Chen William, Bibby Kyle
Department of Civil & Environmental Engineering & Earth Sciences, University of Notre Dame, Notre Dame, IN 46556, United States of America.
Department of Civil & Environmental Engineering & Earth Sciences, University of Notre Dame, Notre Dame, IN 46556, United States of America.
Sci Total Environ. 2025 Jan 10;959:178141. doi: 10.1016/j.scitotenv.2024.178141. Epub 2024 Dec 21.
Measles is a highly transmissible disease of increasing concern due to waning vaccination contributing to a significant rise in measles cases, with 283 reported cases and 16 outbreaks in the U.S. as of November 7, 2024. Early identification of measles cases is thus critical to disease containment and control. Wastewater-based epidemiology (WBE) represents a potential strategy for the efficient identification of measles outbreaks. We investigated the suitability of WBE for measles outbreak identification by using a model-based approach to elucidate the relationship between measles shedding, wastewater concentration, and detectability. The model reveals conditions for effective detection, specifying the optimal timing, location, and methodology needed to achieve a specific probability of detection, including accounting for geographic variability of wastewater generation and measles case rates. Measles RNA shedding, primarily from urine, contributes an average of 8.72 log10 genome copies (GC) daily per infection into sewage. At the average U.S. wastewater treatment plant (WWTP), achieving a 50 % probability of detection requires approximately 78 cases per 100,000 people with a process limit of detection (PLOD) of 3.0 log10 GC/L. At a PLOD of 3.0 log10 GC/L, over half of all WWTPs in the world can detect a single hypothetical measles case at a 10 % probability of detection. However, achieving a 50-90 % detection rate is challenging, especially with a higher PLOD, except in areas with the highest measles cases. Some locations require case levels consistent with a complete lack of vaccination for feasible measles detection in wastewater. Future work exploring measles shedding, variable shedding behavior, and local case rates can enhance model predictions. Overall, this analysis suggests that WBE detection of measles in most locations remains challenging without a significant increase in case rates or technical improvements decreasing the PLOD.
麻疹是一种传染性很强的疾病，由于疫苗接种率下降导致麻疹病例大幅增加，这一问题日益受到关注。截至2024年11月7日，美国报告了283例病例和16起疫情。因此，早期识别麻疹病例对于疾病的遏制和控制至关重要。基于废水的流行病学（WBE）是一种有效识别麻疹疫情的潜在策略。我们采用基于模型的方法来阐明麻疹病毒排放、废水浓度和可检测性之间的关系，以此研究WBE在麻疹疫情识别中的适用性。该模型揭示了有效检测的条件，明确了实现特定检测概率所需的最佳时间、地点和方法，包括考虑废水产生和麻疹病例率的地理变异性。麻疹RNA主要通过尿液排出，每次感染平均每天向污水中排放8.72 log10基因组拷贝（GC）。在美国的平均污水处理厂（WWTP），要达到50%的检测概率，每10万人中大约需要78例病例，检测过程的最低检测限（PLOD）为3.0 log10 GC/L。在PLOD为3.0 log10 GC/L时，世界上超过一半的污水处理厂能够以10%的检测概率检测到单个假设的麻疹病例。然而，要实现50%-90%的检测率具有挑战性，特别是在PLOD较高的情况下，除了麻疹病例最多的地区。一些地点需要与完全未接种疫苗一致的病例水平，才能在废水中实现可行的麻疹检测。未来探索麻疹病毒排放、可变排放行为和当地病例率的工作可以提高模型预测能力。总体而言，该分析表明，在大多数地区，若病例率没有显著增加或技术改进没有降低PLOD，WBE检测麻疹仍然具有挑战性。
