 Bayesian inference of transmission chains using timing of symptoms, pathogen genomes and contact dataFinlay Campbell, Anne Cori, Neil Ferguson and Thibaut Jombart PLOS Computational Biology, 2019, vol. 15, issue 3, 1-20 Abstract: There exists significant interest in developing statistical and computational tools for inferring ‘who infected whom’ in an infectious disease outbreak from densely sampled case data, with most recent studies focusing on the analysis of whole genome sequence data. However, genomic data can be poorly informative of transmission events if mutations accumulate too slowly to resolve individual transmission pairs or if there exist multiple pathogens lineages within-host, and there has been little focus on incorporating other types of outbreak data. We present here a methodology that uses contact data for the inference of transmission trees in a statistically rigorous manner, alongside genomic data and temporal data. Contact data is frequently collected in outbreaks of pathogens spread by close contact, including Ebola virus (EBOV), severe acute respiratory syndrome coronavirus (SARS-CoV) and Mycobacterium tuberculosis (TB), and routinely used to reconstruct transmission chains. As an improvement over previous, ad-hoc approaches, we developed a probabilistic model that relates a set of contact data to an underlying transmission tree and integrated this in the outbreaker2 inference framework. By analyzing simulated outbreaks under various contact tracing scenarios, we demonstrate that contact data significantly improves our ability to reconstruct transmission trees, even under realistic limitations on the coverage of the contact tracing effort and the amount of non-infectious mixing between cases. Indeed, contact data is equally or more informative than fully sampled whole genome sequence data in certain scenarios. We then use our method to analyze the early stages of the 2003 SARS outbreak in Singapore and describe the range of transmission scenarios consistent with contact data and genetic sequence in a probabilistic manner for the first time. This simple yet flexible model can easily be incorporated into existing tools for outbreak reconstruction and should permit a better integration of genomic and epidemiological data for inferring transmission chains.Author summary: Reconstructing the history of transmission events in an infectious disease outbreak provides valuable information for informing infection control policy. Recent years have seen considerable progress in the development of statistical tools for the inference of such transmission trees from outbreak data, with a major focus on whole genome sequence data (WGS). However, complex evolutionary behavior, missing sequences and the limited diversity accumulating along transmission chains limit the power of existing approaches in reconstructing outbreaks. We have developed a methodology that uses information on the contact structures between cases to infer likely transmission links, alongside genomic and temporal data. Such contact data is frequently collected in outbreak settings, for example during Ebola, HIV or Tuberculosis outbreaks, and can be highly informative of the infectious relationships between cases. Using simulations, we show that our contact model effectively incorporates this information and improves the accuracy of outbreak reconstruction even when only a portion of contacts are reported. We then apply our method to the 2003 SARS outbreak in Singapore and describe the range of transmission scenarios consistent with genetic data and contact data for the first time. Our work suggests that, whenever available, contact data should be explicitly incorporated in outbreak reconstruction tools. Date: 2019 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1006930 DOI: 10.1371/journal.pcbi.1006930 Access Statistics for this article More articles in PLOS Computational Biology from Public Library of Science |
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