Individual risk perception and empirical social structures shape the dynamics of infectious disease outbreaks
Fig 2
Population fraction of risk-deniers affects epidemics in synthetic networks models.
a: Peak of hospitalized population as a function of the fraction of population of infectious risk-deniers α, for, respectively, uniformly random (ER), Barabási-Albert (BA), Watts and Strogatz (WS) and stochastic block model (SBM4). Each dot represents the average measure across 50 dynamical samples of a single network realization, box-plots show quartiles of distributions across 50 network realizations. We perform a Kruskal-Wallis H-test to test the null hypothesis that the population medians at fixed α are equal. Post hoc pairwise comparisons between groups at fixed α are required to determine which distributions are different. To this aim, for those α values with Kruskal-Wallis H-test p value p ≤ 0.05, we use post-hoc pairwise comparisons between distributions. In particular we use a Mann-Whitney test, because groups are independent, corrected for multiple comparisons. Significance results are reported as: ****: p ≤ 10−4, ***: 10−4 < p ≤ 10−3, **: 10−3 < p ≤ 10−2, *: 10−2 < p ≤ 0.05. Results show that the presence of communities, as in SBM4, significantly decreases the hospitalized peak for all α fractions, except for α = 0.9. b: Peak of hospitalized population evaluated with respect to the one estimated at α = 0.0, as percentage increase. At high fractions of risk-deniers (α ≥ 0.9), community structures give rise to higher increase in hospitalizations peak with respect models with different topology.