树统计量相较于仅用病例计数可改进流行病学参数的神经模拟推理
Tree statistics improve neural simulation-based inference of epidemiological parameters over case counts alone
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中文总结 AI 辅助
该研究将病例计数与传播树的汇总统计量结合，通过神经比率估计（NRE）改进了流行病学参数（R0、D）的估计，其表现优于仅用病例计数的基线方法。
中文摘要 AI 辅助
在传染病暴发期间，对繁殖数和感染持续时间等流行病学参数的准确估计可为公共卫生应对提供信息。近年来，神经模拟推理（NSBI）方法因比传统方法计算成本更低、输入数据灵活性更高，在估计这些参数方面变得流行。在NSBI框架中单独评估过的输入数据模态包括病例计数和传播树。本研究评估将这两种数据类型的信息结合起来是否比单独使用每种数据的信息在NSBI中表现更好。为此，我们在一系列基本繁殖数（R0）和平均感染持续时间（D）下模拟了随机易感-感染-恢复（SIR）暴发，以生成训练、验证和测试数据集。然后，我们为每个模拟暴发计算了病例计数轨迹和传播树的汇总统计量。使用这些特征（单独使用或组合使用），我们执行了一种名为神经比率估计（NRE）的NSBI来估计R0和D。我们还通过拟合确定性SIR模型并使用病例计数轨迹计算了参数的最大似然估计。所有三个NRE模型的表现都优于基线方法。此外，与仅使用病例计数汇总统计量相比，纳入树汇总统计量改进了R0和D的估计。这表明，树提供了超出仅病例计数之外的、用于估计流行病学参数的有价值附加信息。
英文摘要
During an infectious disease outbreak, accurate estimates of epidemiological parameters such as the reproduction number and infection duration can inform the public health response. Recently, neural simulation-based inference (NSBI) methods have become popular for estimating these parameters due to lower computational costs and more input data flexibility than traditional methods. Input data modalities that have been evaluated independently in an NSBI framework include case counts and transmission trees. Here, we evaluate whether combining information from these two data types outperforms using information from each individually with NSBI. To do so, we simulated stochastic susceptible-infectious-recovered (SIR) outbreaks across a range of basic reproduction numbers (R0) and average infection durations (D) to generate training, validation, and testing datasets. We then calculated, for each simulated outbreak, summary statistics from case count trajectories and from transmission trees. Using these features, individually and in combination, we performed a type of NSBI called neural ratio estimation (NRE) to estimate R0 and D. We also calculated maximum likelihood estimates of the parameters by fitting a deterministic SIR model using the case count trajectory. All three NRE models outperformed the baseline method. Furthermore, incorporating tree summary statistics improved the estimation of both R0 and D over case count summaries alone. This suggests that trees provide valuable additional information for estimating epidemiological parameters above and beyond case counts alone.
发表机构
- Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）
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