Wong Tony E, Thurston George M, Barlow Nathaniel, Cahill Nathan D, Carichino Lucia, Maki Kara, Ross David, Schneider Jennifer
School of Mathematical Sciences, Rochester Institute of Technology, Rochester, NY, 14623, USA.
School of Physics and Astronomy, Rochester Institute of Technology, Rochester, NY, 14623, USA.
Infect Dis Model. 2021;6:1144-1158. doi: 10.1016/j.idm.2021.09.003. Epub 2021 Sep 21.
As college campuses reopened in fall 2020, we saw a large-scale experiment unfold on the efficacy of various strategies to contain the SARS-CoV-2 virus. Traditional individual surveillance testing via nasal swabs and/or saliva is among the measures that colleges are pursuing to reduce the spread of the virus on campus. Additionally, some colleges are testing wastewater on their campuses for signs of infection, which can provide an early warning signal for campuses to locate COVID-positive individuals. However, a representation of wastewater surveillance has not yet been incorporated into epidemiological models for college campuses, nor has the efficacy of wastewater screening been evaluated relative to traditional individual surveillance testing, within the structure of these models. Here, we implement a new model component for wastewater surveillance within an established epidemiological model for college campuses. We use a hypothetical residential university to evaluate the efficacy of wastewater surveillance for maintaining low infection rates. We find that wastewater sampling with a 1-day lag to initiate individual screening tests, plus completing the subsequent tests within a 4-day period can keep overall infections within 5% of the infection rates seen with traditional individual surveillance testing. Our results also indicate that wastewater surveillance can effectively reduce the number of false positive cases by identifying subpopulations for surveillance testing where infectious individuals are more likely to be found. Through a Monte Carlo risk analysis, we find that surveillance testing that relies solely on wastewater sampling can be fragile against scenarios with high viral reproductive numbers and high rates of infection of campus community members by outside sources. These results point to the practical importance of additional surveillance measures to limit the spread of the virus on campus and the necessity of a proactive response to the initial signs of outbreak.
随着大学校园在2020年秋季重新开放，我们目睹了一场关于控制SARS-CoV-2病毒的各种策略效果的大规模实验展开。通过鼻拭子和/或唾液进行的传统个体监测检测是高校为减少病毒在校园内传播而采取的措施之一。此外，一些高校正在检测校园废水中的感染迹象，这可以为校园定位新冠病毒阳性个体提供早期预警信号。然而，废水监测的代表性尚未纳入大学校园的流行病学模型，在这些模型的框架内，也未评估废水筛查相对于传统个体监测检测的效果。在此，我们在一个既定的大学校园流行病学模型中为废水监测实现了一个新的模型组件。我们使用一所假设的住宿制大学来评估废水监测对于维持低感染率的效果。我们发现，废水采样滞后1天以启动个体筛查检测，并在4天内完成后续检测，可以使总体感染率保持在传统个体监测检测所见感染率的5%以内。我们的结果还表明，废水监测可以通过识别更有可能发现感染个体的亚群体进行监测检测，从而有效减少假阳性病例的数量。通过蒙特卡洛风险分析，我们发现仅依赖废水采样的监测检测在面对病毒繁殖数高和校园社区成员受外部来源感染率高的情况时可能较为脆弱。这些结果指出了采取额外监测措施以限制病毒在校园内传播的实际重要性，以及对疫情初始迹象做出积极应对的必要性。
