W-SPHERE (Wastewater - SARS Public Health Environmental Response) as Global Data Center for SARS-CoV-2 detection in wastewater and environmental samples

W-SPHERE (Wastewater - SARS Public Health Environmental Response) as Global Data Center for SARS-CoV-2 detection in wastewater and environmental samples Andri T Rachmadi*, MSU, Postdoc, rachmad1@msu.edu. Collleen Naughton, UC Merceed, Asst Professor. Gertjan Medema, KWR, Lead researcher. Panagis Katsivelis, Ventic IT, Lead technician, and Joan B. Rose, MSU, Professor. Over a year since the declaration of the global coronavirus disease 2019 (COVID-19) pandemic there have been over 219 million cases and 4.55 million deaths recorded. Wastewater-based epidemiology (WBE) quickly adapted their existing methods to detect SARS-CoV-2 RNA in wastewater to track community spread of the virus. Wastewater surveillance has the potential to provide a real-time view of trends in community infection while public health struggles to monitor individuals and obtain good data to support testing, interventions (closures, masks, social distancing) and economic reopening policy decisions. The wastewater surveillance can also support vaccination implementation and tracking of variants of concern. Unlike COVID-19 case and mortality data, there was not a global dashboard to track wastewater monitoring of SARS-CoV-2 RNA worldwide. Currently, over 2600+ sites including cities and counties (regional systems) across the world are monitoring wastewater for SARS-CoV-2 or have communicated that they will be setting up for monitoring soon. Using COVIDPoops19 as a starting point the W-SPHERE datasets, along with their metadata and dictionaries, will be open for access and sharing across the globe in a number of reusable formats. The mission of W-SPHERE is to advance environmental surveillance of sewage to inform local and global efforts for monitoring and supporting public health measures to combat COVID-19. We use ArcGIS online dashboards for data visualization, case studies, and employ geospatial and statistical tools for data normalization standardization, comparison, and analysis. Methods for the W-SPHERE online dashboard included direct data extraction from available online dashboard worldwide, data from individual or team contributors, and input from SARS-CoV-2 environmental surveillance working groups from low and middle income countries. All the input parameters were uploaded into our standardized data form. The data form contains dashboard meta data, site detailed information, laboratory methods for SARS-CoV-2 detection, and sample details such as gene targets measured, collection method, SARS-CoV-2 concentrations, presence absence, limit of detection, and concentration unit. On the W-SPHERE dashboard, the user will have the option to select public health use cases based on specific characteristics such as the system setting (e.g. sewered, non-sewered system) or use case type (e.g. city level, national level, buildings, hospitals, etc.). The story map will provide a closer look at the different steps (e.g. design, monitoring, and execution) for developing a program that used the wastewater data for public health decision making. After a year of data tracking, wastewater surveillance for SARS-CoV-2 is conducted in over 53 countries, 2,082 sites, with 248 universities/institutions involved. A small subset (75) of those monitoring for SARS-CoV-2 in wastewater provide their data publicly and only thirteen provide limited downloadable data. Of the 53 that are conducting wastewater monitoring: 35 (65%) are in high-income countries, 11 (20%) are upper middle income, 8 (15%) are lower middle income, and 0% are low-income countries. Currently, W-SPHERE has 13 datasets available from 8 different countries consist of 528 sites and more than 20,000 samples. More datasets are being added weekly. Sites included sewage from wastewater treatment facility, sewer shed from building (eg. social service, correctional facilities) and environmental samples (impacted rivers). Of the13 datasets, 12 reported the detection SARS-CoV-2 using N1, N2 genes or both and only one dataset used the E gene (US-Honolulu). In the standardized datasets, information such as presence absence results and population served by the wastewater treatment are included. In addition, SARS-CoV-2 concentration trends (increase, slight increase, decrease, or no change) from a selectable time period are available. The trend was calculated by using linear regression with the five most recent measurements. As an open access dashboard, all the data are available for to download in csv format. As examples of the datasets from May to July 2021, 3 correctional facilities in New Mexico showed an increase trend of SARS-CoV-2 concentrations in their sewage, which was also observed at three Switzerland WWTPs which served more than 50 thousand populations and Ohio WWTPs which served more than 300 thousand people. There was a statistical difference between SARS-CoV-2 concentrations among the three datasets however representing the different populations served and likely number of infections in the last 6 months of sampling (January-June 2021), Kruskal-Wallis test (P<0.001, =0.05). Compelling case studies of wastewater surveillance for SARS-CoV-2 and uses in public health have been included. The Catalan surveillance network of SARS-CoV-2 is monitoring SARS-CoV-2 weekly from 56 WWTPs and used the information for i. estimated circulation of SARS-CoV-2 in communities where clinical testing is far from optimal or less available, ii. assessment of the efficacy of containment measures implemented by Health authorities in different areas of the Catalan territory and iii. identification of potential outbreaks in monitored municipalities (forecasting of clinical cases, i.e., early-warning). Another example from the Dutch demonstrated that the trends of the virus served an indicator of undertesting in city areas and early warning and localization of resurgence. Wastewater surveillance has been a powerful tool for providing information that helped to build resilience to the COVID-19 pandemic. However, there is a severe lack of data in low-income countries, limited data sharing publicly and challenges in analysis of the data to communicate to public health officials for decision making.