Transmission risk of SARS-CoV-2 in the watershed triggered by domestic wastewater discharge

SARS-CoV-2 infection in animals: Patterns, transmission routes, and drivers

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is more widespread in animals than previously thought, and it may be able to infect a wider range of domestic and wild species. To effectively control the spread of the virus and protect animal health, it is crucial to understand the cross-species transmission mechanisms and risk factors of SARS-CoV-2. This article collects published literature on SARS-CoV-2 in animals and examines the distribution, transmission routes, biophysical, and anthropogenic drivers of infected animals. The reported cases of infection in animals are mainly concentrated in South America, North America, and Europe, and species affected include lions, white-tailed deer, pangolins, minks, and cats. Biophysical factors influencing infection of animals with SARS-CoV-2 include environmental determinants, high-risk landscapes, air quality, and susceptibility of different animal species, while anthropogenic factors comprise human behavior, intensive livestock farming, animal markets, and land management. Due to current research gaps and surveillance capacity shortcomings, future mitigation strategies need to be designed from a One Health perspective, with research focused on key regions with significant data gaps in Asia and Africa to understand the drivers, pathways, and spatiotemporal dynamics of interspecies transmission.

Identifying spatiotemporal trends of SARS-CoV-2 RNA in wastewater: from the perspective of upstream and downstream wastewater-based epidemiology (WBE)

Recently, many efforts have been made to address the rapid spread of newly identified COVID-19 virus variants. Wastewater-based epidemiology (WBE) is considered a potential early warning tool for identifying the rapid spread of this virus. This study investigated the occurrence of SARS-CoV-2 in eight wastewater treatment plants (WWTPs) and their sewerage systems which serve most of the population in Taoyuan City, Taiwan. Across the entire study period, the wastewater viral concentrations were correlated with the number of COVID-19 cases in each WWTP (Spearman's r = 0.23-0.76). In addition, it is confirmed that several treatment technologies could effectively eliminate the virus RNA from WWTP influent (> 90%). On the other hand, further results revealed that an inverse distance weighted (IDW) interpolation and hotspot model combined with the geographic information system (GIS) method could be applied to analyze the spatiotemporal variations of SARS-CoV-2 in wastewater from the sewer system. In addition, socio-economic factors, namely, population density, land use, and income tax were successfully identified as the potential drivers which substantially affected the onset of the COVID-19 outbreak in Taiwan. Finally, the data obtained from this study can provide a powerful tool in public health decision-making not only in response to the current epidemic situation but also to other epidemic issues in the future.

Detection of SARS-CoV-2 RNA throughout wastewater treatment plants and a modeling approach to understand COVID-19 infection dynamics in Winnipeg, Canada

Although numerous studies have detected SARS-CoV-2 RNA in wastewater and attempted to find correlations between the concentration of SARS-CoV-2 RNA and the number of cases, no consensus has been reached on sample collection and processing, and data analysis. Moreover, the fate of SARS-CoV-2 in wastewater treatment plants is another issue, specifically regarding the discharge of the virus into environmental settings and the water cycle. The current study monitored SARS-CoV-2 RNA in influent and effluent wastewater samples with three different concentration methods and sludge samples over six months (July to December 2020) to compare different virus concentration methods, assess the fate of SARS-CoV-2 RNA in wastewater treatment plants, and describe the potential relationship between SARS-CoV-2 RNA concentrations in influent and infection dynamics. Skimmed milk flocculation (SMF) resulted in 15.27 ± 3.32% recovery of an internal positive control, Armored RNA, and a high positivity rate of SARS-CoV-2 RNA in stored wastewater samples compared to ultrafiltration methods employing a prefiltration step to eliminate solids in fresh wastewater samples. Our results suggested that SARS-CoV-2 RNA may predominate in solids, and therefore, concentration methods focusing on both supernatant and solid fractions may result in better recovery. SARS-CoV-2 RNA was detected in influent and primary sludge samples but not in secondary and final effluent samples, indicating a significant reduction during primary and secondary treatments. SARS-CoV-2 RNA was first detected in influent on September 30th, 2020. A decay-rate formula was applied to estimate initial concentrations of late-processed samples with SMF. A model based on shedding rate and new cases was applied to estimate SARS-CoV-2 RNA concentrations and the number of active shedders. Inferred sensitivity of observed and modeled concentrations to the fluctuations in new cases and test-positivity rates indicated a potential contribution of newly infected individuals to SARS-CoV-2 RNA loads in wastewater.

Space-time analysis of COVID-19 cases and SARS-CoV-2 wastewater loading: A geodemographic perspective

Severe acute respiratory syndrome - coronavirus 2 (SARS-CoV-2) continues to effect communities across the world. One way to combat these effects is to enhance our collective ability to remotely monitor community spread. Monitoring SARS-CoV-2 in wastewater is one approach that enables researchers to estimate the total number of infected people in a region; however, estimates are often made at the sewershed level which may mask the geographic nuance required for targeted interdiction efforts. In this work, we utilize an apportioning method to compare the spatial and temporal trends of daily case count with the temporal pattern of viral load in the wastewater at smaller units of analysis within Austin, TX. We find different lag-times between wastewater loading and case reports. Daily case reports for some locations follow the temporal trend of viral load more closely than others. These findings are then compared to socio-demographic characteristics across the study area.