Colman Ewan, Kao Rowland
Bristol Medical School, University of Bristol, Oakfield Grove, Bristol, United Kingdom.
Royal (Dick) School of Veterinary Studies and the Roslin Institute, University of Edinburgh, Easter Bush, Midlothian, United Kingdom.
PLoS One. 2025 May 28;20(5):e0322057. doi: 10.1371/journal.pone.0322057. eCollection 2025.
Testing samples of wastewater 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. Here 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. The real-time growth rate of the SARS-CoV-2 prevalence is estimated at all 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 same 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. In conclusion, this paper provides 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.
在新冠疫情期间，检测废水样本中的传染病标志物成为一种广泛应用的监测方法。虽然这些数据通常与国家流行率的其他指标相关性良好，但覆盖局部地区的样本在短时间尺度上往往变化很大。在此，我们介绍一种利用废水采样的时间序列数据估算病原体流行率实时增长率的方法。样本中发现的目标基因拷贝数被建模为一个随时间变化的随机变量，其分布通过最大似然法进行估计。输出结果取决于一个控制对基础数据变异性敏感度的超参数。我们将此方法应用于2021年2月至2023年2月期间在苏格兰各地污水处理厂收集的报告新冠病毒N1基因拷贝数的数据。在覆盖不同地点和人口规模的所有121个废水采样点，我们估算了新冠病毒流行率的实时增长率。我们发现，拟合程序对自然变异性的敏感度决定了其在检测疫情波早期阶段的可靠性。将相同方法应用于医院入院数据时，我们发现废水中增长率的变化平均比医院入院数据早2天被检测到。总之，本文提供了一种从高度可变数据中生成可靠疫情增长估计值的稳健方法。将此方法应用于污水处理厂收集的样本，可提供高度灵敏的态势感知，为公共卫生提供信息。
