Visualizing COVID-19 pandemic risk through network connectedness

Mike K P So; Agnes Tiwari; Amanda M Y Chu; Jenny T Y Tsang; Jacky N L Chan

doi:10.1016/j.ijid.2020.05.011

Visualizing COVID-19 pandemic risk through network connectedness

Int J Infect Dis. 2020 Jul:96:558-561. doi: 10.1016/j.ijid.2020.05.011. Epub 2020 May 8.

Authors

Mike K P So¹, Agnes Tiwari², Amanda M Y Chu³, Jenny T Y Tsang³, Jacky N L Chan⁴

Affiliations

¹ Department of Information Systems, Business Statistics and Operations Management, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong. Electronic address: [email protected].
² LKS Faculty of Medicine, The University of Hong Kong, Hong Kong; School of Nursing, Hong Kong Sanatorium and Hospital, Hong Kong.
³ Department of Social Sciences, The Education University of Hong Kong, Tai Po, Hong Kong.
⁴ Department of Information Systems, Business Statistics and Operations Management, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong.

Abstract

With the domestic and international spread of the coronavirus disease 2019 (COVID-19), much attention has been given to estimating pandemic risk. We propose the novel application of a well-established scientific approach - the network analysis - to provide a direct visualization of the COVID-19 pandemic risk; infographics are provided in the figures. By showing visually the degree of connectedness between different regions based on reported confirmed cases of COVID-19, we demonstrate that network analysis provides a relatively simple yet powerful way to estimate the pandemic risk.

Keywords: Coronavirus; Infographics; Pandemic network; Pandemic preparedness; Risk assessment; Visualization.

Publication types

Review

MeSH terms

Betacoronavirus
COVID-19
China
Coronavirus Infections / epidemiology*
Global Health
Humans
Pandemics
Pneumonia, Viral / epidemiology*
Risk Assessment / methods*
SARS-CoV-2