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Model misspecification misleads inference of the spatial dynamics of disease outbreaks

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...

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Autores principales: Gao, Jiansi, May, Michael R., Rannala, Bruce, Moore, Brian R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10089176/
https://www.ncbi.nlm.nih.gov/pubmed/36897983
http://dx.doi.org/10.1073/pnas.2213913120
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author Gao, Jiansi
May, Michael R.
Rannala, Bruce
Moore, Brian R.
author_facet Gao, Jiansi
May, Michael R.
Rannala, Bruce
Moore, Brian R.
author_sort Gao, Jiansi
collection PubMed
description 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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spelling pubmed-100891762023-04-12 Model misspecification misleads inference of the spatial dynamics of disease outbreaks Gao, Jiansi May, Michael R. Rannala, Bruce Moore, Brian R. Proc Natl Acad Sci U S A Biological Sciences 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. National Academy of Sciences 2023-03-10 2023-03-14 /pmc/articles/PMC10089176/ /pubmed/36897983 http://dx.doi.org/10.1073/pnas.2213913120 Text en Copyright © 2023 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by/4.0/This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY) (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Biological Sciences
Gao, Jiansi
May, Michael R.
Rannala, Bruce
Moore, Brian R.
Model misspecification misleads inference of the spatial dynamics of disease outbreaks
title Model misspecification misleads inference of the spatial dynamics of disease outbreaks
title_full Model misspecification misleads inference of the spatial dynamics of disease outbreaks
title_fullStr Model misspecification misleads inference of the spatial dynamics of disease outbreaks
title_full_unstemmed Model misspecification misleads inference of the spatial dynamics of disease outbreaks
title_short Model misspecification misleads inference of the spatial dynamics of disease outbreaks
title_sort model misspecification misleads inference of the spatial dynamics of disease outbreaks
topic Biological Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10089176/
https://www.ncbi.nlm.nih.gov/pubmed/36897983
http://dx.doi.org/10.1073/pnas.2213913120
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