Test-and-treat approach to HIV/AIDS: a primer for mathematical modeling.
Test-and-treat approach to HIV/AIDS: a primer for mathematical modeling.
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DOI：
10.1186/s12976-017-0062-9
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发表时间：
2017-09-05
影响因子：
--
通讯作者：
Imamura A
中科院分区：
文献类型：
--
作者：
Nah K;Nishiura H;Tsuchiya N;Sun X;Asai Y;Imamura A
测试和治疗的公共利益引起了需要证明善良的公众，和数学建模研究在设计和评估的测试和治疗策略，以控制艾滋病毒/艾滋病发挥了关键作用。在这里，我们简要而全面地回顾了当代理解的测试和治疗政策的本质，通过数学建模的方法，并确定已被确定为日期的关键陷阱。虽然艾滋病毒发病率的下降是通过提供一定的诊断、护理和持续治疗来实现的，但艾滋病毒的流行率并不一定下降，有时检测和治疗伴随着抗逆转录病毒疗法的长期费用增加。针对这一政策评估的复杂性，提出了消除阈值或有效再生产数来确定总体成功率，以预测最终消除。自2009年发表原始模型以来，已经确定了测试和治疗建模研究的关键问题，包括围绕性伙伴关系网络的理论问题，传播动力学的异质性，以及在最难以接触的人群中实现和保持高治疗覆盖率的现实问题。为了明确地设计针对具体国家的控制政策，针对具有不同流行病学背景的每一种环境的定量建模方法将需要临床医生、公共卫生从业人员、实验室技术人员、流行病学家和数学建模人员之间的多学科合作。
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.
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影响因子：
3.7
作者：
Luo S;Han L;Lu H;Dou Z;Tao Q;Khoshnood K;Wu Z;Xu J
通讯作者：
Xu J
DOI：
10.1126/science.1228160
发表时间：
2013-02-22
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Tanser F;Bärnighausen T;Grapsa E;Zaidi J;Newell ML
通讯作者：
Newell ML
影响因子：
3.6
作者：
Brown T;Bao L;Raftery AE;Salomon JA;Baggaley RF;Stover J;Gerland P
通讯作者：
Gerland P
影响因子：
5.4
作者：
Kim, Jong-Hoon;Riolo, Rick L.;Koopman, James S.
通讯作者：
Koopman, James S.
影响因子：
3.6
作者：
Shafer, Leigh Anne;Nsubuga, Rebecca N.;White, Richard G.
通讯作者：
White, Richard G.