Abstract
Heterogeneity in individual-level transmissibility can be quantified by the dispersion parameter of the offspring distribution. Quantifying heterogeneity is important as it affects other parameter estimates, it modulates the degree of unpredictability of an epidemic, and it needs to be accounted for in models of infection control. Aggregated data such as incidence time series are often not sufficiently informative to estimate . Incorporating phylogenetic analysis can help to estimate concurrently with other epidemiological parameters. We have developed an inference framework that uses particle Markov Chain Monte Carlo to estimate and other epidemiological parameters using both incidence time series and the pathogen phylogeny. Using the framework to fit a modified compartmental transmission model that includes the parameter to simulated data, we found that more accurate and less biased estimates of the reproductive number were obtained by combining epidemiological and phylogenetic analyses. However, was most accurately estimated using pathogen phylogeny alone. Accurately estimating was necessary for unbiased estimates of the reproductive number, but it did not affect the accuracy of reporting probability and epidemic start date estimates. We further demonstrated that inference was possible in the presence of phylogenetic uncertainty by sampling from the posterior distribution of phylogenies. Finally, we used the inference framework to estimate transmission parameters from epidemiological and genetic data collected during a poliovirus outbreak. Despite the large degree of phylogenetic uncertainty, we demonstrated that incorporating phylogenetic data in parameter inference improved the accuracy and precision of estimates.
Highlights
The intensity of epidemics is often summarized by the reproductive number R, the average number of secondary infections caused by a typical infectious individual over the course of their infectious period
Phylogeny Is More Informative than Incidence Time Series for Estimating k Based on data simulated from an SIR model, pathogen phylogeny was needed to accurately estimate the dispersion parameter k of the offspring distribution when k was small
Building on methods that enable parameter inference for stochastic models and phylodynamic approaches integrating both epidemiological and phylogenetic data, we presented a framework for quantifying the offspring distribution dispersion k while inferring key epidemiological parameters from both types of data
Summary
The intensity of epidemics is often summarized by the reproductive number R, the average number of secondary infections caused by a typical infectious individual over the course of their infectious period. This statistic is useful for determining whether an epidemic can take off and if so the final size of the epidemic. The presence of superspreading as indicated by small values of k can affect the effectiveness of control strategies (Garske and Rhodes 2008). Inferring the value of k from data is not straightforward, even in the presence of contact tracing data as many infections may be asymptomatic or not reported. Obtaining precise estimates of k from just incidence time series is usually not possible because k only affects the noisiness of the incidence time series at low numbers
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