Tang Shunyu, Cao Yongtao
Department of Mathematical and Computer Sciences, Indiana University of Pennsylvania, Indiana, PA 15705, United States of America.
Department of Mathematical and Computer Sciences, Indiana University of Pennsylvania, Indiana, PA 15705, United States of America.
Sci Total Environ. 2023 Dec 1;902:166024. doi: 10.1016/j.scitotenv.2023.166024. Epub 2023 Aug 2.
Although wastewater-based epidemiology (WBE) has emerged as an inexpensive and non-intrusive method in contrast to clinical testing to track public health at community levels, there is a lack of structured interpretative criteria to translate the SARS-CoV-2 concentrations in wastewater to COVID-19 infection cases. The difficulties lie in the uncertainties of the amount of virus shed by an infected individual to wastewater as documented in clinical studies. This situation is even worse considering the existence of a population of silent infections and many other confounding factors. In this research, a quantitative framework of a phenomenological neural network (PNN) was developed to compute silent infections. The PNN was trained using the WBE data from the National Wastewater Surveillance System (NWSS) - a program launched by the CDC of the United States in 2020. It is found that the PNN excelled with superior interpretability and reduced overfitting. A big-data perspective on virus shedding by an infected population revealed more deterministic virus-shedding dynamics compared to the clinical studies perspective on virus shedding by an infected individual. With such characteristics employed as the theoretical basis for the estimation of the silent infections, a ratio of silent to reported infections was found to be 5.7 as the national median during the studied period. The study also noted the influence of temperature, sewershed population, and per-capita flow rates on the computation of silent infections. It is expected that the proposed framework in this work would facilitate public health actions guided by the SARS-CoV-2 concentrations in wastewater. In case of a new wave emergence or a new virus disease outbreak like COVID-19, the PNN powered by the NWSS would outline consolidated and systematic information that would enable rapid deployment of public health actions.
尽管与临床检测相比，基于废水的流行病学（WBE）已成为一种在社区层面追踪公共卫生情况的低成本且非侵入性的方法，但目前缺乏将废水中的新冠病毒浓度转化为新冠感染病例的结构化解释标准。困难在于临床研究中记录的受感染个体向废水中排出病毒量的不确定性。考虑到存在无症状感染人群以及许多其他混杂因素，这种情况更加严重。在本研究中，开发了一种现象学神经网络（PNN）的定量框架来计算无症状感染情况。该PNN使用了美国疾病控制与预防中心（CDC）于2020年启动的国家废水监测系统（NWSS）的WBE数据进行训练。研究发现，PNN具有出色的可解释性且减少了过拟合现象。与临床研究中关于受感染个体病毒排出情况的视角相比，从大数据角度观察受感染人群的病毒排出情况揭示了更具确定性的病毒排出动态。以这些特征作为估计无症状感染的理论基础，发现在研究期间，无症状感染与报告感染的比例全国中位数为5.7。该研究还指出了温度、排水区域人口和人均流量对无症状感染计算的影响。预计本研究中提出的框架将促进以废水中的新冠病毒浓度为指导的公共卫生行动。万一出现新一波疫情或像新冠疫情这样的新病毒疾病爆发，由NWSS支持的PNN将勾勒出全面且系统的信息，从而能够迅速部署公共卫生行动。
