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medrxiv; 2021.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2021.11.30.21266952

ABSTRACT

The use of RNA sequencing from wastewater samples is proven to be a valuable way for estimating infection dynamics and circulating lineages of SARS-CoV-2. This approach has the advantage of being independent from patient population testing and symptomatic disease courses. However, it is equally important to develop easily accessible and scalable tools which can highlight critical changes in infection rates and dynamics over time across different locations given the sequencing data from the wastewater. Here we provide the first analysis of variant dynamics in Germany using wastewater sequencing and present PiGx SARS-CoV-2, a bit-by-bit reproducible end-to-end pipeline with comprehensive reports. To our knowledge, this is the first pipeline that includes all steps from raw-data to shareable reports, additional taxonomic analysis, deconvolution and geospatial time series analysis. Using our pipeline on a dataset of wastewater samples, from different locations across Berlin, over the time period from February 2021 to June 2021, we could reconstruct the dynamic of the Variant of Concern (VoC) B.1.1.7 (alpha). Additionally, we detected the unique signature mutation M:T26767C for the VoC B.1.617.2 (delta) and its raise in early June. We also show that SARS-CoV-2 mutation load measured from wastewater sequencing is correlated with actual case numbers and it has potential to be used in a predictive manner. All in all, our study provides additional evidence that systematic wastewater analysis using sequencing and computational methods can be used for modeling the infection dynamics of SARS-CoV-2. In addition, the results show that our tool can be used to tease out new mutations and to detect any emerging new lineages of concern before clinical detection. Our approach can support efforts for establishing continuous monitoring and early-warning projects for COVID-19 or any other infectious disease.


Subject(s)
Communicable Diseases , COVID-19
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