ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2014
Generalized linear models for identifying predictors of the evolutionary diffusion of viruses.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Phylodynamic and phylogeographic reconstruction of IBV lineages: diverse paths and determinants, one goal for control.Scientific reports · 2025Article
- Emergence of the B.1.214.2 SARS-CoV-2 lineage with an Omicron-like spike insertion and a unique upper airway immune signature.BMC infectious diseases · 2024Article
- Ecological Changes Exacerbating the Spread of Invasive Ticks has Driven the Dispersal of Severe Fever with Thrombocytopenia Syndrome Virus Throughout Southeast Asia.Molecular biology and evolution · 2024Article
- Ecogeographic Drivers of the Spatial Spread of Highly Pathogenic Avian Influenza Outbreaks in Europe and the United States, 2016-Early 2022.International journal of environmental research and public health · 2023Article
- Article
- Characterising routes of H5N1 and H7N9 spread in China using Bayesian phylogeographical analysis.Emerging microbes & infections · 2018Article
- Phylodynamic applications in 21Global health research and policy · 2017Article
- Combining phylogeography and spatial epidemiology to uncover predictors of H5N1 influenza A virus diffusion.Archives of virology · 2015Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Bioinformatics and phylogeography models use viral sequence data to analyze spread of epidemics and pandemics. However, few of these models have included analytical methods for testing whether certain predictors such as population density, rates of disease migration, and climate are drivers of spatial spread. Understanding the specific factors that drive spatial diffusion of viruses is critical for targeting public health interventions and curbing spread. In this paper we describe the application and evaluation of a model that integrates demographic and environmental predictors with molecular sequence data. The approach parameterizes evolutionary spread of RNA viruses as a generalized linear model (GLM) within a Bayesian inference framework using Markov chain Monte Carlo (MCMC). We evaluate this approach by reconstructing the spread of H5N1 in Egypt while assessing the impact of individual predictors on evolutionary diffusion of the virus.
Identifiers
25717395PMC4333690What OpenQuestion holds
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.