Quantifying and Predicting Ongoing Human Immunodeficiency Virus Type 1 Transmission Dynamics in Switzerland Using a Distance-Based Clustering Approach

Background Despite effective prevention approaches, ongoing human immunodeficiency virus 1 (HIV-1) transmission remains a public health concern indicating a need for identifying its drivers. Methods We combined a network-based clustering method using evolutionary distances between viral sequences with statistical learning approaches to investigate the dynamics of HIV transmission in the Swiss HIV Cohort Study and to predict the drivers of ongoing transmission. Results We found that only a minority of clusters and patients acquired 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 area under the curve receiver operating characteristic AUC(ROC) = 0.77) was achieved by models including characteristics of the network as predictors and that models excluding them performed substantially worse (median AUC(ROC) = 0.54). Conclusions These results highlight the utility of molecular epidemiology-based network approaches for analyzing and predicting ongoing HIV transmission dynamics. This approach may serve for real-time prospective assessment of HIV transmission. Combining distance-based clustering with statistical learning approaches, we characterize and predict the long-term growth of HIV-1-transmission clusters in Switzerland and investigate its drivers. We show the potential of cluster structure for the real-time prediction of ongoing transmission.

Automated summary

This study investigated and predicted the dynamics of HIV transmission in Switzerland using a network-based clustering method and statistical learning approaches. The study found that network characteristics can capture major heterogeneities in transmission and that cluster structure has the potential for real-time prediction of ongoing transmission.