TuringDataStories: An open community creating “Data Stories”: A mix of open data, code, narrative 💬, visuals 📊📈 and knowledge 🧠 to help understand the world around us.
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[Turing Data Story] Using wastewater data to monitor SARS-CoV-2 in England #193
Please provide a high level description of the Turing Data Story
Wastewater has been successfully used for health surveillance in a number of contexts such as tracking drug use or monitoring disease outbreaks (e.g., polio). During the Covid-19 pandemic, over 50 countries have set up programmes to test wastewater data for presence of the SARS-CoV-2 virus in the hope of using it as an unbiased, fairly cheap and fast Covid-19 surveillance tool to complement (replace?) other data sources (e.g., community testing, randomised surveys, routine data such as hospitalisations and deaths). The cons of wastewater data is that it sensitive to variables such as weather and the exact relationship between SARS-COV-2 viral load in wastewater and Covid-19 prevalence is unclear.
This TDS has come out of a research project that a few REG members have worked on (scientific article in preparation). The idea here is to given an introduction to the area of wastewater based epidemiology and show the workflow for manipulating spatio-temporal data but without getting into the really complex modelling the original research project undertook. We might for example explore here population coverage of the wastewater sites and how the wastewater data correlates with other variables such as rainfall.
The plan is to create this TDS in R to have an example in a programming language other than Python.
Which datasets will you be using in this Turing Data Story?
In England, wastewater has been sampled 4 times a week across a number of sites and this data is released publicly.
Story description
Please provide a high level description of the Turing Data Story
Wastewater has been successfully used for health surveillance in a number of contexts such as tracking drug use or monitoring disease outbreaks (e.g., polio). During the Covid-19 pandemic, over 50 countries have set up programmes to test wastewater data for presence of the SARS-CoV-2 virus in the hope of using it as an unbiased, fairly cheap and fast Covid-19 surveillance tool to complement (replace?) other data sources (e.g., community testing, randomised surveys, routine data such as hospitalisations and deaths). The cons of wastewater data is that it sensitive to variables such as weather and the exact relationship between SARS-COV-2 viral load in wastewater and Covid-19 prevalence is unclear.
This TDS has come out of a research project that a few REG members have worked on (scientific article in preparation). The idea here is to given an introduction to the area of wastewater based epidemiology and show the workflow for manipulating spatio-temporal data but without getting into the really complex modelling the original research project undertook. We might for example explore here population coverage of the wastewater sites and how the wastewater data correlates with other variables such as rainfall.
The plan is to create this TDS in R to have an example in a programming language other than Python.
Which datasets will you be using in this Turing Data Story?
In England, wastewater has been sampled 4 times a week across a number of sites and this data is released publicly.
We also have data on the wastewater catchment sites
We might also use data from ONS on population statistics:
and weather (need to look into data sources for this).
Additional context
Ethical guideline
Ideally a Turing Data Story has these properties and follows the 5 safes framework.
Current status
Updates