COVID-19 Data Manipulations - A Tale of Four Pandemics
In a recently published book chapter, I summarize the data issues that resulted in the contrived pandemics of the unafraid, the untreated, the unvaccinated, and the unmasked.
During the SARS-CoV-2 pandemic, various data-centred narratives and counter-narratives have emerged based on the availability of real-time and cumulative open data. Open data, specifically geospatial open data, had become an area of interest and expertise a few years ago when government open data catalogues were launched and several research students got me hooked on the topic*. Now, in response to an invitation for book chapters for “New Trends and Challenges in Open Data”, I wrote some 5,000 words with 75 references about “Pandemic Open Data: Blessing or Curse?”
I had been meaning to write about “The pandemic data circus” for quite some time, a notion which now serves as the heading for Section 2 of the book chapter. There, I honour eminent researchers who have criticized and/or corrected some of the data issues that have arisen throughout the SARS-CoV-2 pandemic, including Drs. Ioannidis, Henegan, Fenton, Neil, Kuhbandner, and Hirsch (see full text for details — freely available in HTML format on that page as well as in PDF format upon registration with the publisher).
In Section 3, I proceed to describing and mildly critiquing “Select open data repositories and apps for COVID-19” including the WHO COVID-19 dashboard, Our World In Data’s coronavirus site, as well as various other sites such as OpenVAERS. I also include the involuntarily open data/document sites by Public Health and Medical Professionals for Transparency and the Australian Therapeutic Goods Authority, which offer content that was force-released based on freedom-of-information access requests.
In the book chapter’s core Section 4 on “Data-centered COVID-19 narratives and counter-narratives”, I then propose four pandemic narratives, namely the pandemic of the unafraid, pandemic of the untreated, pandemic of the unvaccinated, and the pandemic of the unmasked.
The first narrative is the pandemic of the unafraid, where politicians and media use public health data to exaggerate the risks of COVID-19 in order to create fear in the general population. The UK government’s “Lockdown Files” prove that state actors deliberately used fear to secure the public’s compliance with pandemic restrictions. Meanwhile, many of us had concerns about the exaggeration of COVID-caused mortality due to overly generous rules about how deaths were counted.
The pandemic of the untreated reflects the narrative that health care systems would be unable to treat everyone equally during the pandemic, leading to lockdowns to “flatten the curve.” Has anyone noticed and noted yet that we did instead “flatten the cure?” As far as I am concerned, it was never established that hospitals worldwide were more overloaded during COVID-19 than during other respiratory disease cycles. In addition, hospitalizations due to COVID-19 were only a subset of all hospitalizations with a positive COVID-19 test, leading to significant over-counting of COVID hospitalizations and deaths.
The infamous pandemic of the unvaccinated refers to the slogan that arose when it appeared that higher proportions of seriously ill COVID patients were unvaccinated. Interestingly, this narrative started innocuously as a concern for unvaccinated individuals, but quickly turned into a debate about the alleged threats posed by unvaccinated individuals and the ethics of segregating us with respect to access to services and participation in public life.
Lastly, the pandemic of the unmasked is held up to this day by zombie characters who contend, against common sense and mounting evidence, that face masks are safe and effective.
My conclusions, titled “Approaches towards reducing misinformation from, and with, open data” (see what I did there?), make a few, not overly exciting recommendations, including to provide data that are as disaggregated as possible; to offer multiple options where aggregation and classification of data is required; and to be transparent about the meaning of all variables and their values.
In terms of aggregation, I give the example of the classification of vaccinated individuals within 14 days of their shot as unvaccinated. Importantly, this may be valid if an analyst is interested in the effectiveness of the injection (which takes some time to develop, in theory), but it is wholly inappropriate if an analyst is studying vaccine safety, in which case the individual should be classified as vaccinated from the moment the needle pierces their skin. You will be familiar with Prof. Fenton’s and Neil’s important work in this regard, e.g. here.
My book chapter emphasizes that open data have been used in many different ways during the pandemic, including negative but also positive examples. What I call “citizen research” would not be possible without access to relevant datasets. I try to highlight the importance of critically assessing data and their interpretations, including but not limited to the “four pandemics” that I identify. Ultimately, I conclude that “the openness of COVID-19 data … was a blessing, if in disguise.”
*Previous work on open data includes a collection of articles organized by Victoria Fast that we prefaced with an editorial titled “Mediating open data: providers, portals, and platforms.” Within that collection, Edgar Baculi led the writeup of his project on “The Geospatial Contents of Municipal and Regional Open Data Catalogs in Canada.” And with Sarah Greene, I later wrote a summary of her Master’s research paper on “Examining the Value of Geospatial Open Data”, which was included in a book titled “The Future of Open Data.”