Visualizing COVID-19 pandemic risk through network connectedness

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 approachthe network analysisto 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.

What factors drive the satisfaction of citizens with governments’ responses to COVID-19?

“…Forced to react swiftly to the rapid spread of the COVID-19 pandemic in early 2020, many governments have tried to balance the implementation of containment policies against other important factors like national economy or citizens' livelihood with priority mostly given to protecting people's health (Sabat et al, 2020). Consequently, heated debates have arisen in nearly all countries about whether or not such measures taken are appropriate after being able to visualize the propagation of COVID-19 (So et al, 2020). Facing the difficulty of enforcing health-related regulations, governments must persuade their citizens to initiate behavioral changes and respect future containment interventions.…”

On Topological Properties of COVID-19: Predicting and Controling Pandemic Risk with Network Statistics

“…From the figure, we can visualize the pandemic networks are getting more dense from February to April 2020. This visual increase in connectedness was documented as an early warning signal of the COVID-19 pandemic in the literature 22 . The left panel of Figure 3 presents the number of edges, network density, clustering coefficient, and the assortativity coefficient of the COVID-19 pandemic networks from February 2020 to mid August 2020.…”

“…Statistically, V t is the number of vertices of the pandemic network at time t. When a country has zero Y i,s in 14 consecutive days (s = t − 13, ...,t), it will be excluded from V t . Figure 2 shows the pandemic networks at t = 1 (4 February 2020), t = 36 (11 March 2020) and t = 67 (11 April 2020), from which we observe increasing level of connectedness in the pandemic networks from early February to early April 22 . To quantify the network connectedness and to summarize other network properties, we consider four network statistics at time t, E t (the number of edges), D t (the edge density), C t (global clustering coefficient), and AS t (assortativity).…”

“…Network analysis has been applied to analyzing scientific collaborations 14 , to econometrics for assessing systemic risk and contagion effects in financial markets 15,16 , to medical research in studying gene co-expression, disease co-occurrence and global epidemics [17][18][19][20] , and to analyzing text 21 . We extend the network approach in the literature 9,22 to study topological properties of COVID-19. There are three main features of our proposed network analysis.…”

Home quarantine is a useful strategy to prevent the coronavirus outbreak: Identifying the reasons for non-compliance in some Iranians

“…Some research has been done to inform the disease spread at different times and places. In studies, for disease spread visualization, graphs, media, maps and area cartograms have been used [ 7 , 8 , 9 , 10 ]. Researchers have evaluated the need for social distancing and personal hygiene to control the virus [ 11 , 12 , 13 , 14 , 15 ].…”