Spreading processes in "post-epidemic" environments

TL;DR

This paper investigates how infection spreads in populations with partial immunity, analyzing stationary states and spatial patterns to determine thresholds and optimal vaccination strategies in post-epidemic environments.

Contribution

It introduces the concept of 'safety patterns' and provides detailed analysis of epidemic thresholds and vaccination optimization in partially immune populations.

Findings

Identification of 'safety patterns' influencing epidemic spread

Estimation of critical reproduction number thresholds as a function of immunity fraction

Prediction of optimal vaccination levels for maximum unaffected individuals

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 $I^*(p)$. 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.