ArticleJournal of the Royal Society, Interface2021
Inferring environmental transmission using phylodynamics: a case-study using simulated evolution of an enteric pathogen.
Article in Journal of the Royal Society, Interface, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
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Who cites it
6 citing papers in PubMed.
- Soil management practices shape the abundance, diversity, and spread of antimicrobial resistance.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Advances in Contact Tracing: A Bayesian Framework to Improve Network and Transmission Models.Journal of molecular evolution · 2026Review
- A Bayesian Framework for the Network Analysis of Transmission Dynamics in Infectious Disease.Journal of molecular evolution · 2026Article
- Bayesian estimation of transmission networks for infectious diseases.Journal of mathematical biology · 2025Article
- Genomic surveillance for antimicrobial resistance - a One Health perspective.Nature reviews. Genetics · 2024Review
- Leveraging insect-specific viruses to elucidate mosquito population structure and dynamics.PLoS pathogens · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Indirect (environmental) and direct (host-host) transmission pathways cannot easily be distinguished when they co-occur in epidemics, particularly when they occur on similar time scales. Phylodynamic reconstruction is a potential approach to this problem that combines epidemiological information (temporal, spatial information) with pathogen whole-genome sequencing data to infer transmission trees of epidemics. However, factors such as differences in mutation and transmission rates between host and non-host environments may obscure phylogenetic inference from these methods. In this study, we used a network-based transmission model that explicitly models pathogen evolution to simulate epidemics with both direct and indirect transmission. Epidemics were simulated according to factorial combinations of direct/indirect transmission proportions, host mutation rates and conditions of environmental pathogen growth. Transmission trees were then reconstructed using the phylodynamic approach SCOTTI (structured coalescent transmission tree inference) and evaluated. We found that although insufficient diversity sets a lower bound on when accurate phylodynamic inferences can be made, transmission routes and assumed pathogen lifestyle affected pathogen population structure and subsequently influenced both reconstruction success and the likelihood of direct versus indirect pathways being reconstructed. We conclude that prior knowledge of the likely ecology and population structure of pathogens in host and non-host environments is critical to fully using phylodynamic techniques.
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Registered trials
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