Quantifying and predicting ongoing Human Immunodeficiency Virus Type 1 (HIV-1) transmission dynamics in Switzerland using a distance-based clustering approach.

Labarile, Marco; Loosli, Tom; Zeeb, Marius; Kusejko, Katharina; Huber, Michael; Hirsch, Hans H; Perreau, Matthieu; Ramette, Alban; Yerly, Sabine; Cavassini, Matthias; Battegay, Manuel; Rauch, Andri; Calmy, Alexandra; Notter, Julia; Bernasconi, Enos; Fux, Christoph; Günthard, Huldrych F; Pasin, Chloé; Kouyos, Roger D (2023). Quantifying and predicting ongoing Human Immunodeficiency Virus Type 1 (HIV-1) transmission dynamics in Switzerland using a distance-based clustering approach. The journal of infectious diseases, 227(4), pp. 554-564. Oxford University Press 10.1093/infdis/jiac457

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BACKGROUND

Despite effective prevention approaches, ongoing HIV-1 transmission remains a public health concern indicating a need for identifying its drivers.

METHODS

We combine a network-based clustering method using evolutionary distances between viral sequences with statistical learning approaches to investigate the dynamics of HIV-1 transmission in the Swiss HIV Cohort Study and to predict the drivers of ongoing transmission.

RESULTS

We find that only a minority of clusters and patients acquire links to new infections between 2007 and 2020. While the growth of clusters and the probability of individual patients acquiring new links in the transmission network was associated with epidemiological, behavioral and virological predictors, the strength of these associations decreased substantially when adjusting for network characteristics. Thus, these network characteristics can capture major heterogeneities beyond classical epidemiological parameters. When modeling the probability of a newly diagnosed patient being linked with future infections, we found that the best predictive performance (median AUCROC = 0.77) was achieved by models including characteristics of the network as predictors and that models excluding them performed substantially worse (median AUCROC = 0.54).

CONCLUSIONS

These results highlight the utility of molecular epidemiology-based network approaches for analysing and predicting ongoing HIV-1-transmission dynamics. This approach may serve for real-time prospective assessment of HIV-1-transmission.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Service Sector > Institute for Infectious Diseases > Research
04 Faculty of Medicine > Department of Haematology, Oncology, Infectious Diseases, Laboratory Medicine and Hospital Pharmacy (DOLS) > Clinic of Infectiology
04 Faculty of Medicine > Service Sector > Institute for Infectious Diseases

UniBE Contributor:

Ramette, Alban Nicolas, Rauch, Andri

Subjects:

600 Technology > 610 Medicine & health
500 Science > 570 Life sciences; biology

ISSN:

1537-6613

Publisher:

Oxford University Press

Language:

English

Submitter:

Pubmed Import

Date Deposited:

28 Nov 2022 09:36

Last Modified:

27 Nov 2023 00:25

Publisher DOI:

10.1093/infdis/jiac457

PubMed ID:

36433831

Uncontrolled Keywords:

HIV transmission dynamics cluster analysis distance-based clustering

BORIS DOI:

10.48350/175188

URI:

https://boris.unibe.ch/id/eprint/175188

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