Individual risk perception and empirical social structures shape the dynamics of infectious disease outbreaks
Fig 3
Population fraction of risk-deniers affects epidemics in real networks.
a: Scatter plot describing number of contacts (edges, E) as a function of the number of individuals (nodes, N) for real-world social network data set (n = 187 networks) considered in this study. Each dot is a single network, its color identifies the database source and its size is proportional to the connectivity heterogeneity measured by the Gini coefficient (see Synthetic and real-world network data for further details). Inset: distributions of modularity show that the considered data sets of real social networks spans from structures characterized by the presence of communities (high modularity) to structures with loosely connected communities (low modularity). b: Temporal evolution of the population of hospitalized patients in real networks. Model dynamics is evaluated at a fixed fraction (α = 0.5) of risk-deniers. Color identifies the database source as shown in panel (a). Values of the population at its maximum height correspond to the peak. c: Peak of hospitalized patients as a function of the population fraction of infectious risk-deniers α for data sets with multiple network instances. Each dot represents results in a single network, box-plots show quartiles of distributions in each data set. Statistical tests and significances as in Fig 2. Results show that networks characterized by high values of degree heterogeneity and modularity, like the ones in Sociopatterns data set, show a peak of hospitalized patients that is significantly lower than peak evaluated in other real networks. d: Peak of hospitalized patients evaluated with respect to the one estimated at α = 0.0, as percentage increase. At high fractions of risk-deniers (α ≥ 0.7), data sets characterized by community structures and high heterogeneity values show an higher increase in hospitalizations peak with respect data set with low modularity and heterogeneity values.