A model-based clustering method to detect infectious disease transmission outbreaks from sequence variation

Rosemary M McCloskey; Art F Y Poon

doi:10.1371/journal.pcbi.1005868

A model-based clustering method to detect infectious disease transmission outbreaks from sequence variation

PLoS Comput Biol. 2017 Nov 13;13(11):e1005868. doi: 10.1371/journal.pcbi.1005868. eCollection 2017 Nov.

Authors

Rosemary M McCloskey¹, Art F Y Poon^{2

3

4}

Affiliations

¹ BC Centre for Excellence in HIV/AIDS, Vancouver, British Columbia, Canada.
² Department of Pathology and Laboratory Medicine, Western University, London, Ontario, Canada.
³ Department of Microbiology and Immunology, Western University, London, Ontario, Canada.
⁴ Department of Applied Mathematics, Western University, London, Ontario, Canada.

Abstract

Clustering infections by genetic similarity is a popular technique for identifying potential outbreaks of infectious disease, in part because sequences are now routinely collected for clinical management of many infections. A diverse number of nonparametric clustering methods have been developed for this purpose. These methods are generally intuitive, rapid to compute, and readily scale with large data sets. However, we have found that nonparametric clustering methods can be biased towards identifying clusters of diagnosis-where individuals are sampled sooner post-infection-rather than the clusters of rapid transmission that are meant to be potential foci for public health efforts. We develop a fundamentally new approach to genetic clustering based on fitting a Markov-modulated Poisson process (MMPP), which represents the evolution of transmission rates along the tree relating different infections. We evaluated this model-based method alongside five nonparametric clustering methods using both simulated and actual HIV sequence data sets. For simulated clusters of rapid transmission, the MMPP clustering method obtained higher mean sensitivity (85%) and specificity (91%) than the nonparametric methods. When we applied these clustering methods to published sequences from a study of HIV-1 genetic clusters in Seattle, USA, we found that the MMPP method categorized about half (46%) as many individuals to clusters compared to the other methods. Furthermore, the mean internal branch lengths that approximate transmission rates were significantly shorter in clusters extracted using MMPP, but not by other methods. We determined that the computing time for the MMPP method scaled linearly with the size of trees, requiring about 30 seconds for a tree of 1,000 tips and about 20 minutes for 50,000 tips on a single computer. This new approach to genetic clustering has significant implications for the application of pathogen sequence analysis to public health, where it is critical to robustly and accurately identify clusters for the most cost-effective deployment of outbreak management and prevention resources.

MeSH terms

Cluster Analysis
Communicable Diseases / classification
Communicable Diseases / genetics*
Communicable Diseases / transmission*
Computational Biology
Computer Simulation
Disease Outbreaks / prevention & control*
Humans
Markov Chains
Models, Biological*

Grants and funding

This work was supported in part by the Government of Canada through Genome Canada and the Ontario Genomics Institute (OGI-131) and by grants from the Canadian Institutes for Health Research (CIHR PJT-153391 and BOP-149562) awarded to AFYP. AFYP was supported by a CIHR New Investigator Award (FRN-130609). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.