• Corpus ID: 221507535

Adaptive preferential sampling in phylodynamics.

  title={Adaptive preferential sampling in phylodynamics.},
  author={Lorenzo Cappello and Julia A. Palacios},
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 $N_{e}(t)$, a time-varying measure of genetic diversity. When the sampling process (collection… 

Statistical Challenges in Tracking the Evolution of SARS-CoV-2.

The models and methods currently used to monitor the spread of SARS-CoV-2 are described, long-standing and new statistical challenges are discussed, and a method for tracking the rise of novel variants during the epidemic is proposed.



Jointly Inferring the Dynamics of Population Size and Sampling Intensity from Molecular Sequences

The BESP improves upon previous coalescent estimators and generates new, biologically-useful insights into the sampling protocols underpinning these datasets, and is generalised to incorporate phylogenetic uncertainty in a new Bayesian package (BESP) in BEAST2.

Viral phylodynamics and the search for an ‘effective number of infections’

  • S. FrostE. Volz
  • Biology
    Philosophical Transactions of the Royal Society B: Biological Sciences
  • 2010
Using commonly used epidemiological models, it is shown that the coalescence rate may indeed reflect the number of infected individuals during the initial phase of exponential growth when time is scaled by infectivity, but in general, a single change in time scale cannot be used to estimate the numberof infected individuals.

Quantifying and Mitigating the Effect of Preferential Sampling on Phylodynamic Inference

A new model is proposed that explicitly accounts for preferential sampling by modeling the sampling times as an inhomogeneous Poisson process dependent on effective population size.

Sampling through time and phylodynamic inference with coalescent and birth–death models

It is found that the birth–death model estimators are subject to large bias if the sampling process is misspecified, which motivates the development of a new coalescent estimator, which is augmented with a model of the known sampling process and is potentially more precise than the coalescent that does not use sample time information.

phylodyn: an R package for phylodynamic simulation and inference

The main functionality of phylodyn is Bayesian nonparametric estimation of effective population size fluctuations over time, which assumes the coalescent and the sequentially Markov coalescent processes as generative models of genealogies.

The Tajima heterochronous n-coalescent: inference from heterochronously sampled molecular data

The method is used to re-examine the scientific question of how Beringian bison went extinct analyzing modern and ancient molecular sequences of bison in North America, and to reconstruct population size trajectory of SARS-CoV-2 from viral sequences collected in France and Germany.

Estimating effective population size changes from preferentially sampled genetic sequences

This work extends the method to allow for joint Bayesian estimation of the genealogy, effective population size trajectory, and other model parameters, and improves the sampling time model by incorporating additional sources of information in the form of time-varying covariates.

Phylodynamics of Infectious Disease Epidemics

A formalism for unifying the inference of population size from genetic sequences and mathematical models of infectious disease in populations, which may be a viable alternative to demographic models used to reconstruct epidemic dynamics.

Integrated Nested Laplace Approximation for Bayesian Nonparametric Phylodynamics

This paper adapts an integrated nested Laplace approximation (INLA), a recently proposed approximate Bayesian inference for latent Gaussian models, to the estimation of population size trajectories and shows that when a genealogy of sampled individuals can be reliably estimated from genetic data, INLA enjoys high accuracy and can replace MCMC entirely.

Bayesian inference of population size history from multiple loci

The Extended Bayesian Skyline Plot is presented, a non-parametric Bayesian Markov chain Monte Carlo algorithm that extends a previous coalescent-based method in several ways, including the ability to analyze multiple loci, demonstrating the essential role of multiple loco in recovering population size dynamics.