GIS2 at Ryerson – Page 2 – Geographic Information Science and Systems

28 November 202029 March 2021

The Little Pandemic That Could

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.

Dr. Kary Mullis, inventor of the PCR technique, speaks at an event in 1997. Source: screen capture from video at https://lbry.tv/@marengeti:c/corporate-greed-aids-santa-monica-1997-07-12-part2:5

25 November 202029 March 2021

The Coronoia Blogbook

Critical Observations re COVID-19 and Lockdowns in Canada, Germany, and the United States

“The Coronoia Blogbook: Critical Observations re COVID-19 and Lockdowns in Canada, Germany, and the United States” is a collection of posts originally published on this blog between March and November 2020. The posts were lightly edited and rearranged into three thematic groups: Part I puts “COVID-19 in Context”, Part II illustrates “Mapping the Pandemic”, and Part III discusses “What the Data Do Not Tell Us”.

Back and front covers of "The Coronoia Blogbook"

16 November 202029 March 2021

The Divided States of Coronamerica: How Big is too Big?

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?

Figure 1: The Johns Hopkins University COVID-19 dashboard zoomed to the United States. Source: screenshot from https://coronavirus.jhu.edu/map.html.

12 November 202029 March 2021

Some Doctors Are Giving John Snow a Bad Name

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.

7 November 202029 March 2021

Respiratory Disease Surveillance, Seasonality, and Sars-CoV-2

When did the pandemic end in Canada and Germany?

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.

Figure 1a. Estimated weekly rate of acute respiratory illness in percent of population in Germany. The data are based on a voluntary survey for three previous seasons (blue and yellow curves) and summer 2020 (black curve). X axis is in calendar weeks, vertical line marks January 1st. Source: https://influenza.rki.de/Wochenberichte/2019_2020/2020-39.pdf

3 November 202029 March 2021

How to Lie with COVID-19 Maps

… or tell some truths through refined cartography

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.

Screenshot from https://newsinteractives.cbc.ca/coronavirustracker/ with data updated as of 2 November 2020. Note this is an example of how NOT to map COVID-19, see text!

26 October 202029 March 2021

Science is Dead – Long Live “The” Science?

This year, politicians, public health officials, journalists, and even our Facebook friends are urging us to listen to “the” data, trust “the” evidence, and follow “the” science with respect to COVID-19. Yet, each of these groups feel entitled to select which data, evidence, and science they elevate to the royal rank of “the” data, evidence, and science. A quick reflection or a look at an encyclopedia (online or otherwise!) will reveal that science is a never-ending process of asking questions, making observations, structuring ideas, hypothesizing explanations, conducting experiments, and drawing preliminary conclusions that inevitably raise more questions to be researched. In complex systems and processes such as infectious disease spread, the data and evidence resulting from the scientific method, and the underlying models and theories, may never be conclusive, and as such it is foolish and misleading to speak of “the” one science guiding us through the corona crisis.

19 October 202029 March 2021

Faces and Facets of the German-Speaking Corona Opposition

An account of events from March to September 2020

Owing to a set of personal circumstances, I was able to closely follow the first half year of Sars-CoV-2 and COVID-19 in Canada in comparison with Europe. Over there, Germany, and to a lesser extent Austria and German-speaking parts of Switzerland, produced some of the earliest skeptical voices who put the virus’s threat into perspective. Germany also has perhaps the broadest spectrum of critics of the government response measures globally. In the interest of open discourse and exchange of ideas, I offer this subjective overview of key actors and aspects of the corona opposition in the German-speaking world. I am focusing specifically on the different areas of expertise represented among the dissenting views.

8 October 202029 March 2021

Issues of Scale in the Corona Crisis

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.

25 September 202029 March 2021

The Saga of False-Positive COVID-19 Tests

[Skip to second paragraph if you are not interested in the German context of the false positives issue.]

On June 5, 2020, OVALMedia’s Robert Cibis interviewed the Austrian microbiologist and infectious disease specialist Dr. Martin Haditsch about laboratory tests and specifically the PCR test that is used globally to detect the Sars-CoV-2 virus in a person. The interview [in German] broached the issue of false-positive test results in the context of a low-prevalence disease and imperfect tests. Two Youtube copies of the one-hour interview have a total of over 100,000 views at the time of writing. The next day, Swiss entrepreneur and Youtuber Samuel Eckert presented a 20-minute summary and explanation of the false-positive issue using an interactive Excel spreadsheet. His video currently boasts over 225,000 views with 15,000 likes. Possibly in response, the German Federal Minister of Health Jens Spahn, a banker by training, said in a brief interview contained in a tweet from public TV channel ARD on June 14 that if the COVID-19 prevalence continued to drop and testing was simultaneously expanded (as has been the case in many Western countries since mid-April) into the millions then you would eventually obtain more false-positive than correct-positive results.