估九大空间风险因素的影响，包括机构、医院、公园广场、运动场、银行和酒店的分布，通过测试它们与流行病分布的显着正相关。这些空间风险因素的权重用于训练生成对抗网络 (GAN) 模型，该模型预测给定区域中病例的分布。机器学习模型的输入图像是公共基础设施转换后的城市规划，输出图像是给定区域的城市空间风险因素图。训练模型的结果表明，优化城市地区的相关兴趣点（POI）以有效控制潜在风险因素有助于控制流行病并防止其进一步扩散。这些空间风险因素的权重用于训练生成对抗网络 (GAN) 模型，该模型预测给定区域中病例的分布。机器学习模型的输入图像是公共基础设施转换后的城市规划，输出图像是给定区域的城市空间风险因素图。训练模型的结果表明，优化城市地区的相关兴趣点（POI）以有效控制潜在风险因素有助于控制流行病并防止其进一步扩散。这些空间风险因素的权重用于训练生成对抗网络 (GAN) 模型，该模型预测给定区域中病例的分布。机器学习模型的输入图像是公共基础设施转换后的城市规划，输出图像是给定区域的城市空间风险因素图。训练模型的结果表明，优化城市地区的相关兴趣点（POI）以有效控制潜在风险因素有助于控制流行病并防止其进一步扩散。