Yang Wan, Omoregie Enoma, Olsen Aaron, Watts Elizabeth A, Parton Hilary, Lee Ellen
Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA.
New York City Department of Health and Mental Hygiene, Queens, NY, USA.
BMC Public Health. 2025 Mar 24;25(1):1108. doi: 10.1186/s12889-025-22306-1.
Wastewater-based surveillance is an important tool for monitoring the COVID-19 pandemic. However, it remains challenging to translate wastewater SARS-CoV-2 viral load to infection number, due to unclear shedding patterns in wastewater and potential differences between variants.
We utilized comprehensive wastewater surveillance data and estimates of infection prevalence (i.e., the source of the viral shedding) available for New York City (NYC) to characterize SARS-CoV-2 fecal shedding pattern over multiple COVID-19 waves.
We collected SARS-CoV-2 viral wastewater measurements in NYC during August 31, 2020 - August 29, 2023 (N = 3794 samples). Combining with estimates of infection prevalence (number of infectious individuals including those not detected as cases), we estimated the time-lag, duration, and per-infection fecal shedding rate for the ancestral/Iota, Delta, and Omicron variants, separately. We also developed a procedure to identify occasions with intensified transmission.
Models suggested fecal viral shedding likely starts around the same time as and lasts slightly longer than respiratory tract shedding. Estimated fecal viral shedding rate was highest during the ancestral/Iota variant wave, at 1.44 (95% CI: 1.35 - 1.53) billion RNA copies in wastewater per day per infection (measured by RT-qPCR), and decreased by around 20% and 50-60% during the Delta wave and Omicron period, respectively. We identified around 200 occasions during which the wastewater SARS-CoV-2 viral load exceeded the expected level in any of the city's 14 sewersheds. These anomalies disproportionally occurred during late January, late April-early May, early August, and from late-November to late-December, with frequencies exceeding the expectation assuming random occurrence (P < 0.05; bootstrapping test).
These estimates may be useful in understanding changes in underlying infection rate and help quantify changes in COVID-19 transmission and severity over time. We have also demonstrated that wastewater surveillance data can support the identification of time periods with potentially intensified transmission.
基于废水的监测是监测新冠疫情的一项重要工具。然而,由于废水中新冠病毒的排放模式尚不清楚以及不同变体之间存在潜在差异,将废水样本中的新冠病毒载量转化为感染人数仍然具有挑战性。
我们利用纽约市全面的废水监测数据以及感染流行率估计值(即病毒排放源),来描述新冠疫情多波次期间新冠病毒的粪便排放模式。
我们收集了2020年8月31日至2023年8月29日期间纽约市的新冠病毒废水测量数据(N = 3794个样本)。结合感染流行率估计值(包括未被检测为病例的感染者数量),我们分别估算了原始毒株/Iota变体、德尔塔变体和奥密克戎变体的时间滞后、持续时间和每次感染的粪便排放率。我们还开发了一种程序来识别传播加剧的时期。
模型表明,粪便病毒排放可能与呼吸道病毒排放在同一时间开始,且持续时间略长。估计粪便病毒排放率在原始毒株/Iota变体流行期间最高,为每次感染每天每升废水14.4亿个RNA拷贝(通过逆转录定量聚合酶链反应测量),在德尔塔变体流行期间和奥密克戎变体流行期间分别下降了约20%和50%-60%。我们确定了大约200个时期,在此期间,纽约市14个排水区域中任何一个区域的废水新冠病毒载量都超过了预期水平。这些异常情况不成比例地发生在1月下旬、4月下旬至5月初、8月初以及11月下旬至12月下旬,其发生频率超过了随机发生的预期(P < 0.05;自抽样检验)。
这些估计值可能有助于理解潜在感染率的变化,并有助于量化新冠疫情随时间推移的传播和严重程度变化。我们还证明了废水监测数据可以支持识别潜在传播加剧的时期。