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Incorporation of information diffusion model for enhancing analyses in HIV molecular surveillance
- Source :
- Emerging Microbes and Infections, Vol 9, Iss 1, Pp 256-262 (2020)
- Publication Year :
- 2020
- Publisher :
- Taylor & Francis Group, 2020.
-
Abstract
- ABSTRACTMolecular surveillance of infections is essential in monitoring their transmission in the population. In this study, newly diagnosed HIV patients' phylogenetic, clinical and behavioural data were integrated, and an information diffusion model was incorporated in analysing transmission dynamics. A genetic network was constructed from HIV sequences, from which transmission cascades were extracted. From the transmission cascades, CRF01_AE had higher values of information diffusion metrics, including scale, speed and range, than that of B, signifying the distinct transmission patterns of two circulating subtypes in Hong Kong. Patients connected in the network, were more likely male, younger, of main circulating subtypes, to have acquired HIV infection locally, and a higher CD4 level at diagnosis. Genetic connections varied among men who have sex with men (MSM) who used different channels of sex networking and varied in their engagement in risk behaviours. MSM using recreational drugs for sex held positions of greater importance within the network. Significant differences in network metrics were observed among MSM as differentiated by their mobile apps usage patterns, evidencing the impact of social network on transmission networks. The applied model in the presence of consistently collected longitudinal data could enhance HIV molecular epidemiologic surveillance for informing future intervention planning.
Details
- Language :
- English
- ISSN :
- 22221751
- Volume :
- 9
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- Emerging Microbes and Infections
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.3af42f29134df2ba4fe8ecdcefc50c
- Document Type :
- article
- Full Text :
- https://doi.org/10.1080/22221751.2020.1718554