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Proofs of sharp phase transition and noise sensitivity in percolation have been significantly simplified by the use of randomized algorithms, via the OSSS inequality (proved by O'Donnell et al. and the Schramm–Steif inequality for the Fourier‐Walsh coefficients of functions defined on the Boolean hypercube. In this article, we prove intrinsic versions of the OSSS and Schramm–Steif inequalities for functionals of a general Poisson process, and apply these new estimates to deduce sufficient conditions—expressed in terms of randomized stopping sets—yielding sharp phase transitions, quantitative noise sensitivity, exceptional times and bounds on critical windows for monotonic Boolean Poisson functions. Our analysis is based on a new general definition of "stopping set", not requiring any topological property for the underlying measurable space, as well as on the new concept of a "continuous‐time decision tree", for which we establish several fundamental properties. We apply our findings to the k$$ k $$‐percolation of the Poisson Boolean model and to the Poisson‐based confetti percolation with bounded random grains. In these two models, we reduce the proof of sharp phase transitions for percolation, and of noise sensitivity for crossing events, to the construction of suitable randomized stopping sets and the computation of one‐arm probabilities at criticality. This enables us to settle an open problem suggested by Ahlberg et al. (a special case of which was conjectured earlier by Ahlberg et al. on noise sensitivity of crossing events for the planar Poisson Boolean model with random balls whose radius distribution has finite (2+α)$$ \left(2+\alpha \right) $$‐moments and also show the same for planar confetti percolation model with bounded random balls. We also prove that critical probability is 1/2$$ 1/2 $$ for the planar confetti percolation model with bounded, π/2$$ \pi /2 $$‐rotation invariant and reflection invariant random grains. Such a result was conjectured by Benjamini and Schramm in the case of fixed balls and proved by Müller, Hirsch and Ghosh and Roy in the case of balls, boxes and random boxes, respectively; our results contain all previous findings as special cases. [ABSTRACT FROM AUTHOR]

We present an improvement in estimating the epidemic threshold for a modified susceptible-infected-removed model. We applied the pair heterogeneous mean-field theory on heterogeneous quenched networks. Our theoretical results are compared with extensive Monte Carlo simulations concerning the model dynamics on random regular and power-law networks. The epidemic thresholds obtained using pair heterogeneous mean-field theory agree with simulation results. We analyze the critical scaling of the model as well. At last, epidemic outbreaks emerge for infective probabilities above the critical threshold. [ABSTRACT FROM AUTHOR]

We present a finite-size scaling theory of the asynchronous susceptible–infected–removed model on scale-free networks, which models epidemic outbreaks and gives a non-vanishing critical threshold. The susceptible–infected–removed model can be mapped in a bond percolation process, as stressed by P. Grassberger, allowing us to compare the critical behavior of site and bond universality classes on networks. We employ a droplet heterogeneous mean-field theory, adding the effect of an external field defined as the initial number of infected individuals. One can choose the external field scaling as N − 1 , where N is the number of network nodes, and compare theoretical results with simulations on the uncorrelated model and Barabasi–Albert networks. The system presents a percolating phase transition where the critical behavior obeys the mean-field universality class, as we show theoretically and by extensive simulations. • We considered the asynchronous SIR model in uncorrelated scale-free networks. • The system presents a percolative phase transition with mean-field exponents. • We show a heterogeneous mean-field theory of the system's finite-size behavior. [ABSTRACT FROM AUTHOR]