Test-and-treat approach to HIV/AIDS: A primer for mathematical modeling

Kyeongah Nah, Hiroshi Nishiura, Naho Tsuchiya, Xiaodan Sun, Yusuke Asai, Akifumi Imamura

Research output: Contribution to journalReview articlepeer-review

4 Citations (Scopus)

Abstract

The public benefit of test-and-treat has induced a need to justify goodness for the public, and mathematical modeling studies have played a key role in designing and evaluating the test-and-treat strategy for controlling HIV/AIDS. Here we briefly and comprehensively review the essence of contemporary understanding of the test-and-treat policy through mathematical modeling approaches and identify key pitfalls that have been identified to date. While the decrease in HIV incidence is achieved with certain coverages of diagnosis, care and continued treatment, HIV prevalence is not necessarily decreased and sometimes the test-and-treat is accompanied by increased long-term cost of antiretroviral therapy (ART). To confront with the complexity of assessment on this policy, the elimination threshold or the effective reproduction number has been proposed for its use in determining the overall success to anticipate the eventual elimination. Since the publication of original model in 2009, key issues of test-and-treat modeling studies have been identified, including theoretical problems surrounding the sexual partnership network, heterogeneities in the transmission dynamics, and realistic issues of achieving and maintaining high treatment coverage in the most hard-to-reach populations. To explicitly design country-specific control policy, quantitative modeling approaches to each single setting with differing epidemiological context would require multi-disciplinary collaborations among clinicians, public health practitioners, laboratory technologists, epidemiologists and mathematical modelers.

Original languageEnglish
Article number16
JournalTheoretical Biology and Medical Modelling
Volume14
Issue number1
DOIs
Publication statusPublished - 2017 Sep 5

ASJC Scopus subject areas

  • Modelling and Simulation
  • Health Informatics

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