The paper introduces an approach to identify a set of spatially constrained homogeneous areas maximally homogeneous in terms of epidemic trends. The proposed hierarchical algorithm is based on the Dynamic Time Warping distances between epidemic time trends where units are constrained by a spatial proximity graph. The paper includes two different applications of this approach to Italy, based on different data (number of positive test and number of differential deaths, with respect to the previous years) and on different observational units (provinces and Labour Market Areas). Both applications, above all the one related to Labour Market Areas, show the existence of well-defined areas, where the dynamics of growth of the infection have been strongly differentiated. The adoption of the same lock-down policy throughout the entire national territory has been therefore sub-optimal, showing once again the urgent need for local data-driven policies.

Identification of spatially constrained homogeneous clusters of COVID-19 transmission in Italy

Pignataro G.;
2020-01-01

Abstract

The paper introduces an approach to identify a set of spatially constrained homogeneous areas maximally homogeneous in terms of epidemic trends. The proposed hierarchical algorithm is based on the Dynamic Time Warping distances between epidemic time trends where units are constrained by a spatial proximity graph. The paper includes two different applications of this approach to Italy, based on different data (number of positive test and number of differential deaths, with respect to the previous years) and on different observational units (provinces and Labour Market Areas). Both applications, above all the one related to Labour Market Areas, show the existence of well-defined areas, where the dynamics of growth of the infection have been strongly differentiated. The adoption of the same lock-down policy throughout the entire national territory has been therefore sub-optimal, showing once again the urgent need for local data-driven policies.
2020
Epidemiological Models
R(t)
Spatial clustering
Spatial heterogeneity
Spatial justice
Time series distance
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1151329
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 16
  • ???jsp.display-item.citation.isi??? 12
social impact