Detecting Global Community Structure in a COVID-19 Activity Correlation Network

Hiroki Sayama*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The global pandemic of COVID-19 over the last 2.5 years have produced an enormous amount of epidemic/public health datasets, which may also be useful for studying the underlying structure of our globally connected world. Here we used the Johns Hopkins University COVID-19 dataset to construct a correlation network of countries/regions and studied its global community structure. Specifically, we selected countries/regions that had at least 100,000 cumulative positive cases from the dataset and generated a 7-day moving average time series of new positive cases reported for each country/region. We then calculated a time series of daily change exponents by taking the day-to-day difference in log of the number of new positive cases. We constructed a correlation network by connecting countries/regions that had positive correlations in their daily change exponent time series using their Pearson correlation coefficient as the edge weight. Applying the modularity maximization method revealed that there were three major communities: (1) Mainly Europe + North America + Southeast Asia that showed similar six-peak patterns during the pandemic, (2) mainly Near/Middle East + Central/South Asia + Central/South America that loosely followed Community 1 but had a notable increase of activities because of the Delta variant and was later impacted significantly by the Omicron variant, and (3) mainly Africa + Central/East Canada + Australia that did not have much activities until a huge spike was caused by the Omicron variant. These three communities were robustly detected under varied settings. Constructing a 3D “phase space” by using the median curves in those three communities for x-y-z coordinates generated an effective summary trajectory of how the global pandemic progressed.

Original languageEnglish
Title of host publicationComplex Networks and Their Applications XI - Proceedings of The 11th International Conference on Complex Networks and Their Applications
Subtitle of host publicationCOMPLEX NETWORKS 2022—Volume 1
EditorsHocine Cherifi, Rosario Nunzio Mantegna, Luis M. Rocha, Chantal Cherifi, Salvatore Miccichè
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9783031211263
Publication statusPublished - 2023
Event11th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2022 - Palermo, Italy
Duration: 2022 Nov 82022 Nov 10

Publication series

NameStudies in Computational Intelligence
Volume1077 SCI
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503


Conference11th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2022

ASJC Scopus subject areas

  • Artificial Intelligence


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