ArticlePLoS computational biology2016
Quantifying and Mitigating the Effect of Preferential Sampling on Phylodynamic Inference.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Optimizing genomic sampling for demographic and epidemiological inference with Markov decision processes.Genetics · 2026Article
- MASCOT-Skyline integrates population and migration dynamics to enhance phylogeographic reconstructions.PLoS computational biology · 2025Article
- Bayesian phylodynamic inference of population dynamics with dormancy.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Accounting for reporting delays in real-time phylodynamic analyses with preferential sampling.PLoS computational biology · 2025Article
- Bayesian phylodynamic inference of population dynamics with dormancy.bioRxiv : the preprint server for biology · 2025Article
- The emergence of NY10: insights into the 2012 West Nile Virus outbreak in the United States.Virus evolution · 2025Article
- Interrogating Genomes and Geography to Unravel Multiyear Vesicular Stomatitis Epizootics.Viruses · 2024Article
- ReMASTER: improved phylodynamic simulation for BEAST 2.7.Bioinformatics (Oxford, England) · 2024Article
- Mechanistic phylodynamic models do not provide conclusive evidence that non-avian dinosaurs were in decline before their final extinction.Cambridge prisms. Extinction · 2024Article
- Fitting stochastic epidemic models to gene genealogies using linear noise approximation.The annals of applied statistics · 2023Article
- adaPop: Bayesian inference of dependent population dynamics in coalescent models.PLoS computational biology · 2023Article
- Data integration in Bayesian phylogenetics.Annual review of statistics and its application · 2023Article
- Predicting the evolution of the Lassa virus endemic area and population at risk over the next decades.Nature communications · 2022Article
- Using multiple sampling strategies to estimate SARS-CoV-2 epidemiological parameters from genomic sequencing data.Nature communications · 2022Article
- Statistical Challenges in Tracking the Evolution of SARS-CoV-2.Statistical science : a review journal of the Institute of Mathematical Statistics · 2022Article
- Hunting alters viral transmission and evolution in a large carnivore.Nature ecology & evolution · 2022Article
- A computationally tractable birth-death model that combines phylogenetic and epidemiological data.PLoS computational biology · 2022Article
- Epidemiological inference from pathogen genomes: A review of phylodynamic models and applications.Virus evolution · 2022Review
- Adaptive Preferential Sampling in Phylodynamics With an Application to SARS-CoV-2.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2022Article
- Accounting for spatial sampling patterns in Bayesian phylogeography.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
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Authors and funding
5 authors.
Funding
Abstract
Phylodynamics seeks to estimate effective population size fluctuations from molecular sequences of individuals sampled from a population of interest. One way to accomplish this task formulates an observed sequence data likelihood exploiting a coalescent model for the sampled individuals' genealogy and then integrating over all possible genealogies via Monte Carlo or, less efficiently, by conditioning on one genealogy estimated from the sequence data. However, when analyzing sequences sampled serially through time, current methods implicitly assume either that sampling times are fixed deterministically by the data collection protocol or that their distribution does not depend on the size of the population. Through simulation, we first show that, when sampling times do probabilistically depend on effective population size, estimation methods may be systematically biased. To correct for this deficiency, we propose a new model that explicitly accounts for preferential sampling by modeling the sampling times as an inhomogeneous Poisson process dependent on effective population size. We demonstrate that in the presence of preferential sampling our new model not only reduces bias, but also improves estimation precision. Finally, we compare the performance of the currently used phylodynamic methods with our proposed model through clinically-relevant, seasonal human influenza examples.
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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.