On the expected total number of infections for virus spread on a finite network.

*(English)*Zbl 1322.60206Summary: In this work we consider a simple SIR infection spread model on a finite population of \(n\) agents represented by a finite graph \(G\). Starting with a fixed set of initial infected vertices the infection spreads in discrete time steps, where each infected vertex tries to infect its neighbors with a fixed probability \(\beta\in(0,1)\), independently of others. It is assumed that each infected vertex dies out after a unit time and the process continues till all infected vertices die out. This model was first studied by M. Draief et al. [Ann. Appl. Probab. 18, No. 2, 359–378 (2008; Zbl 1137.60051)]. In this work, we find a simple lower bound on the expected number of ever infected vertices using a breath-first search algorithm and show that it asymptotically performs better for a fairly large class of graphs than the upper bounds obtained in [loc. cit.]. As a by product, we also derive the asymptotic value of the expected number of the ever infected vertices when the underlying graph is the random \(r\)-regular graph and \(\beta<\frac{1}{r-1}\).

##### MSC:

60K35 | Interacting random processes; statistical mechanics type models; percolation theory |

60J80 | Branching processes (Galton-Watson, birth-and-death, etc.) |

60J85 | Applications of branching processes |

05C80 | Random graphs (graph-theoretic aspects) |

92D30 | Epidemiology |

##### Keywords:

percolation; finite graphs; random \(r\)-regular graphs; virus infections; breath-first search; local weak convergence; susceptible infected removed model
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\textit{A. Bandyopadhyay} and \textit{F. Sajadi}, Ann. Appl. Probab. 25, No. 2, 663--674 (2015; Zbl 1322.60206)

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