Investigations on the spatial patterns of COVID-19 spreading indicate the possibility of the virus transmission by moving infected people in an urban area. Hospitals are the most susceptible locations due to the COVID-19 contaminations in metropolises. This paper aims to find the riskiest places surrounding the hospitals using an MLP-ANN. The main contribution is discovering the influence zone of COVID-19 treatment hospitals and the main spatial factors around them that increase the prevalence of COVID-19. The innovation of this paper is to find the most relevant spatial factors regarding the distance from central hospitals modeling the risk level of the study area. Therefore, eight hospitals with two service areas for each of them are computed with [0-500] and [500-1000] meters distance. Besides, five spatial factors have been considered, consist of the location of patients’ financial transactions, the distance of streets from hospitals, the distance of highways from hospitals, the distance of the non-residential land use from the hospitals, and the hospital patient number. The implementation results revealed a meaningful relation between the distance from the hospitals and patient density. The RMSE and R measures are 0.00734 and 0.94635 for [0-500 m] while these quantities are 0.054088 and 0.902725 for [500-1000 m] respectively. These values indicate the role of distance to central hospitals for COVID-19 treatment. Moreover, a sensitivity analysis demonstrated that the number of patients’ transactions and the distance of the non-residential land use from the hospitals are two dominant factors for virus propagation. The results help urban managers to begin preventative strategies to decrease the community incidence rate in high-risk places.

对 COVID-19 传播空间模式的调查表明，病毒可能通过在城市地区移动感染者而传播。由于大都市中的 COVID-19 污染，医院是最容易受到影响的地方。本文旨在使用 MLP-ANN 找到医院周围风险最高的地方。主要贡献是发现 COVID-19 治疗医院的影响区及其周围增加 COVID-19 流行率的主要空间因素。本文的创新之处在于找到了与中心医院距离相关的空间因素，这些因素对研究区域的风险水平进行了建模。因此，按 [0-500] 和 [500-1000] 米距离计算八家医院，每家医院有两个服务区。此外，还考虑了五个空间因素，包括患者金融交易地点、街道与医院的距离、高速公路与医院的距离、非住宅用地与医院的距离以及医院患者人数。实施结果揭示了与医院的距离与患者密度之间的有意义的关系。对于 [0-500 m]，而对于 [500-1000 m]，这些数量分别为 0.054088 和 0.902725  。这些值表明到中心医院的距离对 COVID-19 治疗的作用。此外，敏感性分析表明，患者的交易数量和非住宅用地与医院的距离是病毒传播的两个主要因素。结果有助于城市管理者开始采取预防策略，以降低高风险地区的社区发病率。