ArticlePhilosophical transactions of the Royal Society of London. Series B, Biological sciences2013
Multiple scales of selection influence the evolutionary emergence of novel pathogens.
Article in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Quasispecies theory and emerging viruses: challenges and applications.Npj viruses · 2024Review
- Spillover: Mechanisms, Genetic Barriers, and the Role of Reservoirs in Emerging Pathogens.Microorganisms · 2024Review
- The source of individual heterogeneity shapes infectious disease outbreaks.Proceedings. Biological sciences · 2022Article
- Infectious disease in an era of global change.Nature reviews. Microbiology · 2022Review
- Zoonotic and reverse zoonotic transmission of viruses between humans and pigs.APMIS : acta pathologica, microbiologica, et immunologica Scandinavica · 2021Review
- SARS-CoV-2: Cross-scale Insights from Ecology and Evolution.Trends in microbiology · 2021Review
- Cross-scale dynamics and the evolutionary emergence of infectious diseases.Virus evolution · 2021Article
- Understanding the emergence of bacterial pathogens in novel hosts.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019Article
- Dynamic and integrative approaches to understanding pathogen spillover.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019Article
- A general framework for modelling the impact of co-infections on pathogen evolution.Journal of the Royal Society, Interface · 2019Article
- Fogarty International Center collaborative networks in infectious disease modeling: Lessons learnt in research and capacity building.Epidemics · 2019Review
- Symbiont evolution during the free-living phase can improve host colonization.Microbiology (Reading, England) · 2019Article
- MERS-CoV spillover at the camel-human interface.eLife · 2018Article
- The effect of the infection within the individual host on its propagation in the population.Infectious Disease Modelling · 2018Article
- Predicting virus emergence amid evolutionary noise.Open biology · 2017Review
- Inferring infection hazard in wildlife populations by linking data across individual and population scales.Ecology letters · 2017Article
- Article
- Pathogen population bottlenecks and adaptive landscapes: overcoming the barriers to disease emergence.Proceedings. Biological sciences · 2016Article
- The role of pathogen shedding in linking within- and between-host pathogen dynamics.Mathematical biosciences · 2015Article
- Expected Effect of Deleterious Mutations on Within-Host Adaptation of Pathogens.Journal of virology · 2015Article
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4 authors.
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Abstract
When pathogens encounter a novel environment, such as a new host species or treatment with an antimicrobial drug, their fitness may be reduced so that adaptation is necessary to avoid extinction. Evolutionary emergence is the process by which new pathogen strains arise in response to such selective pressures. Theoretical studies over the last decade have clarified some determinants of emergence risk, but have neglected the influence of fitness on evolutionary rates and have not accounted for the multiple scales at which pathogens must compete successfully. We present a cross-scale theory for evolutionary emergence, which embeds a mechanistic model of within-host selection into a stochastic model for emergence at the population scale. We explore how fitness landscapes at within-host and between-host scales can interact to influence the probability that a pathogen lineage will emerge successfully. Results show that positive correlations between fitnesses across scales can greatly facilitate emergence, while cross-scale conflicts in selection can lead to evolutionary dead ends. The local genotype space of the initial strain of a pathogen can have disproportionate influence on emergence probability. Our cross-scale model represents a step towards integrating laboratory experiments with field surveillance data to create a rational framework to assess emergence risk.
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