Laboratory of Chemical and Environmental Technology, Dept. of Chemistry, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece.
Laboratory of Environmental Engineering & Planning, Department. of Civil Engineering, Aristotle University of Thessaloniki, Thessaloniki 54 124, Greece.
Sci Total Environ. 2024 Nov 15;951:175724. doi: 10.1016/j.scitotenv.2024.175724. Epub 2024 Aug 22.
During the COVID-19 pandemic, wastewater-based epidemiology has proved to be an important tool for monitoring the spread of a disease in a population. Indeed, wastewater surveillance was successfully used as a complementary approach to support public health monitoring schemes and decision-making policies. An essential feature for the estimation of a disease transmission using wastewater data is the distribution of viral shedding rate of individuals in their personal human wastes as a function of the days of their infection. Several candidate shapes for this function have been proposed in literature for SARS-CoV-2. The purpose of the present work is to explore the proposed function shapes and examine their significance on analyzing wastewater SARS-CoV-2 shedding rate data. For this purpose, a simple model is employed applying to medical surveillance and wastewater data of the city of Thessaloniki during a period of Omicron variant domination in 2022. The distribution shapes are normalized with respect to the total virus shedding and then their basic features are investigated. Detailed analysis reveals that the main parameter determining the results of the model is the difference between the day of maximum shedding rate and the day of infection reporting. Since the latter is not part of the distribution shape, the major feature of the distribution affecting the estimation of the number of infected people is the day of maximum shedding rate with respect to the initial infection day. On the contrary, the duration of shedding (total number of disease days) as well as the exact shape of the distribution are by far less important. The incorporation of such wastewater surveillance models in conventional epidemiological models - based on recorded disease transmission data- may improve predictions for disease spread during outbreaks.
在 COVID-19 大流行期间，基于污水的流行病学已被证明是监测人群中疾病传播的重要工具。事实上，污水监测已成功用作支持公共卫生监测计划和决策政策的补充方法。使用污水数据估计疾病传播的一个重要特征是个体在个人人类粪便中病毒脱落率随感染天数的分布。文献中已经提出了几种用于 SARS-CoV-2 的候选函数形状。本工作的目的是探索所提出的函数形状，并检验它们在分析污水 SARS-CoV-2 脱落率数据中的重要性。为此，我们采用了一种简单的模型，应用于 2022 年在奥密克戎变体主导时期的塞萨洛尼基市的医疗监测和污水数据。这些分布形状与总病毒脱落进行归一化，然后研究它们的基本特征。详细分析表明，决定模型结果的主要参数是最大脱落率日与感染报告日之间的差异。由于后者不是分布形状的一部分，因此影响感染人数估计的分布主要特征是相对于初始感染日的最大脱落率日。相反，脱落持续时间（疾病总天数）以及分布的精确形状重要性要小得多。将此类污水监测模型纳入基于记录的疾病传播数据的传统流行病学模型中，可能会提高对爆发期间疾病传播的预测。
