Infection in social networks: using network analysis to identify high-risk individuals

摘要: Simulation studies using susceptible-infectious-recovered models were conducted to estimate individuals' risk of infection and time in small-world randomly mixing networks. Infection transmitted more rapidly but ultimately resulted fewer infected individuals the small-world, compared with random, network. The ability measures network centrality identify high-risk was also assessed. "Centrality" describes an individual's position a population; numerous parameters are available assess this attribute. Here, authors use degree (number contacts), random-walk betweenness (a measure proportion times individual lies on path between other individuals), shortest-path (the shortest farness sum number steps all individuals). Each associated simulated outbreaks. In networks examined, (which is most readily measured) at least as good predicting infection. Identification central populations may be used inform surveillance control strategies.