The Ottawa Hospital Research Institute, 1053 Carling Ave, Ottawa, Ontario K1Y 4E9, Canada; University of Ottawa, Ottawa, 75 Laurier Ave E, ON K1N 6N5, Canada.
The Ottawa Hospital Research Institute, 1053 Carling Ave, Ottawa, Ontario K1Y 4E9, Canada.
Sci Total Environ. 2024 Nov 1;949:174937. doi: 10.1016/j.scitotenv.2024.174937. Epub 2024 Jul 25.
Day-to-day variation in the measurement of SARS-CoV-2 in wastewater can challenge public health interpretation. We assessed a Bayesian smoothing and forecasting method previously used for surveillance and short-term projection of COVID-19 cases, hospitalizations, and deaths.
SARS-CoV-2 viral measurement from the sewershed in Ottawa, Canada, sampled at the municipal wastewater treatment plant from July 1, 2020, to February 15, 2022, was used to assess and internally validate measurement averaging and prediction. External validation was performed using viral measurement data from influent wastewater samples from 15 wastewater treatment plants and municipalities across Ontario.
Plots of SARS-CoV-2 viral measurement over time using Bayesian smoothing visually represented distinct COVID-19 "waves" described by case and hospitalization data in both initial (Ottawa) and external validation in 15 Ontario communities. The time-varying growth rate of viral measurement in wastewater samples approximated the growth rate observed for cases and hospitalization. One-week predicted viral measurement approximated the observed viral measurement throughout the assessment period from December 23, 2020, to August 8, 2022. An uncalibrated model showed underprediction during rapid increases in viral measurement (positive growth) and overprediction during rapid decreases. After recalibration, the model showed a close approximation between observed and predicted estimates.
Bayesian smoothing of wastewater surveillance data of SARS-CoV-2 allows for accurate estimates of COVID-19 growth rates and one- and two-week forecasting of SARS-CoV-2 in wastewater for 16 municipalities in Ontario, Canada. Further assessment is warranted in other communities representing different sewersheds and environmental conditions.
在污水中每日测量 SARS-CoV-2 可能会对公共卫生解释构成挑战。我们评估了一种贝叶斯平滑和预测方法，该方法以前曾用于 COVID-19 病例、住院和死亡的监测和短期预测。
使用加拿大渥太华污水流域的污水中 SARS-CoV-2 的病毒测量值，该测量值于 2020 年 7 月 1 日至 2022 年 2 月 15 日在市政污水处理厂进行采样，用于评估和内部验证测量平均值和预测值。外部验证是使用来自安大略省 15 个污水处理厂和市政当局的进水污水样本中的病毒测量数据进行的。
使用贝叶斯平滑对随时间变化的 SARS-CoV-2 病毒测量值进行绘图，直观地表示了初始（渥太华）和外部验证在安大略省 15 个社区中病例和住院数据所描述的 COVID-19“波”。污水样本中病毒测量值的时变增长率与病例和住院的观察增长率近似。在整个评估期间（从 2020 年 12 月 23 日至 2022 年 8 月 8 日），对下周的病毒测量值进行预测，可近似预测观察到的病毒测量值。未经校准的模型在病毒测量值快速增加（正增长）时显示低估，在病毒测量值快速减少时显示高估。经过重新校准后，该模型显示出观察到的和预测到的估计值之间的紧密逼近。
对 SARS-CoV-2 污水监测数据进行贝叶斯平滑，可以准确估计加拿大安大略省 16 个城市的 COVID-19 增长率，并对污水中的 SARS-CoV-2 进行一到两周的预测。需要在代表不同污水流域和环境条件的其他社区进一步评估。
