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Jing F, Ye Y, Zhou Y, Ni Y, Yan X, Lu Y, Ong JJ, Tucker JD, Wu D, Xiong Y, Xu C, He X, Huang S, Li X, Jiang H, Wang C, Dai W, Huang L, Mei W, Cheng W, Zhang Q, Tang W
Identification of Key Influencers for Secondary Distribution of HIV Self-Testing Kits Among Chinese Men Who Have Sex With Men: Development of an Ensemble Machine Learning Approach
Identification of Key Influencers for Secondary Distribution of HIV Self-Testing among Chinese MSM: A Machine Learning Approach
Fengshi Jing;
Yang Ye;
Yi Zhou;
Yuxin Ni;
Xumeng Yan;
Ying Lu;
Jason J Ong;
Joseph D Tucker;
Dan Wu;
Yuan Xiong;
Chen Xu;
Xi He;
Shanzi Huang;
Xiaofeng Li;
Hongbo Jiang;
Cheng Wang;
Wencan Dai;
Liqun Huang;
Wenhua Mei;
Weibin Cheng;
Qingpeng Zhang;
Weiming Tang
ABSTRACT
Background:
HIV self-testing (HIVST) has been rapidly scaled up and additional strategies further expand testing uptake. Secondary distribution has people (indexes) apply for multiple kits and pass these kits to people (alters) in their social networks. However, identifying key influencers is difficult.
Objective:
This study aimed to develop an innovative ensemble machine learning approach to identify key influencers among Chinese men who have sex with men (MSM) for HIVST secondary distribution.
Methods:
We defined three types of key influencers: 1) key distributors who can distribute more kits; 2) key promoters who can contribute to finding first-time testing alters; 3) key detectors who can help to find positive alters. Four machine learning models (logistic regression, support vector machine, decision tree, random forest) were trained to identify key influencers. An ensemble learning algorithm was adopted to combine these four models. Simulation experiments were run to validate our approach.
Results:
309 indexes distributed kits to 269 alters. Our approach outperformed human identification (self-reported scales cut-off), exceeding by an average accuracy of 11·0%, could distribute 18·2% (95%CI: 9·9%-26·5%) more kits, find 13·6% (95%CI: 1·9%-25·3%) more first-time testing alters and 12·0% (95%CI: -14·7%-38·7%) more positive-testing alters. Our approach could also increase simulated intervention efficiency by 17·7% (95%CI: -3·5%-38·8%) than human identification.
Conclusions:
We built machine learning models to identify key influencers among Chinese MSM who were more likely to engage in HIVST secondary distribution. Clinical Trial: Our study was a secondary modeling analysis of an RCT, which was registered with the Chinese Clinical Trial Registry (ChiCTR) ChiCTR1900025433.
Citation
Please cite as:
Jing F, Ye Y, Zhou Y, Ni Y, Yan X, Lu Y, Ong JJ, Tucker JD, Wu D, Xiong Y, Xu C, He X, Huang S, Li X, Jiang H, Wang C, Dai W, Huang L, Mei W, Cheng W, Zhang Q, Tang W
Identification of Key Influencers for Secondary Distribution of HIV Self-Testing Kits Among Chinese Men Who Have Sex With Men: Development of an Ensemble Machine Learning Approach