This research analyzes the spatiotemporal trend of 23,121 monkeypox virus cases in the multi-country outbreak that affected 82 countries from January 2022 to July 2022. The spatiotemporal trends analysis is developed using open data and GIS to model 3D bins and emerging hot spots globally (data by country) and nationally (data by region) for hardest hit countries, like the USA and Spain. The implemented methodology distinguishes between problem areas —as significant hot spots— and countries with no pattern. Results show consecutive hot spot patterns in Western Europe and high location quotients in North America. Factually, the countries with consecutive patterns record 16,494 cases, that is, 71.34% of the cases, where 7.63% of the world population live. At the national level, in the analysis of the USA and Spain, the results reveal regional differences with significative hot spots in California and on the East Coast of the USA and the Mediterranean coast of Spain. The proposed methodology facilitates the monitoring of the spatiotemporal evolution of monkeypox cases and is scalable and replicable using non-arbitrary and statistical parameters. The findings indicate problematic zones in real-time, enabling policymakers to develop focused interventions and proactive strategies to mitigate the future risk of monkeypox.

中文翻译：

---

时空多尺度诊断模型主动应对2022年多国猴痘病毒疫情

本研究分析了 2022 年 1 月至 2022 年 7 月影响 82 个国家的多国疫情中 23,121 例猴痘病毒病例的时空趋势。时空趋势分析是使用开放数据和 GIS 对全球 3D 分箱和新兴热点进行建模（数据对于受灾最严重的国家，例如美国和西班牙，按国家/地区划分）和全国（按地区划分）。所实施的方法区分了问题领域（作为重要热点）和没有模式的国家。结果显示西欧有连续的热点模式，而北美有较高的区位商数。事实上，连续模式的国家记录了 16,494 例病例，即 71.34% 的病例，居住着世界人口的 7.63%。在国家层面，对美国和西班牙的分析结果揭示了区域差异，其中热点地区显着集中在加利福尼亚州、美国东海岸和西班牙地中海沿岸。所提出的方法有利于监测猴痘病例的时空演变，并且可以使用非任意和统计参数进行扩展和复制。研究结果实时指出了有问题的区域，使政策制定者能够制定有针对性的干预措施和主动战略，以减轻未来的猴痘风险。