ArticleProceedings of the National Academy of Sciences of the United States of America2023
Model misspecification misleads inference of the spatial dynamics of disease outbreaks.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- SAASI: Sampling Aware Ancestral State Inference.Nature communications · 2026Article
- Emergence of distinct Streptococcus pyogenes emm1 and emm12 lineages in China.Nature microbiology · 2026Article
- Genomic epidemiology ofMicrobial genomics · 2026Article
- Biological causes and impacts of rugged tree landscapes in phylodynamic inference.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Inferring epidemiological parameters under an infectious phylogeography model with visitor dynamics.medRxiv : the preprint server for health sciences · 2025Article
- Aging dynamics in captive sea turtles reflect conserved life-history patterns across the testudine phylogeny.Communications biology · 2025Article
- On Rooting and Dating Viral Trees With a Changing Evolutionary Rate Following Host-Switching.Genome biology and evolution · 2025Article
- Biological causes and impacts of rugged tree landscapes in phylodynamic inference.bioRxiv : the preprint server for biology · 2025Article
- Bayesian Inference of Pathogen Phylogeography using the Structured Coalescent Model.PLoS computational biology · 2025Article
- Unveiling the Dynamics of SARS-CoV-2 Gamma and Delta Waves in Paraná, Brazil - Delta Displacing a Persistent Gamma Through Alternative Routes of Dispersal.Journal of medical virology · 2025Article
- Dispersal dynamics and introduction patterns of SARS-CoV-2 lineages in Iran.Virus evolution · 2025Article
- From infectious diseases to chronic diseases: the paradigm shift of spatial epidemiology in disease prevention and control.Frontiers in public health · 2025Review
- Assessing the effect of model specification and prior sensitivity on Bayesian tests of temporal signal.PLoS computational biology · 2024Article
- Genetic heterogeneity in theMicrobial genomics · 2024Article
- Deep Learning and Likelihood Approaches for Viral Phylogeography Converge on the Same Answers Whether the Inference Model Is Right or Wrong.Systematic biology · 2024Article
- A phylogenetic assessment of HIV-1 transmission trends among people who inject drugs from Coastal and Nairobi, Kenya.Virus evolution · 2024Article
- PrioriTree: a utility for improving phylodynamic analyses in BEAST.Bioinformatics (Oxford, England) · 2023Article
- Mechanistic modeling of mitosis: Insights from three collaborative case studies.Seminars in cell & developmental biologyReview
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4 authors.
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Abstract
Epidemiology has been transformed by the advent of Bayesian phylodynamic models that allow researchers to infer the geographic history of pathogen dispersal over a set of discrete geographic areas [1, 2]. These models provide powerful tools for understanding the spatial dynamics of disease outbreaks, but contain many parameters that are inferred from minimal geographic information (i.e., the single area in which each pathogen was sampled). Consequently, inferences under these models are inherently sensitive to our prior assumptions about the model parameters. Here, we demonstrate that the default priors used in empirical phylodynamic studies make strong and biologically unrealistic assumptions about the underlying geographic process. We provide empirical evidence that these unrealistic priors strongly (and adversely) impact commonly reported aspects of epidemiological studies, including: 1) the relative rates of dispersal between areas; 2) the importance of dispersal routes for the spread of pathogens among areas; 3) the number of dispersal events between areas, and; 4) the ancestral area in which a given outbreak originated. We offer strategies to avoid these problems, and develop tools to help researchers specify more biologically reasonable prior models that will realize the full potential of phylodynamic methods to elucidate pathogen biology and, ultimately, inform surveillance and monitoring policies to mitigate the impacts of disease outbreaks.
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