Spatiotemporal Monitoring of COVID-19 Cases in Geneva

This lecture focuses on the spatial and temporal analysis of COVID-19 positive cases in Geneva, analyzing the location of 2800 positive subjects' residences between February 26 and April 16. By using a non-parametric space-time density processing algorithm, significant spatial clusters were detected, located, and characterized. The study aims to describe the geographical clustering of positive cases across space and time to inform on disease outbreak origins, current spreading zones, and enable accurate prevention and containment measures. Data collected by the Virology Laboratory at Geneva University Hospitals was used, and a Modified Space-Time Density-Based Spatial Clustering of Applications with Noise algorithm was applied. The lecture also discusses the evolution of clusters, the impact of containment measures, and the association between deprivation and the persistence of COVID-19 transmission clusters.