On topological properties of COVID-19: Predicting and assessing pandemic risk with network statistics

Mike K. P. SO, Man Ying Amanda CHU, Agnes TIWARI, Jacky N. L. CHAN

Research output: Contribution to journalArticlespeer-review

4 Citations (Scopus)

Abstract

The spread of coronavirus disease 2019 (COVID-19) has caused more than 80 million confirmed infected cases and more than 1.8 million people died as of 31 December 2020. While it is essential to quantify risk and characterize transmission dynamics in closed populations using Susceptible-Infection-Recovered modeling, the investigation of the effect from worldwide pandemic cannot be neglected. This study proposes a network analysis to assess global pandemic risk by linking 164 countries in pandemic networks, where links between countries were specified by the level of ‘co-movement’ of newly confirmed COVID-19 cases. More countries showing increase in the COVID-19 cases simultaneously will signal the pandemic prevalent over the world. The network density, clustering coefficients, and assortativity in the pandemic networks provide early warning signals of the pandemic in late February 2020. We propose a preparedness pandemic risk score for prediction and a severity risk score for pandemic control. The preparedness risk score contributed by countries in Asia is between 25% and 50% most of the time after February and America contributes around 40% in July 2020. The high preparedness risk contribution implies the importance of travel restrictions between those countries. The severity risk score of America and Europe contribute around 90% in December 2020, signifying that the control of COVID-19 is still worrying in America and Europe. We can keep track of the pandemic situation in each country using an online dashboard to update the pandemic risk scores and contributions. Copyright © 2021 The Author(s).
Original languageEnglish
Article number5112
JournalScientific Reports
Volume11
Early online date04 Mar 2021
DOIs
Publication statusPublished - 2021

Citation

So, M. K. P., Chu, A. M. Y., Tiwari, A., & Chan, J. N. L. (2021). On topological properties of COVID-19: Predicting and assessing pandemic risk with network statistics. Scientific Reports, 11. Retrieved from https://doi.org/10.1038/s41598-021-84094-z

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