背景:废水中SARS-CoV-2测量的日常变化可能会挑战公共卫生解释。我们评估了以前用于COVID-19病例监测和短期预测的贝叶斯平滑和预测方法,住院治疗,和死亡。
方法:来自渥太华下水道的SARS-CoV-2病毒测量,加拿大,2020年7月1日至2022年2月15日在市政污水处理厂采样,用于评估和内部验证测量平均和预测。使用来自安大略省15个污水处理厂和市政当局的流入废水样品的病毒测量数据进行外部验证。
结果:使用贝叶斯平滑的SARS-CoV-2病毒随时间的测量图直观地代表了在初始(渥太华)和15个安大略省社区的外部验证中由病例和住院数据描述的不同的COVID-19“波”。废水样品中病毒测量的随时间变化的生长速率近似于病例和住院观察到的生长速率。从2020年12月23日至2022年8月8日的整个评估期间,一周的预测病毒测量值接近观察到的病毒测量值。未校准的模型在病毒测量(正生长)的快速增加期间显示预测不足,在快速减少期间显示预测过度。重新校准后,该模型显示了观察到的和预测的估计值之间的近似。
结论:对SARS-CoV-2的废水监测数据进行贝叶斯平滑,可以准确估计COVID-19的增长率,并对SARS-CoV-2的废水进行一周和两周的预测。安大略省的16个城市,加拿大。代表不同下水道和环境条件的其他社区需要进一步评估。
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.