Spatiotemporal multiscale diagnosis model to proactively respond to the multi-country monkeypox virus outbreak in 2022     

Olga De Cos Guerra  Valentín Castillo Salcines  David Cantarero Prieto 《Transactions in GIS》2023,27(8):2175-2196

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.  相似文献