ArticleInfection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases2018
The effects of random taxa sampling schemes in Bayesian virus phylogeography.
Article in Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed, 15 citations in OpenAlex.
- Molecular epidemiology of SARS-CoV-2 genome sentinel surveillance in commercial COVID-19 testing sites targeting asymptomatic individuals during Japan's seventh epidemic wave.Scientific reports · 2024Article
- Changing genomic epidemiology of COVID-19 in long-term care facilities during the 2020-2022 pandemic, Washington State.BMC public health · 2024Article
- Sentinel Surveillance System Implementation and Evaluation for SARS-CoV-2 Genomic Data, Washington, USA, 2020-2021.Emerging infectious diseases · 2023Review
- Impact and mitigation of sampling bias to determine viral spread: Evaluating discrete phylogeography through CTMC modeling and structured coalescent model approximations.Virus evolution · 2023Article
- The impact of sampling bias on viral phylogeographic reconstruction.PLOS global public health · 2022Article
- Going back to the roots: Evaluating Bayesian phylogeographic models with discrete trait uncertainty.Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases · 2020Article
- Determinants of dengue virus dispersal in the Americas.Virus evolution · 2020Article
- Agricultural and geographic factors shaped the North American 2015 highly pathogenic avian influenza H5N2 outbreak.PLoS pathogens · 2020Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Public health researchers are often tasked with accurately and quickly identifying the location and time when an epidemic originated from a representative sample of nucleotide sequences. In this paper, we investigate multiple approaches to subsampling the sequence set when employing a Bayesian phylogeographic generalized linear model. Our results indicate that near-categorical posterior MCC estimates on the root can be obtained with replicate runs using 25-50% of the sequence data, and that including 90% of sequences does not necessarily entail more accurate inferences. We present the first analysis of predictor signal suppression and show how the ability to detect the influence of predictor variables is limited when sample size predictors are included in the models.
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Registered trials
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.