Evaluation of strategies to mitigate contagion spread using social network characteristics

Computer simulation is an effective tool for assessing mitigation strategies, with recent trends concentrating on agent-based techniques. These methods require high computational efforts in order to simulate enough scenarios for statistical significance. The population individuals and their contacts determined by agent-based simulations form a social network. For some network structures it is possible to gain high accuracy estimates of contagion spread based on the connection structure of the network, an idea that is utilized in this work. A representative social network constructed from the 2006 census of the Greater Toronto Area (Ontario, Canada) of 5 million individuals in 1.8 million households is used to demonstrate the efficacy of our approach. We examine the effects of six mitigation strategies with respect to their ability to contain disease spread as indicated by pre- and post-vaccination reproduction numbers, mean local clustering coefficients and degree distributions. One outcome of the analysis provides evidence supporting the design of mitigation strategies that aim to fragment the population into similarly sized components. While our analysis is framed in the context of pandemic disease spread, the approach is applicable to any contagion such as computer viruses, rumours, social trends, and so on.

► Very large realistic network model of the Greater Toronto Area is considered.
► Efficient evaluation of contagion spread mitigation using only network characteristics.
► Increasing proportion of vaccination has minimal decrease on largest network component.
► Increasing proportion of vaccination has minimal impact on reproductive rate.
► Current vaccination strategies are insufficient to significantly limit spread.