Spreading processes in "post-epidemic" environments

Abstract

We analyze infection spreading processes in a system where only a fraction $p$ of individuals can be affected by disease, while remaining $1-p$ individuals are immune. Such a picture can emerge as a natural consequence of previously terminated epidemic process or arise in formerly vaccinated population. To this end, we apply the synchronous cellular automata algorithm studying stationary states and spatial patterning in SI, SIS and SIR models on a square lattice with the fraction $p$ of active sites. A concept of "safety patterns" of susceptible agents surrounded by immune individuals naturally arises in a proposed system, which plays an important role in the course of epidemic processes under consideration. Detailed analysis of distribution of such patterns is given, which in turn determine the fraction of infected agents in a stationary state $I^{\ast }(p)$. Estimates for the threshold values of the basic reproduction number $R_0^c$ as a function of active agents fraction $p$ are obtained as well. In particular, our results allow to predict the optimal fraction of individuals, needed to be vaccinated in advance in order to get the maximal values of unaffected agents in a course of epidemic process with a given curing rate.

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Cited by in corpus (3)

• Effect of two vaccine doses in the SEIR epidemic model using a stochastic cellular automaton
• Spreading processes in "post-epidemic" environments. II. Safety patterns on scale-free networks
• Modelling SARS-CoV-2 epidemics via compartmental and cellular automaton SEIRS model with temporal immunity and vaccination