The impact of signal variability on epidemic growth rate estimation from wastewater surveillance data
The impact of signal variability on epidemic growth rate estimation from wastewater surveillance data
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DOI：
10.1101/2023.03.07.23286904
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发表时间：
2023-03
期刊：
影响因子：
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通讯作者：
E. Colman;R. Kao
中科院分区：
文献类型：
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作者：
E. Colman;R. Kao
背景：在新冠肺炎大流行期间，对废水样本进行传染病标志物检测成为一种广泛使用的监测方法。虽然这些数据总体上与国家流行的其他指标有很好的相关性，但覆盖局部地区的样本在短时间尺度上往往具有很大的变数。方法：我们介绍了一种使用污水采样的时间序列数据来估计病原体流行的实时增长率的方法。在样本中发现的目标基因的拷贝数被建模为与时间相关的随机变量，其分布使用最大似然估计。输出依赖于一个超参数，该参数控制对基础数据中的可变性的敏感性。我们将这一程序应用于报告2021年2月至2023年2月期间在苏格兰各地的水处理厂收集的SARS-CoV-2 N1基因拷贝数的数据。结果：估计了121个废水采样点的SARS-CoV-2流行的实时增长率，覆盖了不同的地点和人口规模。我们发现，拟合过程对自然变异性的敏感性决定了它在检测流行病波早期阶段的可靠性。将该程序应用于医院入院数据，我们发现废水中的增长率变化比医院入院数据平均提前2天检测到。结论：我们提供了一种稳健的方法，可以从高度可变的数据中生成可靠的疫情增长估计。将这种方法应用于在污水处理厂收集的样本，可以提供高度灵敏的情景感知，以告知公众健康。
Background: Testing samples of waste water for markers of infectious disease became a widespread method of surveillance during the COVID-19 pandemic. While these data generally correlate well with other indicators of national prevalence, samples that cover localised regions tend to be highly variable over short time scales. Methods: We introduce a procedure for estimating the real-time growth rate of pathogen prevalence using time series data from wastewater sampling. The number of copies of a target gene found in a sample is modelled as time-dependent random variable whose distribution is estimated using maximum likelihood. The output depends on a hyperparameter that controls the sensitivity to variability in the underlying data. We apply this procedure to data reporting the number of copies of the N1 gene of SARS-CoV-2 collected at water treatment works across Scotland between February 2021 and February 2023. Results: The real-time growth rate of the SARS-CoV-2 prevalence is estimated at 121 wastewater sampling sites covering a diverse range of locations and population sizes. We find that the sensitivity of the fitting procedure to natural variability determines its reliability in detecting the early stages of an epidemic wave. Applying the procedure to hospital admissions data, we find that changes in the growth rate are detected an average of 2 days earlier in wastewater than in hospital admissions data. Conclusion: We provide a robust method to generate reliable estimates of epidemic growth from highly variable data. Applying this method to samples collected at wastewater treatment works provides highly responsive situational awareness to inform public health.