Systematic comparison of coexistence in models of drug-sensitive and drug-resistant pathogen strains

Mulberry, Nicola; Rutherford, Alexander R.; Colijn, Caroline

doi:10.1016/j.tpb.2019.12.001

Cited by 11 publications

(13 citation statements)

References 22 publications

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“…We can apply this model to understand epidemiological competition dynamics between two strains under antibiotics, co-circulating in a host population with the possibility of coinfection, which constitutes a big study field in the epidemiological literature (Mulberry et al, 2019). In its simplest form, broad-spectrum antibiotic treatment can be modelled as an increase in global clearance rate of colonization γ, keeping fixed all other parameters between strains.…”

Section: Antibiotic Treatment Fitness Costs and Competitive Releasementioning

confidence: 99%

Disentangling how multiple traits drive 2 strain frequencies in SIS dynamics with coinfection

Tmt

Madec

Gjini³

2021

Preprint

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A general theory for competitive dynamics among many strains at the epidemiological level is required to understand polymorphisms in virulence, transmissibility, antibiotic resistance and other biological traits of infectious agents. Mathematical coinfection models have addressed specific systems, focusing on the criteria leading to stable coexistence or competitive exclusion, however, due to their complexity and nonlinearity, analytical solutions in coinfection models remain rare. Here we study a 2-strain SIS compartmental model with co-infection/co-colonization, incorporating multiple fitness dimensions under the same framework: variation in transmissibility, duration of carriage, pairwise susceptibilities to coinfection, coinfection duration, and transmission priority effects from mixed coinfection. Taking advantage of a singular perturbation approach, under the assumption of strain similarity, we expose how strain dynamics on a slow timescale are explicitly governed by a replicator equation which encapsulates all traits and their interplay. This allows us to predict explicitly not only the final epidemiological outcome of a given 2-player competition, but moreover, their entire frequency dynamics as a direct function of their relative variation and of strain-transcending global parameters. Based on mutual invasion fitnesses, we analyze and report rigorous results on transition phenomena in the 2-strain system, strongly mediated via coinfection prevalence. This framework offers a deeper analytical understanding of 2-strain competitive games in coinfection, with applications to virulence, interventions, antibiotic resistance, and social evolution theory.

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Section: Antibiotic Treatment Fitness Costs and Competitive Releasementioning

confidence: 99%

Disentangling how multiple traits drive 2 strain frequencies in SIS dynamics with coinfection

Tmt

Madec

Gjini³

2021

Preprint

View full text Add to dashboard Cite

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“…(Blanquart et al, 2018;Cobey et al, 2017;Colijn and Cohen, 2015;Donker et al, 2017;Lehtinen et al, 2017;van Bunnik and Woolhouse, 2017) Nevertheless, within-host bacterial competition remains a key mechanism of selection for antibiotic resistance dissemination, and an active area of research at the forefront of resistance modelling. (Lipsitch and Samore, 2002;Mulberry et al, 2020;Spicknall et al, 2013) For instance, the 'mixed-carriage' model by Davies et al demonstrates how intraspecific competition results in negative frequency-dependent selection for either of two competing strains, and provides a satisfying mechanistic explanation for widespread strain coexistence at the population level. (Davies et al, 2019) However, contemporary work has stopped short of evaluating consequences of between-species competition on resistance epidemiology.…”

Section: Introductionmentioning

confidence: 99%

“…Accounting for other forms of complexity in epidemiological models – from treatment intensity, to age-assortative contact behavior, to hospital referral networks, to animal-human interactions, to genetic linkage between resistance and non-resistance genes – has helped to unravel the many, disparate forces that contribute to drive the spread of resistance ( Blanquart et al, 2018 ; Cobey et al, 2017 ; Colijn and Cohen, 2015 ; Donker et al, 2017 ; Lehtinen et al, 2017 ; van Bunnik and Woolhouse, 2017 ). Nevertheless, within-host bacterial competition remains a key mechanism of selection for antibiotic resistance dissemination, and an active area of research at the forefront of resistance modeling ( Lipsitch and Samore, 2002 ; Mulberry et al, 2020 ; Spicknall et al, 2013 ). For instance, the ‘mixed-carriage’ model by Davies et al demonstrates how intraspecific competition results in negative frequency-dependent selection for either of two competing strains, and provides a satisfying mechanistic explanation for widespread strain coexistence at the population level ( Davies et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Microbiome-pathogen interactions drive epidemiological dynamics of antibiotic resistance: A modeling study applied to nosocomial pathogen control

Temime

Opatowski

2021

eLife

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The human microbiome can protect against colonization with pathogenic antibiotic-resistant bacteria (ARB), but its impacts on the spread of antibiotic resistance are poorly understood. We propose a mathematical modelling framework for ARB epidemiology formalizing within-host ARB-microbiome competition, and impacts of antibiotic consumption on microbiome function. Applied to the healthcare setting, we demonstrate a trade-off whereby antibiotics simultaneously clear bacterial pathogens and increase host susceptibility to their colonization, and compare this framework with a traditional strain-based approach. At the population level, microbiome interactions drive ARB incidence, but not resistance rates, reflecting distinct epidemiological relevance of different forces of competition. Simulating a range of public health interventions (contact precautions, antibiotic stewardship, microbiome recovery therapy) and pathogens (Clostridioides difficile, methicillin-resistant Staphylococcus aureus, multidrug-resistant Enterobacteriaceae) highlights how species-specific within-host ecological interactions drive intervention efficacy. We find limited impact of contact precautions for Enterobacteriaceae prevention, and a promising role for microbiome-targeted interventions to limit ARB spread.

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“…Existing models have proved useful in understanding specific aspects of co-infection, but here we develop a more general framework relating co-infection processes to ecological and evolutionary outcomes. This approach is particularly important because constructing appropriate models of co-infection is not trivial: theoretical work on co-infection between disease strains has shown that seemingly innocuous modelling choices can introduce unintended ecological differences between strains, with considerable impact on model outcomes [36][37][38]. In particular, model structures easily introduce mechanisms which unintentionally promote strain diversity (coexistence for free) [36].…”

Section: Introductionmentioning

confidence: 99%

Plasmid co-infection: linking biological mechanisms to ecological and evolutionary dynamics

Igler

Huisman

Siedentop

et al. 2021

Phil. Trans. R. Soc. B

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As infectious agents of bacteria and vehicles of horizontal gene transfer, plasmids play a key role in bacterial ecology and evolution. Plasmid dynamics are shaped not only by plasmid–host interactions but also by ecological interactions between plasmid variants. These interactions are complex: plasmids can co-infect the same cell and the consequences for the co-resident plasmid can be either beneficial or detrimental. Many of the biological processes that govern plasmid co-infection—from systems that exclude infection by other plasmids to interactions in the regulation of plasmid copy number—are well characterized at a mechanistic level. Modelling plays a central role in translating such mechanistic insights into predictions about plasmid dynamics and the impact of these dynamics on bacterial evolution. Theoretical work in evolutionary epidemiology has shown that formulating models of co-infection is not trivial, as some modelling choices can introduce unintended ecological assumptions. Here, we review how the biological processes that govern co-infection can be represented in a mathematical model, discuss potential modelling pitfalls, and analyse this model to provide general insights into how co-infection impacts ecological and evolutionary outcomes. In particular, we demonstrate how beneficial and detrimental effects of co-infection give rise to frequency-dependent selection on plasmid variants. This article is part of the theme issue ‘The secret lives of microbial mobile genetic elements’.

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Systematic comparison of coexistence in models of drug-sensitive and drug-resistant pathogen strains

Cited by 11 publications

References 22 publications

Disentangling how multiple traits drive 2 strain frequencies in SIS dynamics with coinfection

Disentangling how multiple traits drive 2 strain frequencies in SIS dynamics with coinfection

Microbiome-pathogen interactions drive epidemiological dynamics of antibiotic resistance: A modeling study applied to nosocomial pathogen control

Plasmid co-infection: linking biological mechanisms to ecological and evolutionary dynamics

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