COVID-19 data

Image of the internal structure of the lungs
‘Internal structure of the lungs: 3D printed plastic’ by Dave Farnham, CC BY 4.0

Health and wellbeing data during the COVID-19 pandemic

The COVID-19 pandemic has turned public attention to the crucial role played by data in enabling us to understand rapid developments worldwide and to take appropriate measures. However, the crisis has also highlighted that these data do not speak for themselves; their collection, use and presentation to the public are complicated. 

 

Calculating COVID-19 mortality rates

In the UK, the daily announcements in the first weeks of the pandemic only included deaths in hospital of those who tested positive for COVID-19. Even then, there was generally a delay of a few days in hospital reports. For example, while on 27 March the government announced that 926 COVID-19 deaths had so far taken place in English hospitals, NHS England now reports that the true figure on that date was 1,649.[1]

A more reliable number is the one collated from death certificates issued by local authorities, by the Office for National Statistics. However, it can take up to thirteen days for deaths to be reported after a person passes away, and, in the absence of systematic testing, it is likely that some deaths caused by COVID-19 may not have been registered accurately.

A more accurate number still – but suffering even further delays – could be arrived at by looking at the excess number of deaths compared to a similar period in previous years. This figure may provide a better approximation. For example, on 22 April 2020, the Financial Times published extrapolations showing that the likely number of “excess deaths” since the start of the pandemic in the UK could be in the region of 41,000, rather than the official 17,337 fatalities officially recorded.[2]

 

Read more: Data on COVID-19 deaths amongst BAME populations

 

Data journalism has been crucial in shedding light on the fact that Black, Asian and minority ethnic (BAME) populations may be suffering disproportionately from the pandemic. Using reliable data is important in order to ensure equality of access to healthcare during the pandemic.

Unfortunately there gaps in data gathering disadvantage BAME communities even further. For example, in the UK, ethnicity is not registered on death certificates.[3] But data from hospital deaths suggests that, up to 19 April 2020, 19% of those who died in hospital in England were from BAME backgrounds when BAME residents make up 15% of the country’s population.[4] The release of data for COVID-19-related deaths sorted by local authority, by the Office for National Statistics has been key in drawing this link. It allowed people to establish correlations between COVID-19 deaths and local authority data (such as population and environmental characteristics), and revealed that a high proportion of BAME residents was the strongest predictor of a high COVID-19 death rate.

Every week it also becomes apparent that the death rate of BAME healthcare workers is disproportionately high. See this simple but striking data visualisation by Bristol-based artist Niki Groom, using statistics cited in Sathnam Sanghera’s article, ‘Coronavirus and ethnicity: black and Asian NHS medics on the front line’.

 

Coronavirus and Data visualisation

As the COVID-19 pandemic progresses, effective visualisation is particularly important to communicate ideas and facts about the situation. For example, this data visualisation by Financial Times journalist Bob Haslett shows how the United States has failed to contain the spread of the virus compared with China.

Good data visualisation can be very useful in conveying information in a format that is easy to understand and help shape policy debates. For example, the graphics in this New York Times article contrast confirmed infection with unconfirmed infections, using a subtly creative approach to mimic the spread of the virus through the air.

Responses to the COVID19 pandemic also provide a good example of the many ways you might choose to approach a particular topic, rather than focusing on the most obvious types of data. While many visualisations have portrayed the numbers of cases per country, visualisations showing the decrease of air pollution or the risk factors for different jobs have also been widely shared. You can read more about this here.

Read more about COVID-19 data visualisation and computer modelling in the ART/DATA/HEALTH article ‘How can data science help the COVID-19 crisis?’ (March 2020)

See also this interesting webcomic COVID-19 Data Literacy is for Everyone curated by Anna Feigenbaum, Aria Alamalhodaei & Alexandra P. ALberda, who aim to “help empower audiences to better understand the COVID-19 data visualisations that now fill our everyday lives:.

See also:

Learning about Data: What do we mean by data?

Health and Wellbeing Data: What forms do health and wellbeing data take?

Data for Advocacy: How can we use data to advocate for social change?

Open Data: Why do we need open data?

Data Art: How can data art help improve health and wellbeing?

Other Resources: Download the ART/DATA/HEALTH Toolkit


References

[1] Richardson, S. and Spiegelhalter, D. (2020). Coronavirus statistics: what can we trust and what should we ignore? The Guardian. Available at: https://www.theguardian.com/world/2020/apr/12/coronavirus-statistics-what-can-we-trust-and-what-should-we-ignore.

[2] Giles, C. (2020). UK coronavirus deaths more than double official figure, according to FT study. Financial Times. Available at: https://www.ft.com/content/67e6a4ee-3d05-43bc-ba03-e239799fa6ab.

[3] Barr, C., Kommenda, N., McIntyre, N. and Voce, A. (2020). Ethnic minorities dying of Covid-19 at higher rate, analysis shows. The Guardian. Available at: https://www.theguardian.com/world/2020/apr/22/racial-inequality-in-britain-found-a-risk-factor-for-covid-19.

[4] Barr et al, 2020.