Bioinformatics Framework for Wastewater-based Surveillance of Infectious Diseases

Background: The purpose of this study was to implement and evaluate a near real-time wastewater-based epidemiology (WBE) framework for SARS-CoV-2 surveillance across the United States. COVID-19 was projected to become one of the largest mass casualty events in U.S. history, highlighting the need for accurate population-level assessment to guide prevention and mitigation strategies. Traditional diagnostic testing through swabs, saliva, or serum had limited reach, while WBE provided a cost-effective alternative capable of screening up to 70% of the U.S. population weekly at less than 0.01% of the cost of individual clinical testing. This study aimed to complement existing surveillance methods, assess the true disease burden, identify geographic disparities, and provide timely data to inform public health decision-making.

Materials/Methods: The study developed a WBE bioinformatics framework for SARS-CoV-2 at national, city, and neighborhood (intra-sewershed) levels to generate RT-qPCR and RNA sequencing data that tracked viral distribution and genetic variations in wastewater samples. Weekly wastewater samples were analyzed to estimate community viral loads, which were then compared with reported infection, hospitalization, and mortality data from local health systems. The team also optimized pipelines for high throughput sequencing analysis to detect SARS-CoV-2 variants and implemented a robust data communication platform, expanding their existing public online dashboard in collaboration with the City of Tempe, Arizona.

Outcome/Impact: The study successfully established a scalable, real-time wastewater surveillance system capable of detecting and tracking SARS-CoV-2 across multiple geographic levels. The WBE framework increased testing coverage, enhanced temporal and spatial resolution of community surveillance, and provided an early warning system for identifying outbreaks before clinical cases surged. The approach enabled continuous variant monitoring, improved public health responsiveness, and demonstrated the utility of wastewater surveillance as a sustainable tool for managing current and future infectious disease outbreaks. Sequencing data from this study are available in the Sequence Read Archive (SRA) repository and can be accessed via https://www.ncbi.nlm.nih.gov/bioproject/847239 https://www.ncbi.nlm.nih.gov/bioproject/847239 and https://www.ncbi.nlm.nih.gov/bioproject/662596.

背景：本研究旨在搭建并评估一套用于美国全境范围内严重急性呼吸综合征冠状病毒2型（SARS-CoV-2）监测的近实时废水流行病学（wastewater-based epidemiology, WBE）框架。新冠疫情曾被预估为美国历史上规模最大的大规模伤亡事件之一，凸显了开展精准人群水平评估以指导防控策略制定的迫切需求。传统的拭子、唾液或血清诊断检测覆盖范围有限，而WBE则提供了一种低成本替代方案：每周可筛查高达70%的美国人群，其成本仅为单人临床检测的0.01%以下。本研究旨在补充现有监测手段，评估真实疾病负担，识别地域差异，并提供及时数据以支撑公共卫生决策。

材料与方法：本研究搭建了适用于国家、城市以及社区（下水道流域内）层面的SARS-CoV-2 WBE生物信息学框架，以生成实时荧光定量聚合酶链式反应（RT-qPCR）和RNA测序数据，追踪废水样本中的病毒分布与遗传变异。研究人员每周分析废水样本以估算社区病毒载量，随后将其与当地卫生系统报告的感染、住院及死亡数据进行比对。团队还优化了高通量测序分析流程以检测SARS-CoV-2变异株，并搭建了可靠的数据通信平台，与亚利桑那州坦佩市合作扩展了现有公共在线仪表盘。

结果与影响：本研究成功搭建了可扩展的近实时废水监测系统，能够在多个地理层面检测并追踪SARS-CoV-2。该WBE框架提升了检测覆盖范围，增强了社区监测的时空分辨率，并可在临床病例激增前为疫情识别提供预警。该方法实现了对病毒变异株的持续监测，提升了公共卫生响应能力，并证实了废水监测作为可持续工具可用于应对当前及未来的传染病疫情。本研究产生的测序数据已上传至序列读取档案库（Sequence Read Archive, SRA），可通过以下链接获取：https://www.ncbi.nlm.nih.gov/bioproject/847239、https://www.ncbi.nlm.nih.gov/bioproject/847239 及 https://www.ncbi.nlm.nih.gov/bioproject/662596。