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Comprehensive & Cost Effective Laboratory Monitoring of HIV/AIDS: an African Role Model
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Optimal resource allocation in HIV self-testing secondary distribution among Chinese MSM: data-driven integer

Fengshi Jing1,2,3, Qingpeng Zhang3, Jason J Ong4,5

  • 1Institute for Healthcare Artificial Intelligence, Guangdong Second Provincial General Hospital, Guangzhou 510317, People's Republic of China.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|November 22, 2021
PubMed
Summary
This summary is machine-generated.

Human immunodeficiency virus self-testing (HIVST) models optimize kit distribution among men who have sex with men (MSM). This data-driven approach enhances economic benefits and expands HIV testing coverage efficiently.

Keywords:
HIV self-testinggreedy algorithminteger programmingmathematical optimizationsecondary distribution

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Area of Science:

  • Data science
  • Public health
  • Mathematical modeling

Background:

  • Human immunodeficiency virus self-testing (HIVST) is crucial for increasing HIV testing coverage.
  • Secondary distribution of HIVST kits among men who have sex with men (MSM) is an innovative strategy.
  • Optimizing HIVST distribution in resource-constrained settings requires novel approaches.

Purpose of the Study:

  • To develop data-driven mathematical models for optimizing secondary HIVST kit distribution.
  • To maximize the health economic benefits of HIVST secondary distribution among Chinese MSM.
  • To improve the efficiency of HIV testing programs through strategic kit allocation.

Main Methods:

  • Proposed two integer linear programming models for secondary HIVST distribution.
  • Utilized implementation data from Chinese MSM for model development.
  • Developed greedy algorithms to find solutions for the linear programming models.

Main Results:

  • The data-driven models successfully maximized the economic benefits of secondary HIVST distribution.
  • The approach improved the overall health economic benefit compared to traditional methods.
  • The models considered the expansion of networks and detection of newly tested individuals.

Conclusions:

  • Mathematical modeling offers a powerful tool for optimizing HIVST secondary distribution strategies.
  • Data-driven approaches can significantly enhance the cost-effectiveness of HIV testing programs.
  • This study provides a framework for improving HIV prevention efforts in resource-limited settings.