Endemic state equivalence between non-Markovian SEIS and Markov SIS model in complex networks

TL;DR

This paper establishes a formal relationship between non-Markovian SEIS and Markov SIS epidemic models on complex networks, simplifying analysis and threshold determination for non-Markovian processes.

Contribution

It proves that stationary states of non-Markovian SEIS can be derived from Markov SIS, providing a new method to analyze complex epidemic models.

Findings

Stationary states of non-Markovian SEIS relate to Markov SIS stationary states.

Epidemic threshold of non-Markovian SEIS can be derived from Markov SIS.

Numerical simulations confirm analytical results.

Abstract

In the light of several major epidemic events that emerged in the past two decades, and emphasized by the COVID-19 pandemics, the non-Markovian spreading models occurring on complex networks gained significant attention from the scientific community. Following this interest, in this article, we explore the relations that exist between the non-Markovian SEIS (Susceptible--Exposed--Infectious--Susceptible) and the classical Markov SIS, as basic re-occurring virus spreading models in complex networks. We investigate the similarities and seek for equivalences both for the discrete-time and the continuous-time forms. First, we formally introduce the continuous-time non-Markovian SEIS model, and derive the epidemic threshold in a strict mathematical procedure. Then we present the main result of the paper that, providing certain relations between process parameters hold, the stationary-state solutions of the status probabilities in the non-Markovian SEIS may be found from the stationary state probabilities of the Markov SIS model. This result has a two-fold significance. First, it simplifies the computational complexity of the non-Markovian model in practical applications, where only the stationary distribution of the state probabilities is required. Next, it defines the epidemic threshold of the non-Markovian SEIS model, without the necessity of a thrall mathematical analysis. We present this result both in analytical form, and confirm the result trough numerical simulations. Furthermore, as of secondary importance, in an analytical procedure we show that each Markov SIS may be represented as non-Markovian SEIS model.