Abstract
Quantifying epidemiological dynamics is crucial for understanding and forecasting the spread of an epidemic. The coalescent and the birth-death model are used interchangeably to infer epidemiological parameters from the genealogical relationships of the pathogen population under study, which in turn are inferred from the pathogen genetic sequencing data. To compare the performance of these widely applied models, we performed a simulation study. We simulated phylogenetic trees under the constant rate birth-death model and the coalescent model with a deterministic exponentially growing infected population. For each tree, we re-estimated the epidemiological parameters using both a birth-death and a coalescent based method, implemented as an MCMC procedure in BEAST v2.0. In our analyses that estimate the growth rate of an epidemic based on simulated birth-death trees, the point estimates such as the maximum a posteriori/maximum likelihood estimates are not very different. However, the estimates of uncertainty are very different. The birth-death model had a higher coverage than the coalescent model, i.e. contained the true value in the highest posterior density (HPD) interval more often (2–13% vs. 31–75% error). The coverage of the coalescent decreases with decreasing basic reproductive ratio and increasing sampling probability of infecteds. We hypothesize that the biases in the coalescent are due to the assumption of deterministic rather than stochastic population size changes. Both methods performed reasonably well when analyzing trees simulated under the coalescent. The methods can also identify other key epidemiological parameters as long as one of the parameters is fixed to its true value. In summary, when using genetic data to estimate epidemic dynamics, our results suggest that the birth-death method will be less sensitive to population fluctuations of early outbreaks than the coalescent method that assumes a deterministic exponentially growing infected population.
Highlights
In many applications determining the past dynamics of populations is of interest
We find that the constant rate birth-death process can account for early stochasticity and is capable of recovering the epidemic growth rates more successfully
We conclude that a birth-death-based method is generally a more reliable method than a deterministic coalescent-based method for epidemiological parameter inference from phylogenies representing epidemic outbreaks
Summary
In many applications determining the past dynamics of populations is of interest. In an epidemiological context in particular, the interest lies in knowing two quantities: the basic reproductive ratio R0 and the growth rate r of the epidemic. R0 is a key parameter that determines the probability and the extent of spread of the disease in the population. It is defined as the number of secondary infections a single pathogen is expected to cause when introduced into a population of susceptible individuals [1,2]. The growth rate r determines the speed of spread of the pathogen. Accurate estimation of these two parameters (R0 and r) is required in order to take appropriate measures of intervention, e.g. vaccinations or isolation of infected individuals. Estimation of these parameters was exclusively based on prevalence and incidence epidemiological data. Recent progress in phylodynamics has enabled the inference of these parameters from pathogen sequence data by integrating methods of phylogenetics with those of mathematical epidemiology (for review see [3])
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