scholarly journals Inferring pandemic growth rates from sequence data

2012 ◽  
Vol 9 (73) ◽  
pp. 1797-1808 ◽  
Author(s):  
Eric de Silva ◽  
Neil M. Ferguson ◽  
Christophe Fraser
Keyword(s):  
Population Size ◽  
Effective Population Size ◽  
Sequence Data ◽  
Simulated Data ◽  
Parametric Methods ◽  
Sampling Strategies ◽  
Size Estimation ◽  
Effective Population ◽  
Population Size Estimates ◽  
Non Parametric

Using sequence data to infer population dynamics is playing an increasing role in the analysis of outbreaks. The most common methods in use, based on coalescent inference, have been widely used but not extensively tested against simulated epidemics. Here, we use simulated data to test the ability of both parametric and non-parametric methods for inference of effective population size (coded in the popular BEAST package) to reconstruct epidemic dynamics. We consider a range of simulations centred on scenarios considered plausible for pandemic influenza, but our conclusions are generic for any exponentially growing epidemic. We highlight systematic biases in non-parametric effective population size estimation. The most prominent such bias leads to the false inference of slowing of epidemic spread in the recent past even when the real epidemic is growing exponentially. We suggest some sampling strategies that could reduce (but not eliminate) some of the biases. Parametric methods can correct for these biases if the infected population size is large. We also explore how some poor sampling strategies (e.g. that over-represent epidemiologically linked clusters of cases) could dramatically exacerbate bias in an uncontrolled manner. Finally, we present a simple diagnostic indicator, based on coalescent density and which can easily be applied to reconstructed phylogenies, that identifies time-periods for which effective population size estimates are less likely to be biased. We illustrate this with an application to the 2009 H1N1 pandemic.

scholarly journals Modeling the growth and decline of pathogen effective population size provides insight into epidemic dynamics and drivers of antimicrobial resistance

2017 ◽  
Author(s):  
Erik M. Volz ◽  
Xavier Didelot
Keyword(s):  
Growth Rate ◽  
Population Size ◽  
Effective Population Size ◽  
Growth Rates ◽  
Sequence Data ◽  
Demographic History ◽  
Parametric Models ◽  
Effective Population ◽  
Epidemic Dynamics ◽  
Non Parametric

AbstractNon-parametric population genetic modeling provides a simple and flexible approach for studying demographic history and epidemic dynamics using pathogen sequence data. Existing Bayesian approaches are premised on stationary stochastic processes which may provide an unrealistic prior for epidemic histories which feature extended period of exponential growth or decline. We show that non-parametric models defined in terms of the growth rate of the effective population size can provide a more realistic prior for epidemic history. We propose a non-parametric autoregressive model on the growth rate as a prior for effective population size, which corresponds to the dynamics expected under many epidemic situations. We demonstrate the use of this model within a Bayesian phylodynamic inference framework. Our method correctly reconstructs trends of epidemic growth and decline from pathogen genealogies even when genealogical data is sparse and conventional skyline estimators erroneously predict stable population size. We also propose a regression approach for relating growth rates of pathogen effective population size and time-varying variables that may impact the replicative fitness of a pathogen. The model is applied to real data from rabies virus and Staphylococcus aureus epidemics. We find a close correspondence between the estimated growth rates of a lineage of methicillin-resistant S. aureus and population-level prescription rates of β-lactam antibiotics. The new models are implemented in an open source R package called skygrowth which is available at https://mrc-ide.github.io/skygrowth/.

scholarly journals Maximum likelihood inference of pathogen population size history from a phylogeny

2021 ◽  
Author(s):  
Xavier Didelot ◽  
Erik M Volz
Keyword(s):  
Population Size ◽  
Effective Population Size ◽  
Sequence Data ◽  
Demographic History ◽  
Genetic Data ◽  
Parametric Models ◽  
Process Models ◽  
Effective Population ◽  
Latent Process ◽  
Non Parametric

ABSTRACTInference of effective population size from genomic data can provide unique information about demographic history, and when applied to pathogen genetic data can also provide insights into epidemiological dynamics. Non-parametric models for population dynamics combined with molecular clock models which relate genetic data to time have enabled phylodynamic inference based on large sets of time-stamped genetic sequence data. The theory for non-parametric inference of effective population size is well-developed in the Bayesian setting, but here we develop a frequentist approach based on non-parametric latent process models of population size dynamics. We appeal to statistical principles based on out-of-sample prediction accuracy in order to optimize parameters that control shape and smoothness of the population size over time. We demonstrate the flexibility and speed of this approach in a series of simulation experiments and apply the models to genetic data from several pathogen data sets.

scholarly journals Contrasting Trends of Population Size Change for Two Eurasian Owlet Species—Athene brama and Glaucidium radiatum From South Asia Over the Late Quaternary

2020 ◽  
Vol 8 ◽  
Author(s):  
Meghana Natesh ◽  
K. L. Vinay ◽  
Samriddha Ghosh ◽  
Rajah Jayapal ◽  
Shomita Mukherjee ◽  
...  
Keyword(s):  
Population Size ◽  
Effective Population Size ◽  
Late Quaternary ◽  
Habitat Associations ◽  
Interglacial Period ◽  
Effective Population ◽  
Size Change ◽  
Last Glacial ◽  
Population Size Estimates ◽  
The Last Glacial

Climatic oscillations over the Quaternary have had a lasting impact on species’ distribution, evolutionary history, and genetic composition. Many species show dramatic population size changes coinciding with the last glacial period. However, the extent and direction of change vary across biogeographic regions, species-habitat associations, and species traits. Here we use genomic data to assess population size changes over the late Quaternary using the Pairwise Sequential Markovian Coalescent (PSMC) approach in two Eurasian Owlet species—the Spotted Owlet, Athene brama, and the Jungle Owlet, Glaucidium radiatum. While Spotted Owlets are typically associated with open habitats, Jungle Owlets are found in deciduous forests and scrublands. We find that the effective population size for the Spotted Owlet increased after the Interglacial period till the Last Glacial Maxima and subsequently declined toward the Mid-Holocene. On the other hand, effective population size estimates for the Jungle Owlet increased gradually throughout this period. These observations are in line with climatic niche model-based predictions for range size change for both species from a previous study and suggest that habitat associations at the local scale are important in determining responses to past climatic and vegetational changes. The Spotted Owlet result also aligns well with the expectation of open habitat expansion during the arid Glacial Maxima, whereas for the Jungle Owlet the contrasting expectation does not hold. Therefore, assessing the impacts of glacial history on population trajectories of multiple species with different habitat associations is necessary to understand the impacts of past climate on South Asian taxa.

scholarly journals Joint Estimation of Pedigrees and Effective Population Size Using Markov Chain Monte Carlo

2018 ◽  
Author(s):  
Amy Ko ◽  
Rasmus Nielsen
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Population Size ◽  
Effective Population Size ◽  
Simulated Data ◽  
Composite Likelihood ◽  
Joint Estimation ◽  
Pedigree Information ◽  
Effective Population

Pedigrees provide a fine resolution of the genealogical relationships among individuals and serve an important function in many areas of genetic studies. One such use of pedigree information is in the estimation of short-term effective population size (Ne), which is of great relevance in fields such as conservation genetics. Despite the usefulness of pedigrees, however, they are often an unknown parameter and must be inferred from genetic data. In this study, we present a Bayesian method to jointly estimate pedigrees and Ne from genetic markers using Markov Chain Monte Carlo. Our method supports analysis of a large number of markers and individuals with the use of composite likelihood, which significantly increases computational efficiency. We show on simulated data that our method is able to jointly estimate relationships up to first cousins and Ne with high accuracy. We also apply the method on a real dataset of house sparrows to reconstruct their previously unreported pedigree.

scholarly journals Sampling strategies for estimating brook trout effective population size

2012 ◽  
Vol 13 (3) ◽  
pp. 625-637 ◽  
Author(s):  
Andrew R. Whiteley ◽  
Jason A. Coombs ◽  
Mark Hudy ◽  
Zachary Robinson ◽  
Keith H. Nislow ◽  
...  
Keyword(s):  
Population Size ◽  
Effective Population Size ◽  
Brook Trout ◽  
Sampling Strategies ◽  
Effective Population

Estimating Ancestral Population Sizes and Divergence Times

Genetics ◽  
2003 ◽  
Vol 163 (1) ◽  
pp. 395-404 ◽  
Author(s):  
Jeffrey D Wall
Keyword(s):  
Population Size ◽  
Effective Population Size ◽  
Sequence Data ◽  
Ancestral Population ◽  
Divergence Times ◽  
Intragenic Recombination ◽  
Intergenic Sequence ◽  
Effective Population ◽  
Recombination Rates ◽  
Population Sizes

Abstract This article presents a new method for jointly estimating species divergence times and ancestral population sizes. The method improves on previous ones by explicitly incorporating intragenic recombination, by utilizing orthologous sequence data from closely related species, and by using a maximum-likelihood framework. The latter allows for efficient use of the available information and provides a way of assessing how much confidence we should place in the estimates. I apply the method to recently collected intergenic sequence data from humans and the great apes. The results suggest that the human-chimpanzee ancestral population size was four to seven times larger than the current human effective population size and that the current human effective population size is slightly >10,000. These estimates are similar to previous ones, and they appear relatively insensitive to assumptions about the recombination rates or mutation rates across loci.

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