A Systematic Bayesian Integration of Epidemiological and Genetic Data

Abstract

Genetic sequence data on pathogens have great potential to inform inference of their transmission dynamics ultimately leading to better disease control. Where genetic change and disease transmission occur on comparable timescales additional information can be inferred via the joint analysis of such genetic sequence data and epidemiological observations based on clinical symptoms and diagnostic tests. Although recently introduced approaches represent substantial progress, for computational reasons they approximate genuine joint inference of disease dynamics and genetic change in the pathogen population, capturing partially the joint epidemiological-evolutionary dynamics. Improved methods are needed to fully integrate such genetic data with epidemiological observations, for achieving a more robust inference of the transmission tree and other key epidemiological parameters such as latent periods. Here, building on current literature, a novel Bayesian framework is proposed that infers simultaneously and explicitly the transmission tree and unobserved transmitted pathogen sequences. Our framework facilitates the use of realistic likelihood functions and enables systematic and genuine joint inference of the epidemiological-evolutionary process from partially observed outbreaks. Using simulated data it is shown that this approach is able to infer accurately joint epidemiological-evolutionary dynamics, even when pathogen sequences and epidemiological data are incomplete, and when sequences are available for only a fraction of exposures. These results also characterise and quantify the value of incomplete and partial sequence data, which has important implications for sampling design, and demonstrate the abilities of the introduced method to identify multiple clusters within an outbreak. The framework is used to analyse an outbreak of foot-and-mouth disease in the UK, enhancing current understanding of its transmission dynamics and evolutionary process.

Spatial and temporal dynamics of superspreading events in the 2014–2015 West Africa Ebola epidemic

“…Future theoretical work will need to include such contacts. Nevertheless, because unsuccessful contacts are not parts of the transmission chain, ignoring them has limited effect on the transmission tree or on many overall topological characteristics (e.g., average number of offspring of an infected case) (25,28,29). Finally, although our analysis reveals the importance of age as demographic determinants of superspreading, future work in linking them with virological factors (e.g., age-specific viral loads) may shed further light (16).…”

Some Likelihood Based Properties in Large Samples: Utility and Risk Aversion, Second Order Prior Selection and Posterior Density Stability

“…This has been especially true in the basic sciences which, while traditionally not very open to the more subjective Bayesian perspective, have been open to its broader and more flexible modeling approach [13]. Bayesian analysis does however require an understanding of the analyst's set of prior beliefs regarding the set of population characteristics or parameters of interest and provides a process by which they will be updated by the observed model-data combination.…”

outbreaker2: a modular platform for outbreak reconstruction

“…A large number of studies have addressed this problem in recent years (Table 1) [6][7][8][9][10][11][12][13][14][15]. These approaches differ in multiple ways, including in their underlying epidemiological models (e.g.…”

“…SIR [8,11], SEIR [9,12] or branching process models [7,14,15]) and genetic models (e.g. non-phylogenetic [7,10,12,16] or phylogenetic models [11,[13][14][15]), as well as their ability to account for unobserved cases and multiple infectious introductions. This methodological diversity is beneficial, providing various theoretical frameworks for outbreak reconstruction in different epidemic scenarios.…”

“…Most consider the same data, namely WGS and some form of temporal data (i.e. dates of symptom onset and assumptions on the distribution of incubation and infectious periods [7,9,12,14,15], or explicitly defined exposure intervals [10,13]). The majority are also implemented in a Bayesian framework, and therefore describe prior distributions on parameters and likelihood functions that evaluate the plausibility of a given parameter sets under specific transmission and evolutionary models.…”