2020/06/05 by Roberto Benedetti, Federica Piersimoni, Benedetti, Roberto +5
Economics, Econometrics and Finance · Mathematics · Medicine · #Applications (stat.AP) #COVID-19 epidemiological studies #Data-Driven Disease Surveillance #FOS: Computer and information sciences #FOS: Physical sciences #Physics and Society (physics.soc-ph) #Spatial and Panel Data Analysis
paper · pdf · doi:10.48550/arxiv.2006.03360
openalex publication_date 2020/06/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
The paper introduces an approach to identify a set of spatially constrained\nhomogeneous areas maximally homogeneous in terms of epidemic trends. The\nproposed hierarchical algorithm is based on the Dynamic TimeWarping distances\nbetween epidemic time trends where units are constrained by a spatial proximity\ngraph. The paper includes two different applications of this approach to Italy,\nbased on different data (number of positive test and number of differential\ndeaths, with respect to the previous years) and on different observational\nunits (provinces and Labour Market Areas). Both applications, above all the one\nrelated to Labour Market Areas, show the existence of well-defined areas, where\nthe dynamics of growth of the infection have been strongly differentiated. The\nadoption of the same lock-down policy throughout the entire national territory\nhas been therefore sub-optimal, showing once again the urgent need for local\ndata-driven policies.\n