Shariati Mohsen, Mesgari Tahoora, Kasraee Mahboobeh, Jahangiri-Rad Mahsa
College of Engineering, Faculty of Environment, Department of Environmental Planning, Management and Education, University of Tehran, Tehran, Iran.
Student Scientific Research Center (SSRC), Tehran University of Medical Sciences, Tehran, Iran.
J Environ Health Sci Eng. 2020 Oct 12;18(2):1499-1507. doi: 10.1007/s40201-020-00565-x. eCollection 2020 Dec.
Understanding the spatial distribution of coronavirus disease 2019 (COVID-19) cases can provide valuable information to anticipate the world outbreaks and in turn improve public health policies. In this study, the cumulative incidence rate (CIR) and cumulative mortality rate (CMR) of all countries affected by the new corona outbreak were calculated at the end of March and April, 2020. Prior to the implementation of hot spot analysis, the spatial autocorrelation results of CIR were obtained. Hot spot analysis and Anselin Local Moran's indices were then applied to accurately locate high and low-risk clusters of COVID-19 globally. San Marino and Italy revealed the highest CMR by the end of March, though Belgium took the place of Italy as of 30th April. At the end of the research period (by 30th April), the CIR showed obvious spatial clustering. Accordingly, southern, northern and western Europe were detected in the high-high clusters demonstrating an increased risk of COVID-19 in these regions and also the surrounding areas. Countries of northern Africa exhibited a clustering of hot spots, with a confidence level above 95%, even though these areas assigned low CIR values. The hot spots accounted for nearly 70% of CIR. Furthermore, analysis of clusters and outliers demonstrated that these countries are situated in the low-high outlier pattern. Most of the surveyed countries that exhibited clustering of high values (hot spot) with a confidence level of 99% (by 31st March) and 95% (by 30th April) were dedicated higher CIR values. In conclusion, hot spot analysis coupled with Anselin local Moran's provides a scrupulous and objective approach to determine the locations of statistically significant clusters of COVID-19 cases shedding light on the high-risk districts.
了解2019冠状病毒病（COVID-19）病例的空间分布可为预测全球疫情提供有价值的信息，进而改进公共卫生政策。在本研究中，计算了2020年3月底和4月底受新冠疫情影响的所有国家的累积发病率（CIR）和累积死亡率（CMR）。在进行热点分析之前，先获得了CIR的空间自相关结果。然后应用热点分析和安塞尔林局部莫兰指数来准确确定全球COVID-19的高风险和低风险集群位置。截至3月底，圣马力诺和意大利的CMR最高，不过截至4月30日，比利时取代了意大利。在研究期结束时（截至4月30日），CIR呈现出明显的空间聚集性。因此，在高高集群中发现了南欧、北欧和西欧，表明这些地区以及周边地区的COVID-19风险增加。北非国家呈现出热点聚集，置信水平高于95%，尽管这些地区的CIR值较低。这些热点地区占CIR的近70%。此外，对集群和离群值的分析表明，这些国家处于低高离群模式。大多数接受调查的国家在3月31日置信水平为99%、4月30日置信水平为95%时呈现高值（热点）聚集，其CIR值较高。总之，热点分析与安塞尔林局部莫兰指数相结合，为确定COVID-19病例具有统计学意义的集群位置提供了一种严谨且客观的方法，揭示了高风险地区。
