Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Jun:41:100500.
doi: 10.1016/j.sste.2022.100500. Epub 2022 Mar 25.

A weighted approach for spatio-temporal clustering of COVID-19 spread in Italy

Raffaele Mattera  1
Affiliations

A weighted approach for spatio-temporal clustering of COVID-19 spread in Italy

Raffaele Mattera. Spat Spatiotemporal Epidemiol. 2022 Jun.

Abstract

The SARS-Cov-2 has spread differently over space and time worldwide. By monitoring the contagion's time evolution, the November 3 2020 the Italian government introduced differentiated regime of restrictions among its regions. This experiment demonstrated that public health policies can be effectively designed by means of clustering. This paper proposes a fuzzy clustering model where spatial and temporal dimensions of the disease spread are optimally weighted. The resulting model is applied with the aim of identifying groups of Italian regions with similar contagion spread. We found that two groups of regions sharing similar patterns of COVID-19 spread over both space and time exist. Appropriate public health policies can be designed on the basis of this evidence.

Keywords: COVID-19; Fuzzy clustering; LISA; Policy design; Spatial auto-correlation; Time series.

PubMed Disclaimer

Figures

Fig. 1
Fig. 1
Number of positive COVID-19 cases per 1000 inhabitants 20/04/2021.
Fig. 2
Fig. 2
Occupied intensive care beds per 1000 inhabitants 20/04/2021.
Fig. 3
Fig. 3
Occupied intensive care beds per 1000 inhabitants: time evolution of some Italian regions.
Fig. 4
Fig. 4
Positive cases per 1000 inhabitants: time evolution of Italian regions.
Fig. 5
Fig. 5
Campello (2007) Fuzzy Silhouette: values for different clusters C.
Fig. 6
Fig. 6
Fuzzy clustering (occupied beds in intensive care): crisp classification.
Fig. 7
Fig. 7
Fuzzy clustering model (occupied beds in intensive care): membership degrees.
Fig. 8
Fig. 8
Campello (2007) Fuzzy Silhouette: values for different clusters C.
Fig. 9
Fig. 9
Fuzzy clustering (positive cases): crisp classification.
Fig. 10
Fig. 10
Fuzzy clustering (positive cases): membership degrees.
See this image and copyright information in PMC

References

    1. Anselin L. Local indicators of spatial association—LISA. Geogr. Anal. 1995;27(2):93–115.
    1. Bastos J.A., Caiado J. On the classification of financial data with domain agnostic features. Internat. J. Approx. Reason. 2021;138:1–11.
    1. Bezdek J.C., Ehrlich R., Full W. FCM: The fuzzy c-means clustering algorithm. Comput. Geosci. 1984;10(2–3):191–203.
    1. Caiado J., Crato N., Peña D. A periodogram-based metric for time series classification. Comput. Statist. Data Anal. 2006;50(10):2668–2684.
    1. Campello R.J. A fuzzy extension of the rand index and other related indexes for clustering and classification assessment. Pattern Recognit. Lett. 2007;28(7):833–841.