Applied Statistics Research Group, Department of Mathematics, Physics and Electrical Engineering, Northumbria University, Newcastle upon Tyne NE1 8ST, UK; Turing-RSS Health Data Lab, UK.
Analytics & Data Science Directorate, UK Health Security Agency, Nobel House, Smith Square, London SW1P 3JR, UK.
Environ Int. 2023 Feb;172:107765. doi: 10.1016/j.envint.2023.107765. Epub 2023 Jan 18.
The potential utility of wastewater-based epidemiology as an early warning tool has been explored widely across the globe during the current COVID-19 pandemic. Methods to detect the presence of SARS-CoV-2 RNA in wastewater were developed early in the pandemic, and extensive work has been conducted to evaluate the relationship between viral concentration and COVID-19 case numbers at the catchment areas of sewage treatment works (STWs) over time. However, no attempt has been made to develop a model that predicts wastewater concentration at fine spatio-temporal resolutions covering an entire country, a necessary step towards using wastewater monitoring for the early detection of local outbreaks. We consider weekly averages of flow-normalised viral concentration, reported as the number of SARS-CoV-2N1 gene copies per litre (gc/L) of wastewater available at 303 STWs over the period between 1 June 2021 and 30 March 2022. We specify a spatially continuous statistical model that quantifies the relationship between weekly viral concentration and a collection of covariates covering socio-demographics, land cover and virus associated genomic characteristics at STW catchment areas while accounting for spatial and temporal correlation. We evaluate the model's predictive performance at the catchment level through 10-fold cross-validation. We predict the weekly viral concentration at the population-weighted centroid of the 32,844 lower super output areas (LSOAs) in England, then aggregate these LSOA predictions to the Lower Tier Local Authority level (LTLA), a geography that is more relevant to public health policy-making. We also use the model outputs to quantify the probability of local changes of direction (increases or decreases) in viral concentration over short periods (e.g. two consecutive weeks). The proposed statistical framework can predict SARS-CoV-2 viral concentration in wastewater at high spatio-temporal resolution across England. Additionally, the probabilistic quantification of local changes can be used as an early warning tool for public health surveillance.
在当前的 COVID-19 大流行期间，全球范围内广泛探索了将废水流行病学作为早期预警工具的潜力。在大流行早期就开发了检测废水中 SARS-CoV-2 RNA 存在的方法，并进行了广泛的工作，以评估污水处理厂（STW）集水区随时间推移的病毒浓度与 COVID-19 病例数之间的关系。然而，尚未尝试开发一种能够以精细时空分辨率预测整个国家废水浓度的模型，这是将废水监测用于早期检测局部暴发的必要步骤。我们考虑每周平均流量归一化的病毒浓度，以每周 303 个 STW 报告的每升废水 SARS-CoV-2N1 基因拷贝数（gc/L）表示，报告时间为 2021 年 6 月 1 日至 2022 年 3 月 30 日。我们指定了一个空间连续的统计模型，该模型量化了每周病毒浓度与涵盖 STW 集水区社会人口统计学、土地覆盖和与病毒相关的基因组特征的一系列协变量之间的关系，同时考虑了空间和时间相关性。我们通过 10 倍交叉验证评估模型在集水区水平上的预测性能。我们预测英格兰 32844 个较低超级输出区（LSOA）的人口加权质心的每周病毒浓度，然后将这些 LSOA 预测汇总到更适合公共卫生决策制定的较低层级地方当局（LTLA）级别。我们还使用模型输出来量化短时间内（例如连续两周）病毒浓度局部变化（增加或减少）的概率。该统计框架可以在整个英格兰以高时空分辨率预测废水中 SARS-CoV-2 的病毒浓度。此外，对局部变化的概率量化可作为公共卫生监测的早期预警工具。
