Construction and stochastic scale-free modelling of empirical, global, index-case SARS-CoV-2 transmission network

Abstract

Abstract First reported in Wuhan, the novel coronavirus disease (COVID-19), caused by severe acute respiratory syndrome 2 (SARS-CoV-2) has astonished health-care systems across the globe due to its rapid and simultaneous spread to the neighbouring and distantly located countries. We constructed the first, global, spatio-temporal, index-case transmission network of SARS-CoV-2 or C19-TraNet consisting of $185$ nodes and $196$ edges, by manually curating their travel history information that allowed us to map multiple virus invasion routes, both short- as well as long-range, into different geographical locations. 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. C19-TraNet is characterized by a fourth-order polynomial growth of average connectivity having two growth phases, namely, a Chinese and a European wave separated by a stagnation phase that delayed overall growth by $51$ days, compared to $1000$ corresponding SSF models. Its community structure reveals a heterogeneous grouping of countries, from different WHO regions, suggesting easy invasion of SARS-CoV-2 to susceptible populations through short- as well as long-range transmission. Border control measures initially diminished Chinese wave, however, lack of coordinated actions, multiple transmission routes transported SARS-CoV-2 to remaining countries.