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

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  • Roberto Benedetti
  • Federica Piersimoni
  • Giacomo Pignataro
  • Francesco Vidoli

Abstract

This paper introduces an approach to identify a set of spatially constrained homogeneous areas that are 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. Two different applications of this approach to Italy are presented, based on different data (number of positive tests and number of differential deaths with respect to previous years) and different observational units observed at different spatial scales (provinces and labour market areas). The provincial analysis was mainly used to divide the national territory into macro‐areas with different contagion trends, while the more detailed partition was carried out only for the macro‐areas with higher risk of transmission of the infection. Both applications, above all that 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 lockdown policy throughout the entire national territory has been therefore sub‐optimal, highlighting once again the urgent need for local data‐driven policies. Este artículo presenta un enfoque para identificar un conjunto de zonas homogéneas con restricciones espaciales que sean lo más homogéneas posible en cuanto a las tendencias de epidemias. El algoritmo jerárquico propuesto se basa en las distancias de deformación temporal dinámica entre las tendencias temporales de la epidemia, donde las unidades están restringidas por un gráfico de proximidad espacial. Se presentan dos aplicaciones diferentes de este enfoque para Italia, con base en datos diferentes (número de pruebas positivas y diferenciales de muertes con respecto a años anteriores) y unidades de estudio diferentes observadas a diferentes escalas espaciales (provincias y zonas del mercado laboral). El análisis provincial se utilizó principalmente para dividir el territorio nacional en macrozonas con diferentes tendencias de contagio, mientras que la división más detallada se realizó sólo para las macrozonas con mayor riesgo de transmisión de la infección. Ambas aplicaciones, sobre todo la relacionada con las áreas del mercado laboral, muestran la existencia de áreas bien definidas con una fuerte diferenciación en la dinámica de crecimiento de la infección. Por consiguiente, la adopción de una misma política de confinamiento en todo el territorio nacional ha sido subóptima, lo que pone de relieve una vez más la necesidad urgente de políticas locales basadas en datos. 本稿では、流行の傾向という観点で最大に均質である空間的に制約された均質地域の集合を特定するアプローチを紹介する。提案する階層的アルゴリズムは、流行の時間トレンド間の動的時間伸縮法による距離に基づいており、ここでユニットは空間的近接グラフにより制約される。このアプローチをイタリアの二つの例に応用し、異なるデータ (検査陽性数と前年との死亡数の差)と異なる空間スケール (地方州と労働市場地域)で観測された異なる観測単位に基づいて示した。地方分析は、主に地域を伝染傾向別のマクロ地域に分割するために用いられたが、COVID‐19の伝播のリスクが高いマクロ地域に対してのみ、より詳細な分割を行った。この2つの応用は、他の労働市場の分野に関連した応用よりも、この感染拡大の動態がはっきりと識別される明確な領域があることを示した。したがって、全国の地域に同じロックダウン政策を採用することは最適ではなく、地域のデータに基づく政策が緊急に必要であることが改めて強調された。

Suggested Citation

  • Roberto Benedetti & Federica Piersimoni & Giacomo Pignataro & Francesco Vidoli, 2020. "Identification of spatially constrained homogeneous clusters of COVID‐19 transmission in Italy," Regional Science Policy & Practice, Wiley Blackwell, vol. 12(6), pages 1169-1187, December.
    • Handle: RePEc:bla:rgscpp:v:12:y:2020:i:6:p:1169-1187
    DOI: 10.1111/rsp3.12371
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    References listed on IDEAS

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    1. Vicente Rios & Lisa Gianmoena, 2021. "On the link between temperature and regional COVID‐19 severity: Evidence from Italy," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(S1), pages 109-137, November.
    2. Cavalieri, Marina & Di Caro, Paolo & Guccio, Calogero & Lisi, Domenico, 2020. "Does neighbours' grass matter? Testing spatial dependent heterogeneity in technical efficiency of Italian hospitals," Social Science & Medicine, Elsevier, vol. 265(C).

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