ArticleMolecular biology and evolution2021
Fundamental Identifiability Limits in Molecular Epidemiology.
Article in Molecular biology and evolution, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- The present does not reflect the past: geographic mismatches between fossil and modern records are widespread across the tree of life.Proceedings. Biological sciences · 2026Review
- Estimating Divergence Times and Diversification Rates with the Unresolved Fossilized Birth-Death Process.bioRxiv : the preprint server for biology · 2026Article
- Unidentifiability and false-positive inference in state-dependent diversification models.Nature communications · 2026Article
- Article
- The effects of cryptic diversity on diversification dynamics analyses in Crocodylia.Proceedings. Biological sciences · 2025Article
- Reading Yule in light of the history and present of macroevolution.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Article
- The Fossilized Birth-Death Model Is Identifiable.Systematic biology · 2025Article
- Evolutionary and epidemic dynamics of COVID-19 in Germany exemplified by three Bayesian phylodynamic case studies.Bioinformatics and biology insights · 2025Article
- Methods to Estimate the Between-Population Level Effective Reproductive Number for Infectious Disease Epidemics: Foot-And-Mouth Disease (FMD) in Vietnam.Transboundary and emerging diseases · 2024Article
- Explanations for latitudinal diversity gradients must invoke rate variation.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Decoding the Fundamental Drivers of Phylodynamic Inference.Molecular biology and evolution · 2023Article
- State-dependent evolutionary models reveal modes of solid tumour growth.Nature ecology & evolution · 2023Article
- Alternate histories in macroevolution.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Rapidly changing speciation and extinction rates can be inferred in spite of nonidentifiability.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- A class of identifiable phylogenetic birth-death models.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Article
- Understanding drivers of phylogenetic clustering and terminal branch lengths distribution in epidemics ofeLife · 2022Article
- Statistical Challenges in Tracking the Evolution of SARS-CoV-2.Statistical science : a review journal of the Institute of Mathematical Statistics · 2022Article
- Pulled Diversification Rates, Lineages-Through-Time Plots, and Modern Macroevolutionary Modeling.Systematic biology · 2022Article
- A computationally tractable birth-death model that combines phylogenetic and epidemiological data.PLoS computational biology · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Viral phylogenies provide crucial information on the spread of infectious diseases, and many studies fit mathematical models to phylogenetic data to estimate epidemiological parameters such as the effective reproduction ratio (Re) over time. Such phylodynamic inferences often complement or even substitute for conventional surveillance data, particularly when sampling is poor or delayed. It remains generally unknown, however, how robust phylodynamic epidemiological inferences are, especially when there is uncertainty regarding pathogen prevalence and sampling intensity. Here, we use recently developed mathematical techniques to fully characterize the information that can possibly be extracted from serially collected viral phylogenetic data, in the context of the commonly used birth-death-sampling model. We show that for any candidate epidemiological scenario, there exists a myriad of alternative, markedly different, and yet plausible "congruent" scenarios that cannot be distinguished using phylogenetic data alone, no matter how large the data set. In the absence of strong constraints or rate priors across the entire study period, neither maximum-likelihood fitting nor Bayesian inference can reliably reconstruct the true epidemiological dynamics from phylogenetic data alone; rather, estimators can only converge to the "congruence class" of the true dynamics. We propose concrete and feasible strategies for making more robust epidemiological inferences from viral phylogenetic data.
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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.