Adaptive preferential sampling in phylodynamics with an application to SARS-CoV-2

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• dc.contributor.author Cappello, Lorenzo
• dc.contributor.author Palacios, Julia A.
• dc.date.accessioned 2024-02-28T08:06:16Z
• dc.date.available 2024-02-28T08:06:16Z
• dc.date.issued 2022
• dc.description.abstract Longitudinal molecular data of rapidly evolving viruses and pathogens provide information about disease spread and complement traditional surveillance approaches based on case count data. The coalescent is used to model the genealogy that represents the sample ancestral relationships. The basic assumption is that coalescent events occur at a rate inversely proportional to the effective population size Ne(t) , a time-varying measure of genetic diversity. When the sampling process (collection of samples over time) depends on Ne(t) , the coalescent and the sampling processes can be jointly modeled to improve estimation of Ne(t) . Failing to do so can lead to bias due to model misspecification. However, the way that the sampling process depends on the effective population size may vary over time. We introduce an approach where the sampling process is modeled as an inhomogeneous Poisson process with rate equal to the product of Ne(t) and a time-varying coefficient, making minimal assumptions on their functional shapes via Markov random field priors. We provide efficient algorithms for inference, show the model performance vis-a-vis alternative methods in a simulation study, and apply our model to SARS-CoV-2 sequences from Los Angeles and Santa Clara counties. The methodology is implemented and available in the R package adapref. Supplementary files for this article are available online.
• dc.format.mimetype application/pdf
• dc.identifier.citation Cappello L, Palacios JA. Adaptive preferential sampling in phylodynamics with an application to SARS-CoV-2. J Comput Graph Stat. 2022;31(2):541-52. DOI: 10.1080/10618600.2021.1987256
• dc.identifier.doi http://dx.doi.org/10.1080/10618600.2021.1987256
• dc.identifier.issn 1061-8600
• dc.identifier.uri http://hdl.handle.net/10230/59280
• dc.language.iso eng
• dc.publisher Taylor & Francis
• dc.relation.ispartof Journal of Computational and Graphical Statistics. 2022;31(2):541-52.
• dc.rights © 2021 The Author(s). Published with license by Taylor & Francis Group, LLC.This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), whichpermits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
• dc.subject.keyword Coalescent process
• dc.subject.keyword Markov random fields
• dc.subject.keyword Poisson processes
• dc.subject.keyword Population size
• dc.title Adaptive preferential sampling in phylodynamics with an application to SARS-CoV-2
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion