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[Turing Data Story] "Who's protected by the Covid19 lockdown?" #78
The name of the story is: "Who's protected by the Covid19 lockdown?"
Please provide a high level description of the Turing Data Story
We are concerned that the impact of COVID-19 has disproportionately affected certain groups of people. In particular, that the lockdown measure may have had a worse impact for those in the most deprived areas, whose livelihoods may have required them to leave the house more frequently.
Our analysis will involve exploring the relationship between the following key metrics:
COVID-19 mortality rates over time and across geographical regions.
Index of multiple deprivation (IMD) by geographical region - a measure of the geographic spread of social deprivation (see definition and explanation).
Which datasets will you be using in this Turing Data Story?
Earlier in June, the Office of National Statistics (ONS) published a report exploring if those living in the most deprived areas of the UK were disproportionately affected by COVID-19. The report seems to confirm our fear - between the months of March to May 2020 those in the most deprived areas of the UK were more than twice as likely to die as a result of COVID-19 than those in the least deprived areas.
There are two caveats that we have with the ONS analysis. The first is reproducibility. We want to confirm the ONS results by making analysis procedure open. The second caveat is that the ONS report aggregates data over time, and therefore that it might miss interesting differences in outcomes between the different stages of lockdown. Between March and May represents the time when the lockdown was most severe, with measures relaxing from June onwards. We wonder whether the ONS analysis will continue to be relevant as lockdown eases. For this purpose, we wish to extend the ONS analysis to cover all available data, and at the same time, make a comparison between the different stages of lockdown.
Current status
This chapter is empty. If anyone would like to make a start they are more than welcome to do so.
[x] Write story outline
[x] Add material to the story notebook
[x] Combine materials into a readable story
[x] Proofread
[x] PR submitted
[x] Request reviews
[x] Address reviews
[x] Merge to master branch.
Updates
The story is developed in branch 'story/1' in the notebook here.
Story description
The name of the story is: "Who's protected by the Covid19 lockdown?"
Please provide a high level description of the Turing Data Story
We are concerned that the impact of COVID-19 has disproportionately affected certain groups of people. In particular, that the lockdown measure may have had a worse impact for those in the most deprived areas, whose livelihoods may have required them to leave the house more frequently.
Our analysis will involve exploring the relationship between the following key metrics:
Which datasets will you be using in this Turing Data Story?
We will use the following datasets:
Additional context
Earlier in June, the Office of National Statistics (ONS) published a report exploring if those living in the most deprived areas of the UK were disproportionately affected by COVID-19. The report seems to confirm our fear - between the months of March to May 2020 those in the most deprived areas of the UK were more than twice as likely to die as a result of COVID-19 than those in the least deprived areas.
There are two caveats that we have with the ONS analysis. The first is reproducibility. We want to confirm the ONS results by making analysis procedure open. The second caveat is that the ONS report aggregates data over time, and therefore that it might miss interesting differences in outcomes between the different stages of lockdown. Between March and May represents the time when the lockdown was most severe, with measures relaxing from June onwards. We wonder whether the ONS analysis will continue to be relevant as lockdown eases. For this purpose, we wish to extend the ONS analysis to cover all available data, and at the same time, make a comparison between the different stages of lockdown.
Current status
This chapter is empty. If anyone would like to make a start they are more than welcome to do so.
Updates
The story is developed in branch 'story/1' in the notebook here.