C19-TraNet: an empirical, global index-case transmission network of SARS-CoV-2

Abstract

Originating in Wuhan, the novel coronavirus, severe acute respiratory syndrome 2 (SARS-CoV-2), has astonished health-care systems across globe due to its rapid and simultaneous spread to the neighboring and distantly located countries. To gain the systems level understanding of the role of global transmission routes in the COVID-19 spread, in this study, we have developed the first, empirical, global, index-case transmission network of SARS-CoV-2 termed as C19-TraNet. We manually curated the travel history of country wise index-cases using government press releases, their official social media handles and online news reports to construct this C19-TraNet that is a spatio-temporal, sparse, growing network comprising of 187 nodes and 199 edges and follows a power-law degree distribution. To model the growing C19-TraNet, a novel stochastic scale free (SSF) algorithm is proposed that accounts for stochastic addition of both nodes as well as edges at each time step. A peculiar connectivity pattern in C19-TraNet is observed, characterized by a fourth degree polynomial growth curve, that significantly diverges from the average random connectivity pattern obtained from an ensemble of its 1,000 SSF realizations. Partitioning the C19-TraNet, using edge betweenness, it is found that most of the large communities are comprised of a heterogeneous mixture of countries belonging to different world regions suggesting that there are no spatial constraints on the spread of disease. This work characterizes the superspreaders that have very quickly transported the virus, through multiple transmission routes, to long range geographical locations alongwith their local neighborhoods.