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Adaptive Preferential Sampling in Phylodynamics With an Application to SARS-CoV-2

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.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS; Vol.31 No.2 June 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 541-552

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

MARKOV RANDOM FIELDS
POPULATION SIZE
POISSON PROCESSES
COALESCENT PROCESS

Info Detail Spesifik

https://doi.org/10.1080/10618600.2021.1987256

Pernyataan Tanggungjawab

Lorenzo Cappello, Julia A. Palacios

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