The PCR test is coming under fire for missing its target: detecting COVID-19 infectiousness
The late Dr. Kary Mullis, a biochemist who won the Nobel Prize for the invention of the polymerase chain reaction (PCR) process that is used in COVID-19 testing since January 2020, said that “with PCR … you can find almost anything in anybody” in a video (around minute 49:00) from a 1997 meeting on “Corporate Greed & AIDS” in Santa Monica, California. He responded to an audience question about the possible misuse of the PCR test in medical diagnoses, stating that the technique cannot be “misused” in a narrow sense, but that the test results are prone to misinterpretation when doctors “claim that it is meaningful” when a virus is discovered based on a minimal amount of genetic material present in a patient. “It doesn’t tell you that you’re sick“, as Mullis says in an extract of the video created by Bright Light News. In this context, he also referred to the “Buddhist notion that everything is contained in everything else.” While the statements were made in conjunction with HIV/AIDS, where Mullis held a highly contested view, they increasingly prove true in the ongoing SARS-CoV-2/COVID-19 testing fiasco.
For coronaphobics and lockdown believers, the United States serve as the poster child for how not to handle the pandemic. The Johns Hopkins University COVID-19 dashboard (Fig. 1) shows cumulative “case” counts by US counties using proportional circles – a suitable cartographic choice, although the bright red colour on dark background is questionable, as discussed elsewhere. The ten-and-a-half million cumulative cases and nearly a quarter-million deaths as of November 10th, place the US at the top of the COVID-19 world rankings. But are these numbers actually big? And what can we gather from the spatial pattern of cases?
On 15 October 2020, a group of some 30 medical scientists and academics published a letter titled “Scientific consensus on the COVID-19 pandemic: we need to act now” in The Lancet, see https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)32153-X/. The authors reiterate details from the early, terrifying assessments of the public health threats from Sars-CoV-2. This includes the initial worry that the entire human population on earth might be susceptible to infection, owing to a lack of prior exposure; the infection-fatality rate of COVID-19 being “several-fold higher” than it is for the flu; and a claim that lockdowns were successful in slowing the spread of the virus.
Among German lockdown sceptics, official data from the infectious disease agency RKI made the rounds, which suggest that Sars-CoV-2 may have disappeared as early as mid-April 2020. We will take a look at the last weekly report of the RKI’s routine influenza surveillance workgroup for the 2019/20 season ending September 29th, available via https://influenza.rki.de/Wochenberichte.aspx. The surveillance program is conducted through five avenues: a voluntary population survey, an incidence index based on consultations for acute respiratory diseases at some 750 regional doctors’ practices, lab reports from a sentinel network of some 35 representative doctors, lab-confirmed flu cases reported in adherence to the infectious disease act, and data from 69 sentinel hospitals about patients with severe acute respiratory illness.
In his seminal book “How to Lie with Maps”, Professor Mark Monmonier illustrates how map makers can intentionally or inadvertently convey falsehoods using misguided data selection and cartographic design options. In an era of widely accessible, easy-to-use online mapping tools, misleading maps are becoming ubiquitous. Maps of COVID-19 statistics, along with associated graphs and data tables, which have become a focus of public attention this year, are no exception. Therefore, I want to take another look at the pitfalls of the popular choropleth map.
The granularity at which you look at COVID-19 may determine your attitude towards Sars-CoV-2
Scale is one of the most fundamental concepts in Geography. My PhD student just completed her dissertation on “The Consequence of Scale: Process and Policy Implications of Composite Index Modelling Using the Conceptual Framework of GIS-MCDA”, in which she compares biodiversity indices computed at different scales within a city, for example smaller census tracts vs larger social planning neighbourhoods. In Geographic Information Systems (GIS), we usually work with aggregated data, and the scale of aggregation can range from census blocks through postcode areas and neighbourhoods/wards to cities, counties, provinces, and countries. Results of data analytics are known to depend on several aspects of scale, including the observation/measurement scale, at which data are collected; modelling scale, at which data are analyzed; and operational/policy scale(s), at which decisions are made and implemented.
The COVID-19 lockdown has brought with it an abundance of online professional development opportunities – a welcome escape from the terrors caused by the novel coronavirus (or by the house arrest and social distancing regime itself, if you concur with my view ;). On April 29, cartographer Daniel P. Huffman of Madison, Wisconsin, organized “How to do Map Stuff: A Live Community Sharing Event” with virtual workshops offered by volunteers from around the world, see https://somethingaboutmaps.wordpress.com/2020/03/19/how-to-do-map-stuff/.
Along with several interesting presentations, I listened in to Minnesota-based cartographer Ross Thorn, who went through the process of “Creating an Interactive Fantasy Map” using QGIS and MapBox. The recording is now posted on Youtube at https://www.youtube.com/watch?v=2nmLibB3lGs (starts around minute 9:30). Rather than create a set of islands from scratch, Ross “floods” a digital elevation model (DEM) so that mountains or hills turn into islands while lower elevations are transformed into the open seas… The remainder of that tutorial focused on vectorizing the island boundaries and adding land-use polygons as well as settlement locations with attached information.
Do not use choropleths for your COVID-19 counts, ever!
In a hilarious contribution to Medium, Dr. Noah Haber et al. issued a call to “Flatten the Curve of Armchair Epidemiology“. They analyze the transmission of “well-intended partial truths” about COVID-19 and caution of hidden “viral reservoirs throughout the internet”. To flatten this curve, they recommend fact-checking before posting and go as far as endorsing social-media distancing measures. As with general COVID-19 tips based on armchair epidemiology, misinformation can also be spread through the numerous COVID-19 maps that are widely circulating through the Web. In this article I want to focus on one particular instance of armchair cartography: wrongly mapping COVID-19 count data using choropleth symbology.
This past fall semester of 2019 marked my 15th time teaching our graduate cartography course. When I joined Ryerson University in August 2006, I had already taught MSA 9050 Digital Cartography at the University of Toronto for three years, in Fall 2003, 2004, and 2005. The course was part of the joint Master of Spatial Analysis (MSA) program between UofT’s and Ryerson’s Geography departments, and was also cross-listed with UofT’s graduate course GGR 1913H of the same title. The course had been taught by Byron Moldofsky, who retired as Manager of UofT’s GIS and Cartography Office in 2017, after 37 years of service as a staff member, and continues to be active as an executive member of the Canadian Cartographic Association and a free-lance cartographer.
Much like many economic, social, health, crime, and environmental data sets, election results have an important geospatial component. For the 2019 federal election, Canada was divided into 338 electoral districts, each of which is represented by a member of parliament. Consequently, thematic maps – usually representing the “first-past-the-post” winning party – are a typical part of news media coverage of the 43rd election. The following examples were found in select Canadian media outlets on the morning after the election.