In recent months, the world has been affected by the infectious coronavirus disease and Iran is one of the most affected countries. The Iranian government's health facilities for an urgent investigation of all provinces do not exist simultaneously. There is no management tool to identify the vulnerabilities of Iranian provinces in prioritizing health services. The aim of this study was to prepare a coronavirus vulnerability map of Iranian provinces using geographic information system (GIS) to monitor the disease. For this purpose, four criteria affecting coronavirus, including population density, percentage of older people, temperature, and humidity, were prepared in the GIS. A multiscale geographically weighted regression (MGWR) model was used to determine the vulnerability of coronavirus in Iran. An adaptive neuro-fuzzy inference system (ANFIS) model was used to predict vulnerability in the next two months. Results indicated that, population density and older people have a more significant impact on coronavirus in Iran. Based on MGWR models, Tehran, Mazandaran, Gilan, and Alborz provinces were more vulnerable to coronavirus in February and March. The ANFIS model findings showed that West Azerbaijan, Zanjan, Fars, Yazd, Semnan, Sistan and Baluchistan, and Tehran provinces were more vulnerable in April and May.

近几个月来，世界受到传染性冠状病毒病的影响，伊朗是受影响最严重的国家之一。伊朗政府紧急调查所有省份的卫生设施并非同时存在。没有管理工具可以识别伊朗各省在优先考虑卫生服务方面的薄弱环节。本研究的目的是使用地理信息系统 (GIS) 绘制伊朗各省的冠状病毒脆弱性地图以监测疾病。为此，在 GIS 中准备了四个影响冠状病毒的标准，包括人口密度、老年人的百分比、温度和湿度。使用多尺度地理加权回归 (MGWR) 模型来确定伊朗冠状病毒的脆弱性。自适应神经模糊推理系统 (ANFIS) 模型用于预测未来两个月的脆弱性。结果表明，人口密度和老年人对伊朗冠状病毒的影响更为显着。根据 MGWR 模型，德黑兰、马赞达兰、吉兰和厄尔布尔士省在 2 月和 3 月更容易感染冠状病毒。ANFIS 模型调查结果显示，西阿塞拜疆、赞詹、法尔斯、亚兹德、塞姆南、锡斯坦和俾路支斯坦以及德黑兰省在 4 月和 5 月更加脆弱。