COVID-19 Community Temporal Visualizer: a new methodology for the network-based analysis and visualization of COVID-19 data

Abstract

Understanding the evolution of the spread of the COVID-19 pandemic requires the analysis of several data at the spatial and temporal levels. Here, we present a new network-based methodology to analyze COVID-19 data measures containing spatial and temporal features and its application on a real dataset. The goal of the methodology is to analyze sets of homogeneous datasets (i.e. COVID-19 data taken in different periods and in several regions) using a statistical test to find similar/dissimilar datasets, mapping such similarity information on a graph and then using a community detection algorithm to visualize and analyze the spatio-temporal evolution of data. We evaluated diverse Italian COVID-19 data made publicly available by the Italian Protezione Civile Department at https://github.com/pcm-dpc/COVID-19/. Furthermore, we considered the climate data related to two periods and we integrated them with COVID-19 data measures to detect new communities related to climate changes. In conclusion, the application of the proposed methodology provides a network-based representation of the COVID-19 measures by highlighting the different behaviour of regions with respect to pandemics data released by Protezione Civile and climate data. The methodology and its implementation as R function are publicly available at https://github.com/mmilano87/analyzeC19D.

Keywords: COVID-19; Community detection; Network analysis.

Fig. 1

A community structure example. The figure depicts community 1, community 2 and community 3 within the network

Fig. 2

Common changes in community structure evolution

Fig. 3

CCTV Methodology pipeline

Fig. 4

Definition of the similarity matrix

Fig. 5

An example of similarity matrix of Intensive Care network after the first week

Fig. 6

Evolution of Hospitalised with Symptoms Network Communities in the first observation period

Fig. 7

Evolution of Intensive Care Network Communities in the first observation period

Fig. 8

Evolution of Total Hospitalised Network Communities in the first observation period

Fig. 9

Evolution of Home Isolation Network Communities in the first observation period

Fig. 10

Evolution of Total Currently Positive Network Communities in the first observation period

Fig. 11

Evolution of New Currently Positive Network Communities in the first observation period

Fig. 12

Evolution of Discharged/ Healed Network Communities in the first observation period

Fig. 13

Evolution of Deceased Network Communities in the first observation period

Fig. 14

Evolution of Total Cases Network Communities in the first observation period

Fig. 15

Evolution of Swabs Network Communities in the first observation period

Fig. 16

Evolution of Hospitalised with Symptoms Network Communities in the second observation period

Fig. 17

Evolution of Intensive Care Network Communities in the second observation period

Fig. 18

Evolution of Total Hospitalised Network Communities in the second observation period

Fig. 19

Evolution of Home Isolation Network Communities in the second observation period

Fig. 20

Evolution of Total Currently Positive Network Communities in the second observation period

Fig. 21

Evolution of New Currently Positive Network Communities in the second observation period

Fig. 22

Evolution of Discharged/ Healed Network Communities in the second observation period

Fig. 23

Evolution of Deceased Network Communities in the second observation period

Fig. 24

Evolution of Total Cases Network Communities in the second observation period

Fig. 25

Evolution of Swabs Network Communities in the second observation period

Fig. 26

Weather data collection pipeline. A list of dates and city names are extracted from the epidemiological data previously described. Then, the cities are combined with all dates to reconstruct the URL of each page that contains historical weather information on the meteorological website. Data is extracted through data scraping, then raw data is normalized and cleaned. Finally, data are stored in a CSV file format

Fig. 27

The comparison among Hospitalised with Symptoms Network Communities and Total Hospitalised Network Communities at the end of the 9 weeks of first observation period

Fig. 28

The comparison among Deceased Network Communities and Total Cases Network Communities at the end of the 9 weeks of first observation period

Fig. 29

The comparison among Total Currently Positive Network Communities and New Currently Positive Network Communities at the end of the 9 weeks of first observation period

Fig. 30

Networks built integrating climate in the first observation period. The formed communities take climate data into account

Fig. 31

Networks built integrating climate in the second observation period. The formed communities take climate data into account