Literature DB >> 29288702

Coupled, multi-strain epidemic models of mutating pathogens.

Michael T Meehan1, Daniel G Cocks2, James M Trauer3, Emma S McBryde4.   

Abstract

We introduce and analyze coupled, multi-strain epidemic models designed to simulate the emergence and dissemination of mutant (e.g. drug-resistant) pathogen strains. In particular, we investigate the mathematical and biological properties of a general class of multi-strain epidemic models in which the infectious compartments of each strain are coupled together in a general manner. We derive explicit expressions for the basic reproduction number of each strain and highlight their importance in regulating the system dynamics (e.g. the potential for an epidemic outbreak) and the existence of nonnegative endemic solutions. Importantly, we find that the basic reproduction number of each strain is independent of the mutation rates between the strains - even under quite general assumptions for the form of the infectious compartment coupling. Moreover, we verify that the coupling term promotes strain coexistence (as an extension of the competitive exclusion principle) and demonstrate that the strain with the greatest reproductive capacity is not necessarily the most prevalent. Finally, we briefly discuss the implications of our results for public health policy and planning.
Copyright © 2017 Elsevier Inc. All rights reserved.

Keywords:  Coupled; Drug resistance; Evolution; Multi-strain

Mesh:

Year:  2017        PMID: 29288702     DOI: 10.1016/j.mbs.2017.12.006

Source DB:  PubMed          Journal:  Math Biosci        ISSN: 0025-5564            Impact factor:   2.144


  2 in total

1.  On the probability of strain invasion in endemic settings: Accounting for individual heterogeneity and control in multi-strain dynamics.

Authors:  Michael T Meehan; Robert C Cope; Emma S McBryde
Journal:  J Theor Biol       Date:  2019-12-06       Impact factor: 2.691

2.  Analysis of multi-strain infection of vaccinated and recovered population through epidemic model: Application to COVID-19.

Authors:  Olusegun Michael Otunuga
Journal:  PLoS One       Date:  2022-07-29       Impact factor: 3.752

  2 in total

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