Evolution of mutating pathogens in networked populations

TL;DR

This paper models the evolution of mutating pathogens during epidemics on networked populations, analyzing how mutations affect spread dynamics and pathogen virulence, with applications to Covid-19 variants.

Contribution

It introduces a continuous mutation model focusing on pathogen mortality mean-time and combines analytical and numerical methods to study mutation effects on epidemic spread.

Findings

Natural selection favors less deadly pathogens over time

Different network structures influence mutation and spread dynamics

The model explains Covid-19 variant emergence

Abstract

Epidemic spreading over populations networks has been an important subject of research for several decades, and especially during the Covid-19 pandemic. Most epidemic outbreaks are likely to create multiple mutations during their spreading over the population. In this paper, we study the evolution of a pathogen which can mutate continuously during the epidemic spreading. We consider pathogens whose mutating parameter is the mortality mean-time, and study the evolution of this parameter over the spreading process. We use analytical methods to compute the dynamic equation of the epidemic and the conditions for it to spread. We also use numerical simulations to study the pathogen flow in this case, and to understand the mutation phenomena. We show that the natural selection leads to less violent pathogens becoming predominant in the population. We discuss a wide range of network structures and show how different effects are manifested in each case. We also applied our theory in the context of the Covid-19 pandemic, using relevant epidemiological data collected for this outbreak. We provided explanations for the variants spreading processes observed throughout this pandemic.