Rapid and extensive SARS-CoV-2 Omicron variant infection wave revealed by wastewater surveillance in Shenzhen following the lifting of a strict COVID-19 strategy.

Wastewater-based epidemiology (WBE) has emerged as a promising tool for monitoring the spread of COVID-19, as SARS-CoV-2 can be shed in the faeces of infected individuals, even in the absence of symptoms. This study aimed to optimize a prediction model for estimating COVID-19 infection rates based on SARS-CoV-2 RNA concentrations in wastewater, and reveal the infection trends and variant diversification in Shenzhen, China following the lifting of a strict COVID-19 strategy. Faecal samples (n = 4337) from 1204 SARS-CoV-2 infected individuals hospitalized in a designated hospital were analysed to obtain Omicron variant-specific faecal shedding dynamics. Wastewater samples from 6 wastewater treatment plants (WWTPs) and 9 pump stations, covering 3.55 million people, were monitored for SARS-CoV-2 RNA concentrations and variant abundance. We found that the viral load in wastewater increased rapidly in December 2022 in the two districts, demonstrating a sharp peak in COVID-19 infections in late-December 2022, mainly caused by Omicron subvariants BA.5.2.48 and BF.7.14. The prediction model, based on the mass balance between total viral load in wastewater and individual faecal viral shedding, revealed a surge in the cumulative infection rate from <0.1 % to over 70 % within three weeks after the strict COVID-19 strategy was lifted. Additionally, 39 cryptic SARS-CoV-2 variants were identified in wastewater, in addition to those detected through clinical surveillance. These findings demonstrate the effectiveness of WBE in providing comprehensive and efficient assessments of COVID-19 infection rates and identifying cryptic variants, highlighting its potential for monitoring emerging pathogens with faecal shedding.

Li Y ，Du C ，Lv Z ，Wang F ，Zhou L ，Peng Y ，Li W ，Fu Y ，Song J ，Jia C ，Zhang X ，Liu M ，Wang Z ，Liu B ，Yan S ，Yang Y ，Li X ，Zhang Y ，Yuan J ，Xu S ，Chen M ，Shi X ，Peng B ，Chen Q ，Qiu Y ，Wu S ，Jiang M ，Chen M ，Tang J ，Wang L ，Hu L ，Wei B ，Xia Y ，Ji JS ，Wan C ，Lu H ，Zhang T ，Zou X ，Fu S ，Hu Q ... -  《-》

Longitudinal wastewater surveillance of four key pathogens during an unprecedented large-scale COVID-19 outbreak in China facilitated a novel strategy for addressing public health priorities-A proof of concept study.

Fu S ，Zhang Y ，Wang R ，Deng Z ，He F ，Jiang X ，Shen L ... -  《-》

[The cases of tracing the source of patients infected with Omicron variant of SARS-CoV-2 based on wastewater-based epidemiology in Shenzhen].

Peng YJ ，Li YH ，Du C ，Guo YS ，Song JT ，Jia CY ，Zhang X ，Liu MJ ，Wang ZM ，Liu B ，Yan SL ，Yang YX ，Tang XL ，Lin GX ，Li XY ，Zhang Y ，Yuan JH ，Xu SK ，Chen CD ，Lu JH ，Zou X ，Wan CS ，Hu QH ... -  《-》

Effect of SARS-CoV-2 shedding rate distribution of individuals during their disease days on the estimation of the number of infected people. Application of wastewater-based epidemiology to the city of Thessaloniki, Greece.

During the COVID-19 pandemic, wastewater-based epidemiology has proved to be an important tool for monitoring the spread of a disease in a population. Indeed, wastewater surveillance was successfully used as a complementary approach to support public health monitoring schemes and decision-making policies. An essential feature for the estimation of a disease transmission using wastewater data is the distribution of viral shedding rate of individuals in their personal human wastes as a function of the days of their infection. Several candidate shapes for this function have been proposed in literature for SARS-CoV-2. The purpose of the present work is to explore the proposed function shapes and examine their significance on analyzing wastewater SARS-CoV-2 shedding rate data. For this purpose, a simple model is employed applying to medical surveillance and wastewater data of the city of Thessaloniki during a period of Omicron variant domination in 2022. The distribution shapes are normalized with respect to the total virus shedding and then their basic features are investigated. Detailed analysis reveals that the main parameter determining the results of the model is the difference between the day of maximum shedding rate and the day of infection reporting. Since the latter is not part of the distribution shape, the major feature of the distribution affecting the estimation of the number of infected people is the day of maximum shedding rate with respect to the initial infection day. On the contrary, the duration of shedding (total number of disease days) as well as the exact shape of the distribution are by far less important. The incorporation of such wastewater surveillance models in conventional epidemiological models - based on recorded disease transmission data- may improve predictions for disease spread during outbreaks.

Kostoglou M ，Petala M ，Karapantsios T ，Dovas C ，Tsiridis V ，Roilides E ，Koutsolioutsou-Benaki A ，Paraskevis D ，Metalidis S ，Stylianidis E ，Papa A ，Papadopoulos A ，Tsiodras S ，Papaioannou N ... -  《-》

Wastewater Genomic Surveillance Captures Early Detection of Omicron in Utah.

Gupta P ，Liao S ，Ezekiel M ，Novak N ，Rossi A ，LaCross N ，Oakeson K ，Rohrwasser A ... -  《Microbiology Spectrum》