Dataset of SARS-CoV-2 wastewater data from Uppsala, and neighbouring towns Knivsta, Enköping, Östhammar and Älvkarleby, Sweden

Székely, Anna;Mohamed, Nahla

Description

This is a metadata record for a continuously updated dataset of SARS-CoV-2 RNA data in wastewater in Uppsala and neighbouring towns Knivsta, Enköping, Östhammar and Älvkarleby.
The dataset is part of a research study led associate professor Anna J. Székely (SLU, Swedish University of Agricultural Sciences) and her research groups in collaboration with Uppsala Vatten. The research group is part of the Environmental Virus Profiling Research Area of the SciLifeLab National COVID-19 Research Program.
The viral content is concentrated according to the protocol from Ahmed et al. https://doi.org/10.1016/j.scitotenv.2020.139960according to the modified electronegative filtration method. Viral RNA is extracted and SARS-CoV-2 gene copy nr quantified using R-qPCR. Recovery efficiency is measured using Bovine coronavirus (BCoV) as a virus surrogate. Pepper mild mottle virus (PPMoV) is used to correct for variations in water size and flow. For more information see https://www.covid19dataportal.se/data_types/environment/wastewater/#uppsala

The dataset for Uppsala started week 38 and updates weekly. Knivsta started week 12, Enköping week 13 and Östhammar and Älvkarleby week 14.
The dataset is available as part of the Environmental Virus Profiling data section. The amount of SARS-CoV-2 virus in wastewater across Sweden" https://www.covid19dataportal.se/data_types/environment/wastewater/#uppsala on the Swedish COVID-19 Data Portal (https://covid19dataportal.se). The dataset is also available as part of the CRUSH Covid project https://crush-covid.shinyapps.io/crush_covid/
Funded by SciLifeLab/KAW National COVID-19 Research Program https://www.scilifelab.se/covid-19/national-program/

Citations (0)

Mentions (0)

Metrics

Dataset Index

0.7

FAIR Score

85%

Citations

0

Mentions

0

Metrics Over Time

Publication Details

DOI

Publisher

Swedish University of Agricultural Sciences

License

Creative Commons Attribution 4.0 International

Assigned Domain

Subfield

Infectious Diseases

Field

Medicine

Domain

Health Sciences

Confidence Score

61%

Source

Scholar Data Model

Keywords

Infectious diseases

Normalization Factors

FT

42.31

CTw

1.00

MTw

1.00