Abstract
Whole‐genome sequencing of pathogens in outbreaks of infectious disease provides the potential to reconstruct transmission pathways and enhance the information contained in conventional epidemiological data. In recent years, there have been numerous new methods and models developed to exploit such high‐resolution genetic data. However, corresponding methods for model assessment have been largely overlooked. In this article, we develop both new modelling methods and new model assessment methods, specifically by building on the work of Worby et al. Although the methods are generic in nature, we focus specifically on nosocomial pathogens and analyze a dataset collected during an outbreak of MRSA in a hospital setting.
Highlights
Recent years have seen intense research activity directed towards methods for analyzing data on outbreaks of communicable diseases where the data contain high-resolution genetic information, such as whole-genome sequences
Particular attention has been given to methods for reconstructing transmission trees.1-9. Speaking, such methods fall into two categories, namely those which require an initial reconstruction of a phylogenetic tree, which itself may be topologically dissimilar to the transmission tree itself,10 and those which do not
There is reasonable agreement across all models, for the transmission parameter β and test sensitivity z, the latter being around 70% for ward 1 and 80% for ward 2
Summary
Recent years have seen intense research activity directed towards methods for analyzing data on outbreaks of communicable diseases where the data contain high-resolution genetic information, such as whole-genome sequences. Speaking, such methods fall into two categories, namely those which require an initial reconstruction of a phylogenetic tree, which itself may be topologically dissimilar to the transmission tree itself, and those which do not Among the latter are those in which statistical inference is carried out by defining a probability model conditional on the observed data, meaning that there is no underlying model that fully describes how the data were generated. A probability model for possible transmission trees can be defined conditional upon observed symptom appearance times, but with no explicit model for the times themselves.6,11 Both Lau et al and Worby et al provide such data-generating models that incorporate both the transmission dynamics of the epidemic and the genetic component. One advantage of the latter approach is that it avoids detailed assumptions about microevolution processes, which are often not well-understood
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