Abstract
HIV molecular epidemiology estimates the transmission patterns from clustering genetically similar viruses. The process involves connecting genetically similar genotyped viral sequences in the network implying epidemiological transmissions. This technique relies on genotype data which is collected only from HIV diagnosed and in-care populations and leaves many persons with HIV (PWH) who have no access to consistent care out of the tracking process. We use machine learning algorithms to learn the non-linear correlation patterns between patient metadata and transmissions between HIV-positive cases. This enables us to expand the transmission network reconstruction beyond the molecular network. We employed multiple commonly used supervised classification algorithms to analyze the San Diego Primary Infection Resource Consortium (PIRC) cohort dataset, consisting of genotypes and nearly 80 additional non-genetic features. First, we trained classification models to determine genetically unrelated individuals from related ones. Our results show that random forest and decision tree achieved over 80% in accuracy, precision, recall, and F1-score by only using a subset of meta-features including age, birth sex, sexual orientation, race, transmission category, estimated date of infection, and first viral load date besides genetic data. Additionally, both algorithms achieved approximately 80% sensitivity and specificity. The Area Under Curve (AUC) is reported 97% and 94% for random forest and decision tree classifiers respectively. Next, we extended the models to identify clusters of similar viral sequences. Support vector machine demonstrated one order of magnitude improvement in accuracy of assigning the sequences to the correct cluster compared to dummy uniform random classifier. These results confirm that metadata carries important information about the dynamics of HIV transmission as embedded in transmission clusters. Hence, novel computational approaches are needed to apply the non-trivial knowledge collected from inter-individual genetic information to metadata from PWH in order to expand the estimated transmissions. We note that feature extraction alone will not be effective in identifying patterns of transmission and will result in random clustering of the data, but its utilization in conjunction with genetic data and the right algorithm can contribute to the expansion of the reconstructed network beyond individuals with genetic data.
Funder
National Institutes of Health
Gilead Sciences Grant
Center for AIDS Research, University of California, San Diego
Publisher
Public Library of Science (PLoS)
Subject
Computational Theory and Mathematics,Cellular and Molecular Neuroscience,Genetics,Molecular Biology,Ecology,Modelling and Simulation,Ecology, Evolution, Behavior and Systematics
Reference24 articles.
1. CDC, 4 June 2018, www.cdc.gov/hiv/programresources/guidance/molecular-cluster-identification/index.html
2. Growth of HIV-1 molecular transmission clusters in New York City;J.O Wertheim;The Journal of infectious diseases,2018
3. HIV-TRACE (TRAnsmission Cluster Engine): a tool for large scale molecular epidemiology of HIV-1 and other rapidly evolving pathogens;S. L Kosakovsky Pond;Molecular biology and evolution,2018
4. Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees;K Tamura;Molecular biology and evolution,1993
5. Transmission network parameters estimated from HIV sequences for a nationwide epidemic;A.J Leigh Brown;Journal of Infectious Diseases,2011
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