ArticleCurrent protocols2021
Bayesian Phylogeographic Analysis Incorporating Predictors and Individual Travel Histories in BEAST.
Article in Current protocols, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- Identification and genomic characterization of BA.3.2: a highly divergent BA.3-related SARS-CoV-2 lineage from southern Africa.Virus evolution · 2026Article
- Article
- Intensive transmission in wild, migratory birds drove rapid geographic dissemination and repeated spillovers of H5N1 into agriculture in North America.bioRxiv : the preprint server for biology · 2025Article
- Comparative Evolutionary Epidemiology of SARS-CoV-2 Delta and Omicron Variants in Kuwait.Viruses · 2024Article
- 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
- Dengue Virus Serotype 3 Origins and Genetic Dynamics, Jamaica.Emerging infectious diseases · 2024Article
- Estimating geographical spread ofMicrobial genomics · 2024Article
- Article
- The Principles of SARS-CoV-2 Intervariant Competition Are Exemplified in the Pre-Omicron Era of the Colombian Epidemic.Microbiology spectrum · 2023Article
- 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
- Global dissemination of influenza A virus is driven by wild bird migration through arctic and subarctic zones.Molecular ecology · 2023Article
- Impact and mitigation of sampling bias to determine viral spread: Evaluating discrete phylogeography through CTMC modeling and structured coalescent model approximations.Virus evolution · 2023Article
- Article
- The early SARS-CoV-2 epidemic in Senegal was driven by the local emergence of B.1.416 and the introduction of B.1.1.420 from Europe.Virus evolution · 2022Article
- SPREAD 4: online visualisation of pathogen phylogeographic reconstructions.Virus evolution · 2022Article
- The phylodynamics of SARS-CoV-2 during 2020 in Finland.Communications medicine · 2022Article
- Emergence and spread of SARS-CoV-2 lineage B.1.620 with variant of concern-like mutations and deletions.Nature communications · 2021Article
- Genomic sequencing of SARS-CoV-2 in Rwanda reveals the importance of incoming travelers on lineage diversity.Nature communications · 2021Article
- Emerging SARS-CoV-2 Diversity Revealed by Rapid Whole-Genome Sequence Typing.Genome biology and evolution · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Advances in sequencing technologies have tremendously reduced the time and costs associated with sequence generation, making genomic data an important asset for routine public health practices. Within this context, phylogenetic and phylogeographic inference has become a popular method to study disease transmission. In a Bayesian context, these approaches have the benefit of accommodating phylogenetic uncertainty, and popular implementations provide the possibility to parameterize the transition rates between locations as a function of epidemiological and ecological data to reconstruct spatial spread while simultaneously identifying the main factors impacting the spatial spread dynamics. Recent developments enable researchers to make use of travel history data of infected individuals in the reconstruction of pathogen spread, offering increased inference accuracy and mitigating sampling bias. Here, we describe a detailed workflow to reconstruct the spatial spread of a pathogen through Bayesian phylogeographic analysis in discrete space using these novel approaches, implemented in BEAST. The individual protocols focus on how to incorporate molecular data, covariates of spread, and individual travel history data into the analysis. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Creating a SARS-CoV-2 MSA using sequences from GISAID Basic Protocol 2: Setting up a discrete trait phylogeographic reconstruction in BEAUti Basic Protocol 3: Phylogeographic reconstruction incorporating travel history information Basic Protocol 4: Visualizing ancestral spatial trajectories for specific taxa.
Indexed as
Identifiers
What 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.