ArticleVirus evolution2021
Cross-scale dynamics and the evolutionary emergence of infectious diseases.
Article in Virus evolution, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
13 citing papers in PubMed.
- Microbial Primer: Understanding the exponential growth of epidemics.Microbiology (Reading, England) · 2026Review
- Within- and between-host evolutionary effects on viral oncogenicity.Virus evolution · 2025Article
- 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
- Quantifying the relationship between within-host dynamics and transmission for viral diseases of livestock.Journal of the Royal Society, Interface · 2024Article
- Analysis of Virus-Derived siRNAs in Strawberry Plants Co-Infected with Multiple Viruses and Their Genotypes.Plants (Basel, Switzerland) · 2023Article
- Contact-number-driven virus evolution: A multi-level modeling framework for the evolution of acute or persistent RNA virus infection.PLoS computational biology · 2023Article
- The mechanism shaping the logistic growth of mutation proportion in epidemics at population scale.Infectious Disease Modelling · 2023Article
- Bridging landscape ecology and urban science to respond to the rising threat of mosquito-borne diseases.Nature ecology & evolution · 2022Review
- The non-pharmaceutical interventions may affect the advantage in transmission of mutated variants during epidemics: A conceptual model for COVID-19.Journal of theoretical biology · 2022Article
- The co-circulating transmission dynamics of SARS-CoV-2 Alpha and Eta variants in Nigeria: A retrospective modeling study of COVID-19.Journal of global health · 2021Article
- A general framework for modelling the impact of co-infections on pathogen evolution.Journal of the Royal Society, Interface · 2019Article
- Inferring infection hazard in wildlife populations by linking data across individual and population scales.Ecology letters · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
When emerging pathogens encounter new host species for which they are poorly adapted, they must evolve to escape extinction. Pathogens experience selection on traits at multiple scales, including replication rates within host individuals and transmissibility between hosts. We analyze a stochastic model linking pathogen growth and competition within individuals to transmission between individuals. Our analysis reveals a new factor, the cross-scale reproductive number of a mutant virion, that quantifies how quickly mutant strains increase in frequency when they initially appear in the infected host population. This cross-scale reproductive number combines with viral mutation rates, single-strain reproductive numbers, and transmission bottleneck width to determine the likelihood of evolutionary emergence, and whether evolution occurs swiftly or gradually within chains of transmission. We find that wider transmission bottlenecks facilitate emergence of pathogens with short-term infections, but hinder emergence of pathogens exhibiting cross-scale selective conflict and long-term infections. Our results provide a framework to advance the integration of laboratory, clinical, and field data in the context of evolutionary theory, laying the foundation for a new generation of evidence-based risk assessment of emergence threats.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.