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. 2022 Jun:49:100518.
doi: 10.1016/j.spasta.2021.100518. Epub 2021 May 15.

Spatial robust fuzzy clustering of COVID 19 time series based on B-splines

Pierpaolo D'Urso  1 , Livia De Giovanni  2 , Vincenzina Vitale  1
Affiliations

Spatial robust fuzzy clustering of COVID 19 time series based on B-splines

Pierpaolo D'Urso et al. Spat Stat. 2022 Jun.

Abstract

The aim of the work is to identify a clustering structure for the 20 Italian regions according to the main variables related to COVID-19 pandemic. Data are observed over time, spanning from the last week of February 2020 to the first week of February 2021. Dealing with geographical units observed at several time occasions, the proposed fuzzy clustering model embedded both space and time information. Properly, an Exponential distance-based Fuzzy Partitioning Around Medoids algorithm with spatial penalty term has been proposed to classify the spline representation of the time trajectories. The results show that the heterogeneity among regions along with the spatial contiguity is essential to understand the spread of the pandemic and to design effective policies to mitigate the effects.

Keywords: COVID-19; Fuzzy Partitioning Around Medoids; Robust clustering; Spatial clustering; Time series clustering.

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Figures

Fig. 1
Fig. 1
Effect of the parameter β on the exponential distance (3).
Fig. 2
Fig. 2
Total cases over population by region (per 10 000 inhabitants).
Fig. 3
Fig. 3
New infections over population (per 10 000 inhabitants).
Fig. 4
Fig. 4
Spatial correlation over γ.
Fig. 5
Fig. 5
Total cases over population (per 10 000 inhabitants) — ternary plot.
Fig. 6
Fig. 6
Total cases over population (per 10 000 inhabitants) — partition of the daily time-series.
Fig. 7
Fig. 7
Total cases over population (per 10 000 inhabitants) — partition map.
Fig. 8
Fig. 8
Total deaths over population by region (per 10 000 inhabitants).
Fig. 9
Fig. 9
Total deaths over population (per 10 000 inhabitants) — ternary plot.
Fig. 10
Fig. 10
Total deaths over population (per 10 000 inhabitants) — partition of the daily time-series.
Fig. 11
Fig. 11
Total deaths over population (per 10 000 inhabitants) — partition map.
Fig. 12
Fig. 12
Total cases over swabs by region.
Fig. 13
Fig. 13
Total cases over swabs — ternary plot.
Fig. 14
Fig. 14
Total cases over swabs — partition of the daily time-series.
Fig. 15
Fig. 15
Total cases over swabs — partition map.
Fig. 16
Fig. 16
Cumulative swabs.
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References

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