ArticleVirus evolution2019
Inferring time-dependent migration and coalescence patterns from genetic sequence and predictor data in structured populations.
Article in Virus evolution, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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28 citing papers in PubMed.
- Ancestral state reconstruction with discrete characters using deep learning.bioRxiv : the preprint server for biology · 2026Article
- Phylogenetic insights into the transmission dynamics of arthropod-borne viruses.Nature reviews. Genetics · 2026Review
- Inferring epidemiological parameters under an infectious phylogeography model with visitor dynamics.medRxiv : the preprint server for health sciences · 2025Article
- phyddle: Software for Exploring Phylogenetic Models with Deep Learning.Systematic biology · 2025Article
- Learning Model Parameter Dynamics in a Combination Therapy for Bladder Cancer from Sparse Biological Data.ArXiv · 2025Article
- Modeling Adoptive Cell Therapy in Bladder Cancer from Sparse Biological Data using PINNs.ArXiv · 2025Article
- MASCOT-Skyline integrates population and migration dynamics to enhance phylogeographic reconstructions.PLoS computational biology · 2025Article
- High genetic diversity of HIV-1 pol region and molecular transmission networks among people living with HIV-1 in Haikou, South China, 2005-2022.BMC infectious diseases · 2025Article
- Comparative performance of viral landscape phylogeography approaches.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- phyddle: software for exploring phylogenetic models with deep learning.bioRxiv : the preprint server for biology · 2025Article
- Quantifying Time-Dependent Predictors for the International Spatial Spread of Highly Pathogenic Avian Influenza H5NX: Focus on Trade and Surveillance Efforts.Transboundary and emerging diseases · 2025Article
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- Local-scale phylodynamics reveal differential community impact of SARS-CoV-2 in a metropolitan US county.PLoS pathogens · 2024Article
- Early underdetected dissemination across countries followed by extensive local transmission propelled the 2022 mpox epidemic.medRxiv : the preprint server for health sciences · 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
- Early Genomic Surveillance and Phylogeographic Analysis of Getah Virus, a Reemerging Arbovirus, in Livestock in China.Journal of virology · 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
- Phylodynamic analysis of the highly pathogenic avian influenza H5N8 epidemic in France, 2016-2017.Transboundary and emerging diseases · 2022Article
- New Phylogenetic Models Incorporating Interval-Specific Dispersal Dynamics Improve Inference of Disease Spread.Molecular biology and evolution · 2022Article
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3 authors.
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Abstract
Population dynamics can be inferred from genetic sequence data by using phylodynamic methods. These methods typically quantify the dynamics in unstructured populations or assume migration rates and effective population sizes to be constant through time in structured populations. When considering rates to vary through time in structured populations, the number of parameters to infer increases rapidly and the available data might not be sufficient to inform these. Additionally, it is often of interest to know what predicts these parameters rather than knowing the parameters themselves. Here, we introduce a method to infer the predictors for time-varying migration rates and effective population sizes by using a generalized linear model (GLM) approach under the marginal approximation of the structured coalescent. Using simulations, we show that our approach is able to reliably infer the model parameters and its predictors from phylogenetic trees. Furthermore, when simulating trees under the structured coalescent, we show that our new approach outperforms the discrete trait GLM model. We then apply our framework to a previously described Ebola virus dataset, where we infer the parameters and its predictors from genome sequences while accounting for phylogenetic uncertainty. We infer weekly cases to be the strongest predictor for effective population size and geographic distance the strongest predictor for migration. This approach is implemented as part of the BEAST2 package MASCOT, which allows us to jointly infer population dynamics, i.e. the parameters and predictors, within structured populations, the phylogenetic tree, and evolutionary parameters.
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