ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2022
Optimal resource allocation in HIV self-testing secondary distribution among Chinese MSM: data-driven integer programming models.
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 10 citations in OpenAlex.
- Evaluating peer navigation in promoting digital secondary distribution of HIV self-testing among men who have sex with men in China: study protocol for a two-arm randomized controlled trial.BMC public health · 2026Article
- The Application of Machine Learning Algorithms to Predict HIV Testing Using Evidence from the 2002-2017 South African Adult Population-Based Surveys: An HIV Testing Predictive Model.Tropical medicine and infectious disease · 2025Article
- The status of machine learning in HIV testing in South Africa: a qualitative inquiry with stakeholders in Gauteng province.Frontiers in digital health · 2025Article
- A Machine Learning Model for Identifying Sexual Health Influencers to Promote the Secondary Distribution of HIV Self-Testing Among Gay, Bisexual, and Other Men Who Have Sex With Men in China: Quasi-Experimental Study.JMIR public health and surveillance · 2024Article
- Data science approaches to infectious disease surveillance.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Review
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Authors and funding
9 authors at 6 institutions in 6 countries.
Funding
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Abstract
Human immunodeficiency virus self-testing (HIVST) is an innovative and effective strategy important to the expansion of HIV testing coverage. Several innovative implementations of HIVST have been developed and piloted among some HIV high-risk populations like men who have sex with men (MSM) to meet the global testing target. One innovative strategy is the secondary distribution of HIVST, in which individuals (defined as indexes) were given multiple testing kits for both self-use (i.e.self-testing) and distribution to other people in their MSM social network (defined as alters). Studies about secondary HIVST distribution have mainly concentrated on developing new intervention approaches to further increase the effectiveness of this relatively new strategy from the perspective of traditional public health discipline. There are many points of HIVST secondary distribution in which mathematical modelling can play an important role. In this study, we considered secondary HIVST kits distribution in a resource-constrained situation and proposed two data-driven integer linear programming models to maximize the overall economic benefits of secondary HIVST kits distribution based on our present implementation data from Chinese MSM. The objective function took expansion of normal alters and detection of positive and newly-tested 'alters' into account. Based on solutions from solvers, we developed greedy algorithms to find final solutions for our linear programming models. Results showed that our proposed data-driven approach could improve the total health economic benefit of HIVST secondary distribution. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'.
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