Exploring and Predicting the Drivers of Ongoing HIV-1 Transmission in Guangyuan, Sichuan

Abstract

Purpose

Guangyuan was selected as the first pilot city of molecular transmission network in Sichuan Province to implement dynamic monitoring. This study aim to insight the characteristics of HIV-1 molecular epidemiology and explore the influencing factors of transmission dynamics. Furthermore, it predict the driving factors of network expansion by established a transmission risk prediction model.

Patients and Methods

A longitudinal cohort study was conducted to obtain a total of 1434 plasma samples from newly diagnosed HIV-infected patients from 2010 to June 2022. Phylogenetic relationship and cluster analysis were performed using HIV-1 polymerase (pol) gene sequences to study the risk factors of clustering. We applied Logistic ML algorithms to establish a transmission risk prediction model, and model performance was checked using 10-fold cross-validation in the training set and receiver operating characteristic (ROC) curve analysis.

Results

A total of 1360 pol sequences linked demographics obtained in this study cover approximately 94.8% of newly notified infections from 2010 to June 2022. The major epidemic genotypes were CRF07_BC, CRF01_AE, CRF08_BC and B subtypes, accounting for 93.82% of all. The differences of some clinical and demographic factors (eg, age, marital status) were statistically significant (P<0.05). We identified 136 clusters containing 654 HIV-1 pol sequences and observed that some characteristics (eg, over 50 years, married) were more likely to associated to the clusters (P<0.05). The predictive model showed excellent predictive ability to forecast cluster growth.

Conclusion

The epidemic genotypes were relatively complex and diverse in Guangyuan. There was a potential transmission association caused widely spread in local area after the new strains entering. The transmission risk prediction model showed excellent predictive ability to forecast cluster growth which can predict the risk factors causing clusters expansion and provide a guidance for precise intervention strategies.

Keywords:

• HIV-1
• genotype
• cluster analysis
• transmission risk prediction model

Acknowledgments

The authors would like to thank participants from Guangyuan Center for Disease Control and Prevention for their hard work on sample collection. Additionally, the authors would like to sincerely appreciate all investigators for their help and support.

Disclosure

The authors report no conflicts of interest in this work.

Additional information

Funding

This study was supported by Natural Science Foundation of Sichuan Province (grant number 2022NSFSC1547) and Scientific research project of Sichuan Center for Disease Control and Prevention (grant number ZX202018).