Xu Bin, Shi Xinfu, Liang Changwei, Shi Congxing, Peng Chuyun, Lai Yingsi
Department of Infectious Diseases, Nanning Center for Disease Control and Prevention, Nanning, 530023, China.
Department of Medical Statistics, School of Public Health, Sun Yat-sen University, No.74 Zhongshan 2nd Road, Guangzhou, 510080, China.
BMC Public Health. 2025 Jan 9;25(1):118. doi: 10.1186/s12889-024-20968-x.
COVID-19 has caused tremendous hardships and challenges around the globe. Due to the prevalence of asymptomatic and pre-symptomatic carriers, relying solely on disease testing to screen for infections is not entirely reliable, which may affect the accuracy of predictions about the pandemic trends. This study is dedicated to developing a predictive model aimed at estimating of the dynamics of COVID-19 at an early stage based on wastewater data, to assist in establishing an effective early warning system for disease control.
Viral load in wastewater and the number of daily reported COVID-19 cases were collected from Nanning CDC and the Chinese Disease Prevention and Control Information System, respectively. We used the viral load to estimate daily reported cases by a Bayesian linear regression model. Subsequently, a Bayesian (segmented) Poisson regression model was developed, using data from the first wave of the epidemic as prior information, to predict the COVID-19 epidemic trend of the second wave. Finally, in order to explore the optimal training data for predicting outbreak dynamics during the pandemic, we fitted the model using various training sets.
The results revealed the estimated cases, using the viral load with a 3-day lag, were consistent with the actual reported cases, with adjusted R² value of 0.935 (p < 0.001). Our model successfully predicted the epidemic peak time and provided early warnings on the third day after the outbreak began. Furthermore, after using data from the first 6 days of the outbreak, the model's MAPE rapidly decreasing to lower levels (MAPE = 29.34%) and eventually stabilized at approximately 20%. Compared to using non-informative priors, this result allows for an advance warning of approximately two weeks. Importantly, as the inclusion of data from early outbreak increased, the predictive results of the model became more stable and accurate.
This study demonstrates the potential of wastewater-based epidemiology combined with Bayesian methods as a monitoring and predictive tool during infectious disease outbreaks.
新型冠状病毒肺炎（COVID-19）在全球范围内造成了巨大困难和挑战。由于无症状和症状前携带者的普遍存在，仅依靠疾病检测来筛查感染并不完全可靠，这可能会影响对疫情趋势预测的准确性。本研究致力于开发一种预测模型，旨在基于废水数据估计COVID-19早期的动态变化，以协助建立有效的疾病控制早期预警系统。
分别从南宁市疾病预防控制中心和中国疾病预防控制信息系统收集废水中的病毒载量和每日报告的COVID-19病例数。我们使用贝叶斯线性回归模型，利用病毒载量估计每日报告病例数。随后，以疫情第一波的数据作为先验信息，建立了贝叶斯（分段）泊松回归模型，以预测第二波COVID-19疫情趋势。最后，为了探索在疫情期间预测疫情动态的最佳训练数据，我们使用各种训练集对模型进行拟合。
结果显示，使用滞后3天的病毒载量估计的病例数与实际报告病例数一致，调整后的R²值为0.935（p<0.001）。我们的模型成功预测了疫情高峰时间，并在疫情开始后的第三天提供了早期预警。此外，在使用疫情爆发前6天的数据后，模型的平均绝对百分比误差（MAPE）迅速降至较低水平（MAPE=29.34%），最终稳定在约20%。与使用非信息先验相比，这一结果可提前约两周发出预警。重要的是，随着纳入疫情早期数据的增加，模型的预测结果变得更加稳定和准确。
本研究证明了基于废水的流行病学与贝叶斯方法相结合作为传染病爆发期间监测和预测工具的潜力。
