Integrating contact tracing data to enhance outbreak phylodynamic inference: a deep learning approach.

IF 11 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Molecular biology and evolution Pub Date : 2024-11-05 DOI:10.1093/molbev/msae232
Ruopeng Xie, Dillon C Adam, Shu Hu, Benjamin J Cowling, Olivier Gascuel, Anna Zhukova, Vijaykrishna Dhanasekaran
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Abstract

Phylodynamics is central to understanding infectious disease dynamics through the integration of genomic and epidemiological data. Despite advancements, including the application of deep learning to overcome computational limitations, significant challenges persist due to data inadequacies and statistical unidentifiability of key parameters. These issues are particularly pronounced in poorly resolved phylogenies, commonly observed in outbreaks such as SARS-CoV-2. In this study, we conducted a thorough evaluation of PhyloDeep, a deep learning inference tool for phylodynamics, assessing its performance on poorly resolved phylogenies. Our findings reveal the limited predictive accuracy of PhyloDeep (and other state-of-the-art approaches) in these scenarios. However, models trained on poorly resolved, realistically simulated trees demonstrate improved predictive power, despite not being infallible, especially in scenarios with superspreading dynamics, whose parameters are challenging to capture accurately. Notably, we observe markedly improved performance through the integration of minimal contact tracing data, which refines poorly resolved trees. Applying this approach to a sample of SARS-CoV-2 sequences partially matched to contact tracing from Hong Kong yields informative estimates of superspreading potential, extending beyond the scope of contact tracing data alone. Our findings demonstrate the potential for enhancing phylodynamic analysis through complementary data integration, ultimately increasing the precision of epidemiological predictions crucial for public health decision making and outbreak control.

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整合接触追踪数据以加强疫情系统动力学推断:一种深度学习方法。
系统动力学是通过整合基因组学和流行病学数据了解传染病动态的核心。尽管取得了进步,包括应用深度学习来克服计算限制,但由于数据不足和关键参数的统计不可识别性,重大挑战依然存在。这些问题在系统发育不完善的情况下尤为突出,这在 SARS-CoV-2 等疫情中很常见。在本研究中,我们对用于系统动力学的深度学习推断工具 PhyloDeep 进行了全面评估,评估了它在解析度较低的系统发生上的表现。我们的研究结果表明,PhyloDeep(以及其他最先进的方法)在这些情况下的预测准确性有限。然而,在解析度较差的真实模拟树上训练的模型尽管并非无懈可击,但其预测能力却有所提高,尤其是在具有超传播动态的情况下,其参数的准确捕捉具有挑战性。值得注意的是,通过整合最小接触追踪数据,我们观察到分辨率较低的树的性能明显提高。将这种方法应用于与香港接触追踪数据部分匹配的SARS-CoV-2序列样本,可以对超级传播潜力做出有参考价值的估计,超出了单纯接触追踪数据的范围。我们的研究结果表明,通过互补数据整合,可以提高系统动力学分析的潜力,最终提高对公共卫生决策和疫情控制至关重要的流行病学预测的精确度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Molecular biology and evolution
Molecular biology and evolution 生物-进化生物学
CiteScore
19.70
自引率
3.70%
发文量
257
审稿时长
1 months
期刊介绍: Molecular Biology and Evolution Journal Overview: Publishes research at the interface of molecular (including genomics) and evolutionary biology Considers manuscripts containing patterns, processes, and predictions at all levels of organization: population, taxonomic, functional, and phenotypic Interested in fundamental discoveries, new and improved methods, resources, technologies, and theories advancing evolutionary research Publishes balanced reviews of recent developments in genome evolution and forward-looking perspectives suggesting future directions in molecular evolution applications.
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