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Abstract:
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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, a time-varying measure of genetic diversity. When the sampling process (collection of samples over time) depends on the effective population size, the coalescent and the sampling processes can be jointly modeled to improve estimation of this parameter. 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 the effective population size and a time-varying coefficient, making minimal assumptions on their functional shapes via Markov random field priors. We discuss a criterion to determine whether it is necessary to model preferential sampling for a given data set at hand. We show applications of our model to SARS-CoV-2 sequences available in GISAID.
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