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SEPIA: simulation-based evaluation of prioritization algorithms

机译：乌贼属：基于模拟的优先算法评估

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The ability to prioritize people living with HIV (PLWH) by risk of future transmissions could aid public health officials in optimizing epidemiological intervention. While methods exist to perform such prioritization based on molecular data, their effectiveness and accuracy are poorly understood, and it is unclear how one can directly compare the accuracy of different methods. We introduce SEPIA (Simulation-based Evaluation of PrIoritization Algorithms), a novel simulation-based framework for determining the effectiveness of prioritization algorithms. SEPIA expands upon prior related work by defining novel metrics of effectiveness with which to compare prioritization techniques, as well as by creating a simulation-based tool with which to perform such effectiveness comparisons. Under several metrics of effectiveness that we propose, we compare two existing prioritization approaches: one phylogenetic (ProACT) and one distance-based (growth of HIV-TRACE transmission clusters). Using all proposed metrics, ProACT consistently slightly outperformed the transmission cluster growth approach. However, both methods consistently performed just marginally better than random, suggesting that there is significant room for improvement in prioritization tools. We hope that, by providing ways to quantify the effectiveness of prioritization methods in simulation, SEPIA will aid researchers in developing novel risk prioritization tools for PLWH.
机译：通过未来传播风险优先考虑艾滋病毒（PLWH）的人的能力可以帮助公共卫生官员优化流行病学干预。虽然存在基于分子数据执行这种优先级的方法，但其有效性和准确性明确地理解，并且目前尚不清楚人们如何直接比较不同方法的准确性。我们介绍乌贼属（基于仿真的优先级算法评估），一种基于仿真的基于仿真框架，用于确定优先级算法的有效性。通过定义有效性的新测量来展示先前相关的工作来扩展到相关的优先级化技术，以及通过创建基于模拟的工具来执行此类有效性比较的新的相关工作。在我们提出的若干有效性的指标下，我们比较了两个现有的优先级方法：一种系统发育（PROACC）和一种距离（HIV痕量传输簇的生长）。使用所有拟议的指标，始终如一地略微超越传输集群增长方法。然而，两种方法始终如一地略微比随机更好地进行，这表明优先级工具有很大的改进空间。我们希望通过提供量化仿真中优先级方法的有效性的方法，乌普亚将帮助研究人员开发新颖的PLWH风险优先级工具。

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