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 I 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 I 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 年冠状病毒病 (COVID-19) 病例的空间分布可以为预测世界范围内的疫情爆发提供有价值的信息，从而改善公共卫生政策。本研究计算了2020年3月底和2020年4月所有受新冠疫情影响国家的累积发病率（CIR）和累积死亡率（CMR）。在实施热点分析之前，空间分布得到了CIR的自相关结果。然后应用热点分析和 Anselin Local Moran's I指数来准确定位全球 COVID-19 的高风险和低风险集群。尽管比利时于 4 月 30 日取代了意大利，但圣马力诺和意大利在 3 月底公布了最高的 CMR。研究期末（截至4月30日），CIR表现出明显的空间聚集性。因此，南欧、北欧和西欧在高高集群中被检测到，表明这些地区以及周边地区感染 COVID-19 的风险增加。北非国家表现出热点聚集，尽管这些地区的 CIR 值较低，但置信度高于 95%。热点占CIR的近70%。此外，对集群和异常值的分析表明，这些国家处于低-高异常值模式。大多数表现出高值聚集（热点）且置信度为 99%（截至 3 月 31 日）和 95%（截至 4 月 30 日）的接受调查国家都致力于较高的 CIR 值。 总之，热点分析与 Anselin 当地 Moran's I相结合，提供了一种严谨、客观的方法来确定具有统计意义的 COVID-19 病例群的位置，从而揭示高风险地区。