Inference of effective population size from genomic data can provide unique information about demographic history, and when applied to pathogen genetic data can also provide insights into epidemiological dynamics. The combination of non-parametric models for population dynamics with molecular clock models which relate genetic data to time has enabled phylodynamic inference based on large sets of time-stamped genetic sequence data. The methodology for non-parametric inference of effective population size is well-developed in the Bayesian setting, but here we develop a frequentist approach based on non-parametric latent process models of population size dynamics. We appeal to statistical principles based on out-of-sample prediction accuracy in order to optimize parameters that control shape and smoothness of the population size over time. We demonstrate the flexibility and speed of this approach in a series of simulation experiments, and apply the methodology to reconstruct the previously described waves in the seventh pandemic of cholera. We also estimate the impact of non-pharmaceutical interventions for COVID-19 in England using thousands of SARS-CoV-2 sequences. By incorporating a measure of the strength of these interventions over time within the phylodynamic model, we estimate the impact of the first national lockdown in the UK on the epidemic reproduction number.

中文翻译：

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非参数系统动力学推断的模型设计及其在病原体监测中的应用

从基因组数据推断有效种群大小可以提供有关人口统计历史的独特信息，并且当应用于病原体遗传数据时，还可以提供对流行病学动态的见解。用于种群动力学的非参数模型与将遗传数据与时间相关联的分子钟模型相结合，使得基于大量带时间戳的遗传序列数据的系统动力学推断成为可能。有效人口规模的非参数推断方法在贝叶斯环境中得到了很好的发展，但在这里我们开发了一种基于人口规模动态的非参数潜在过程模型的频率论方法。我们呼吁基于样本外预测准确性的统计原则，以优化控制人口规模随时间变化的形状和平滑度的参数。我们在一系列模拟实验中证明了这种方法的灵活性和速度，并应用该方法来重建先前描述的第七次霍乱大流行中的波浪。我们还使用数千个 SARS-CoV-2 序列估计了非药物干预对英格兰 COVID-19 的影响。通过在系统动力学模型中纳入对这些干预措施随时间推移的强度的衡量，我们估计了英国第一次全国封锁对流行病繁殖数量的影响。我们还使用数千个 SARS-CoV-2 序列估计了非药物干预对英格兰 COVID-19 的影响。通过在系统动力学模型中纳入对这些干预措施随时间推移的强度的衡量，我们估计了英国第一次全国封锁对流行病繁殖数量的影响。我们还使用数千个 SARS-CoV-2 序列估计了非药物干预对英格兰 COVID-19 的影响。通过在系统动力学模型中纳入对这些干预措施随时间推移的强度的衡量，我们估计了英国第一次全国封锁对流行病繁殖数量的影响。