We analyse the structure and resilience of a massive encounter network generated from commuters who share the same bus ride on a single day within a metropolitan city. We demonstrate our analysis using a case study of the network created by all the commuters who utilised the public bus system during a typical weekday in the whole of Singapore using smartcard data. We show that the network structure is of random-exponential type with small world features rather than a scale-free network. Within one day, 99.97% of all commuters are connected approximately within 7 steps of each other. We report on how this network structure changes upon application of a threshold based on the encounter duration (TE). Among others, we demonstrate that the total number of connected commuters reduces by 50% when we set the threshold for TE to be 15mins. We then assess the dynamics of infection spreading by comparing the effect of both random and targeted removal strategies of commuters. By assuming that the network characteristic is invariant day after day, our simulation indicates that without node removal, 99% of the commuter network will be infected within 7 days of the onset of infection. While a targeted removal strategy is demonstrated to delay the onset of the maximum number of infected individuals, it is not able to effectively isolate commuters away from being eventually infected.
Ramli, M. A., & Monterola, C. P. (2015). The resilience of the encounter network of commuters for a metropolitan public bus system. In Procedia Computer Science (Vol. 51, pp. 2117–2126). Elsevier B.V. https://doi.org/10.1016/j.procs.2015.05.482