Quality Over Quantity: COVID-19 community data

This post was written by Anniek de Koning, research master student in Media Studies at the University of Amsterdam

‘To measure is to know’ is the mantra that underlies the increase in the collection of data that can be tracked, monitored, or analyzed. Datafication played a big role in the battle against the COVID-19 pandemic too. The collection of COVID-19 data actively shaped policies to contain the virus by measuring demographic trends in the spread of the virus. Data collection on communities in the battle against COVID-19 has been implemented, although it is mostly still done through a top-down approach. Data on specific communities is thus particularly useful in detecting mild or asymptomatic cases and in reaching people who are not normally reached, creating a more accurate picture of the pandemic. This can be seen in the example of Toronto.

In July 2020, Toronto mayor, John Tory, shared the first analyses of sociodemographic disaggregated data by Toronto Public Health on COVID-19. It became clear that Black and Latino populations in Toronto had 611 times more cases of COVID-19 compared to white citizens. In an article about Toronto’s sociodemographic COVID-19 data collection, Kwame McKenzie states that in order to confine the virus, the city used strategies focused on the community. Through ‘community-based multilingual public health campaigns, community testing sites, free masks, free voluntary isolation sites, eviction prevention advocacy, food security programs, free digital access, and emergency child-care’ (Ibid: p. 1), the numbers went down tremendously. Although this data may seem like an objective representation of the virus, this data is not raw. That is because, what data we collect, how we collect that data, who is collecting that data, and what choices are made in the visualization of that data, all influence the outcomes often portrayed as neutral.  In their book Data Feminism, Catherine D’Ignazio and Lauren Klein have argued for a more feminist approach to data science, which centralizes the embodiment of data and a plurality of local knowledge (p. 34). Through collecting data, communities can have a say in which data is collected and how, yet they can also use it to challenge data produced by nation-states, for example. As COVID-19has arguably intensified the process of datafication, according to Dennis Nguyen, now is the time to critically assess the top-down processes of data collection that are prevalent today, and explore more grassroots-oriented data collection, such as community data. As I argue in this blog, however, there are merits and risks to considering community data in the battle against COVID-19. 

 

Community data

In the example mentioned above, having community-specific data produced by and for community members resulted in a less racialized risk of getting the virus. Although community members were increasingly involved in Toronto’s COVID-19 policy change, which indeed resulted in positive effects, this data collection process was still led by the state instead of community members, as was suggested by D’Ignazio and Klein. When data collection for communities is made by community members themselves, datasets can potentially yield more specific insight into COVID-19 trends in a specific neighborhood or among a select group of people. Besides the idealistic approach of participatory governance that can be attributed to community data, there are pragmatic reasons to apply community data collection, such as gaining better insights and increased participation, which can lead to more effective local plans and policies. Community data that is collected by various community members can therefore lead to better solutions to local problems as community members can provide insights into their specific situation. This has already been implemented in improving communal health before COVID-19 when communities pointed out issues that would otherwise remain unnoticed. A current example of community-based data collection that is suggested by John Murphy, Berkely Franz, and Karen Callaghan, is popular epidemiology. Popular epidemiology refers to a process through which the effects of pollution or other maladies are measured and interpreted by community members who otherwise ‘may easily escape the assessment of professional epidemiologists’ (p. 10). In such an example, community data can result in a more contextualized approach to health. 

Community data can also be used to check the data provided by governments or corporations. This type of grassroot data collection has been given many different names, such as ‘counterdata, agnostic data collection, data activism, and citizen science’ (p. 34). In this process, community members do not only collect the data but also play an active role in its interpretation, according to John Murphy, Berkely Franz and Karen Callaghan  (p.14). Although technological innovations facilitated the collection of vast amounts of data, the use of data to dispute governmentally provided data is nothing new. An example of counter-data given by Lauren Klein and Catherine D’Ignazio dates back to 1895 when Ida B. Wells ´assembled a set of statistics on the epidemic of lynching´ of black people in the United States (p. 34). In doing so, she exposed false statements by white people about allegations of rape, theft, or assault that were used to justify the lynching. By collecting her own data, she was able to challenge that narrative and expose what was actually happening. So, even though collecting community data may not eradicate inequality, it can, according to D’Ignazio and Klein, be an exhaustive method in holding powerful institutions accountable. In their article on intersectional principles for equitable and actionable COVID-19 data, D’Ignazio and Klein give an example of a community collecting data to counter the narrative on COVID-19: the data activist group Data for Black Lives (p. 4). Data for Black Lives is ‘a group of activists, organizers, and mathematicians committed to the mission of using data science to create concrete and measurable change in the lives of Black people’. Through collecting data, such as which states in the United States collect racial COVID-19 data, or the rates of COVID-19 related cases and deaths of black people, Data for Black Lives challenges the dominant narrative on COVID-19. This narrative either ignores race as a factor or considers black people riskier in spreading COVID-19. Instead, Data for Black Lives acknowledges the socioeconomic and environmental factors that put them at risk. This shows the merits that can be attributed to relying on community data collected by community members themselves. 

Risks 

There are risks and difficulties in the application of community data, however. For example, what constitutes a community can mean different things for different people. It can, for example, be based on a geographical area or a shared characteristic. This ambiguity can, despite being focused on a specific neighborhood or a community with a shared characteristic, exclude people on the margins of that community. As Stefania Milan and Emile Treré have pointed out, there are still people who are largely excluded from any COVID-19 data. The authors mention undocumented migrants and refugees as an example, or people with precarious working conditions, including sex workers. Although community data could be beneficial for rural areas that are often overlooked, not all people and communities have the same access to collecting their own COVID-19 data or can claim the same level of ownership. This leads to the question: even when communities do collect their own data, who is represented and who is still excluded from that data?

Another risk of community-based data is when outside experts are included and the data is stripped from its social embeddedness. Because ´when data are removed for analysis, the intimacy is lost’ (Murphy et al.:p. 14). This could be the case if community-collected data is, for example, interpreted by an outside expert. Only if community members are part of every phase of the project, this could be maintained. This has also been raised by D’Ignazio and Klein in their third principle of data feminism, to elevate emotion and embodiment in data. Data visualization of COVID-19 deaths strips the data from the individuals who were behind it. Although COVID-19’s increased datafication has highlighted the need for local, community-driven data, public health restrictions have also made it more challenging to collect such data. Projects like this are also already less likely to get funding, according to Murphy, Franz and Callaghan (p. 10). Using their example, health officials often express the fear that the outcomes could not be applied outside of the community and would therefore be a risky investment (p. 17). Producing generalizable proof of the benefits of community data seems paradoxical, yet is often required, which puts the workload on the community itself. 

 As I mentioned in my previous blog, technologies do not always have the most equitable outcomes, which is something to keep in mind with the implementation of community data as well. Although the value of data on communities in the battle against COVID-19 was shared by the World Health Organization, I argue that community data can be useful too. Despite the fact that COVID-19 community data may actively include various community members and can lead to more local knowledge and a better representation of the pandemic, the lack of funding, and the questions around data ownership, data interpretation, and inclusion and exclusion criteria within communities constitute serious risks that need to be taken into account. D’Ignazio and Klein have addressed some of these risks by prioritizing local knowledge and embodied data in data science. These risks should not rule out communities collecting their own data, but I hope they emphasize what we do need to consider in collecting community data on COVID-19. 

 

References

Anderson, K. M., & Olsen, S. (2016). The private sector as a catalyst for health equity and a vibrant economy : proceedings of a workshop. The National Academies Press.

Data for black lives. Data 4 Black Lives. (n.d.). https://d4bl.org/. 

D’Ignazio, C., & Klein, L. F. (2020). Data Feminism. MIT Press. https://doi.org/10.7551/mitpress/11805.001.0001

I-sense. (n.d.). Community-based study seeks to better Understand Covid-19 spread. https://www.i-sense.org.uk/news/community-based-study-seeks-better-understand-covid-19-spread. 

McKenzie K. (2021). Socio-demographic data collection and equity in covid-19 in Toronto. EClinicalMedicine, 34, 100812. https://doi.org/10.1016/j.eclinm.2021.100812

Merriam-Webster. (n.d.). Community. Merriam-Webster. https://www.merriam-webster.com/dictionary/community. 

Milan, S., & Treré, E. (2020). The Rise of the Data Poor: The COVID-19 Pandemic Seen From the Margins. Social Media + Society, 6(3), 205630512094823–2056305120948233. https://doi.org/10.1177/2056305120948233

Murphy, J. W., Franz, B. A., & Callaghan, K. A. (2015). Is community-based work compatible with data collection? Journal of Sociology and Social Welfare, 42(4), 9–.22

Nguyen, D. (2021). Mediatisation and datafication in the global COVID-19 pandemic: on the urgency of data literacy. Media International Australia Incorporating Culture & Policy, 178(1), 210–214. https://doi.org/10.1177/1329878X20947563

Walker, S. J. (2015). Any color you want as long as it’s beige? Review of Big Data: A Revolution That Will Transform How We Live, Work, and Think by Viktor Mayer-Schönberger and Kenneth Cukier. https://nua.repository.guildhe.ac.uk/id/eprint/17236/2/Review%20of%20Big%20Data3.pdf

https://www.who.int/publications/i/item/WHO-2019-nCoV-Comm_health_care-2020.