New: COVID19 Geographic Risk Index (UK)
Now more than ever it’s important for business, government, and society to understand the risks of COVID19 to the UK population and economy and coronavirus affected areas. Having insights to those risks to make sure we doing all we can to stay safe now, and to thrive in the future is so important we’ve decided to produce a fresh and up to date dataset to help fast track insights and monitor coronavirus updates.
What’s more… we’re making a free version available to individuals who can lend expert analysis into the Coronavirus protection and recovery conversation. To support the wider analytical community investigating COVID-19, we are making our datasets at Ward, Parliamentary Constituency and CCG level freely available under a Non-Commercial Licence.
It’s our hope that our commercial datasets combined with the new COVID risk data will provide a range of useful insights at a local neighbourhood and national level that are associated with an increased risk of being infected by COVID-19 or affected by the actions taken to mitigate its spread.
Get the latest Data for coronavirus in the UK
- Individuals can access the COVID Specific data for free
- Commercial and governmental bodies requiring the full dataset and joined data
Content of the Dataset
Risk factors by Ward, Parliamentary Constituency and CQC
Doorda is making available a COVID-19 dataset that provides estimates of risk factors and COVID-19 infection rates at a range of local geographies (Ward, Parliamentary Constituency and Clinical Commission Group) and segmented by several other risk factors including:
- Age and Household
- Mortality and Co-morbidity
- Economic Resilience
- Engagement with advice and Information
- COVID-19 Infection Rate
- Associated Polygon boundaries for faster insights, these include:
- Parliamentary Constituencies
- Electoral Wards
- Health Areas
- Plus nine other measures considered to be relevant to COVID-19.
Risks Linked to other Stats
You can use this risk data in tandem with our massive and highly interlinked data products: Biz, Stats, Property, Spatial, and Procurement, allowing you to see the relationships between, and get insights on COVID19 risk and any area of UK business, population, geography, government and even details such as regulation and procurement.
Example: if you wanted to carry out a predictive economic impact assessment by parliamentary constituency we have current and predicted data you’re are going to need.
Who should use this data?
We hope by making this data available we can provide the tools for companies, government bodies, and other sectors to plan for and mitigate the impact of COVID19 in specific geographic areas of the UK. This data will undoubtedly be useful for:
- Charities and NGOs
- Data Scientists and Statisticians
- Think Tanks and Strategic Planners
- Data Journalists
- Data Analysts
It’s been said before “COVID19 does not discriminate”. It’s affecting every aspect of society, government, business and academia, so if you’re trying to work out an exit strategy, or a way to get ahead of the virus impact, then having a clear understanding of how geographic location affects risk is essential.
The unique dataset we’re making available is a multi-dimensional dataset that ranks all Wards across the UK by risk. Separate rankings are also being made available tagged by Clinical Commissioning Groups (CCG) and Parliamentary Constituencies and are supplied in a single Excel Workbook. As part of this we include “as is” data, and data on a “timeline adjusted” basis across the whole of the UK.
Is This Risk Data Predictive?
The comparison of risk factors and infection rates at a local level suggests that there are plausible associations between the two. The high infection rates seen to date are dominated by London and are occurring in locations with higher overcrowding risks, and higher overall engagement risk (indicating adherence to the lockdown is much more difficult in these locations).
The time-adjusted pattern for the UK as a whole, shifts the risk profile to areas with poorer health and lower incomes, but with a hot spot for Coronavirus outbreaks still clearly associated with overcrowding that is consistent with the “as is” profile.
A further comparison of modelled infection rates to the Output Area Classifications provided by ONS show that categories in supergroups 3: Ethnicity Central, 4: Multicultural metropolitans and 7: Constrained city dwellers, are over-indexed either on the “as is” or timeline adjusted models. Sub-groups are generally over-indexed compared to their parent Super Group where they have residents who live in more overcrowded conditions and / or use public transport more and / or have a higher proportion of workers in industries engaging with the general public (e.g. accommodation, food service).
To find out more get in touch with Doorda for a free consultation and discussion of the data.