A new “killer virus”, global panic, squandered public monies, and a rushed vaccine—déjà vu from the swine flu pandemic
The Council of Europe blasts the World Health Organization and national governments around the globe for the handling of the pandemic. Their report cites “overwhelming evidence that the seriousness of the pandemic was vastly over-rated by WHO, which led to a distortion of public health priorities.” The British Labour politician and author of the report, Paul Flynn, is cited with the comment: “This was a pandemic that never really was.”The pandemic in question is the 2009 swine flu pandemic and the quote is from a 2010 article in the British Medical Journal. The concerns at the time focused on undue influence by big pharma companies on the WHO, which led to poor decision-making and excessive spending by many countries on unnecessary anti-viral drugs and vaccines. By early 2010, Mr. Flynn apparently filed a motion in the House of Parliament to deplete left over Tamiflu pill stockpiles by using them as winter road salt.
Cover page of Council of Europe report on the handling of the 2009 swine flu pandemic. Source: http://www.assembly.coe.int/CommitteeDocs/2010/20100329_MemorandumPandemie_E.pdf
The BMJ, one of the oldest and most highly regarded medical journals, together with the Bureau of Investigative Journalism in London, UK, published another article in mid-2010 about “WHO and the pandemic flu ‘conspiracies’“. The 6-page article notes that the WHO labelled allegations of hiding the influence of pharma advisors on its policies, which benefited the same pharmaceutical industry, as “conspiracies”. Whether or not these advisors were biased seems irrelevant to me; the appearance alone of a possible bias is clear. Research ethics require the disclosure of “real, perceived or potential conflict of interest” of e.g. evaluation committee members, here cited from the Natural Sciences and Engineering Research Council of Canada’s “Conflict of Interest and Confidentiality” policy. We should expect the same transparency from advisors to public health agencies that make life-changing decisions for millions of people.
Arte TV documentary “Profiteers of Fear – The Business of Swine Flu”. Source: Screenshot from https://www.youtube.com/watch?v=lR3pXGQ_tqI
“Profiteers of Fear” is a documentary created in late 2009 for the Franco-German public TV channel Arte. The film-makers speak with numerous WHO staff, politicians, scientists, and pharma representatives about the H1N1 pandemic. In essence, the work suggests that the WHO over-estimated the virulence of that virus, possibly under the influence of big pharma corporations, and by declaring the outbreak a pandemic prompted national governments to initiate overblown responses.
A contemporary commentary (in German) from October 7 starts off sneering at the comeback among corona critics of the 11-years old “Profiteers of Fear”. The writer reports that the 2009 film-maker Jutta Pinzler and the TV channel distance themselves from applying the lessons from the swine flu scandal to the SARS-CoV-2 pandemic. She also assiduously belittles one of the critics cited in the 2009 film for wrongly minimizing the threat from COVID-19 in comparison to H1N1. Yet, the original film-maker is also cited as supporting open debate of dissenting opinions. And it then turns out that Ms Pinzler was working on a new documentary about the implications of the corona crisis on democracy, focused on a comparison between France, Germany, and Sweden. In the meantime, this documentary “Corona – Safety Contra Freedom” (in German or French) has premiered on November 10 and turns out to be quite critical of lockdowns and the general handling of the ongoing pandemic.
Documentary exploring the conflict between public health & safety versus democratic freedoms. Source: Screenshot from https://www.arte.tv/de/videos/098118-000-A/corona-sicherheit-kontra-freiheit/
Back a couple of years, another BMJ article reviews safety issues with the Pandemrix vaccine for the swine flu. Author Dr. Peter Doshi asks “why was the public not told of early warning signs?” The article reveals that as part of court proceedings against a pharma corporation, internal documents were revealed that would have allowed to raise the alarm on serious side effects of Pandemrix, including debilitating narcolepsy among immunized children.
More recently, Dr. Doshi issued an important warning again. This time, he reviewed the COVID-19 vaccine trials and found that the two widely accepted criteria for an effective vaccine—to reduce the likelihoods of transmission and of severe illness—are not being assessed by the ongoing phase III experiments. None of the seven candidate vaccine is tested for “Reduction in severe covid-19 (hospital admission, ICU, or death)” nor “Interruption of transmission (person to person spread)”. Instead, the only marker of success is a reduction in mild illness confirmed by the notoriously unreliable PCR test.
Source: screenshot from https://www.bmj.com/content/371/bmj.m4037 (full text)
The first BMJ article cited above has one response from a medical student in India. The student notes that it is easy to criticize the swine flu pandemic response with the benefit of hindsight and argues that we should be “better safe than sorry”. But are we safer with untested drugs, then or now? The inability of the COVID-19 vaccine trials to determine whether the candidate vaccines achieve the basic expectations that the public has, is shocking. The chief medical officer of one of the vendors explains that there is not enough money and time to conduct an even larger trial that would allow to test for the rare occurrences of serious illness as well as sporadic transmission. Yet, given the 2009/10 experience with side effects, I worry that undetected issues could be even more costly in the long term.
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
While researching this post, I came across an interesting Reuters “fact check” of social media posts claiming that the COVID-19 PCR test is a fraud. The fact checkers actually revised their initial finding that a quote that the PCR test “can’t be used in virus detection” was falsely attributed to the PCR inventor Dr. Mullis to a more nuanced assessment that the statement “may have been a fair reflection of Mullis’s views, even if not a direct quote.” These so-called fact checkers that are employed by news organizations and social media corporations use questionable tactics discussed by other analysts. Their verdicts inevitably lead to suppression and removal of dissenting opinions in public debate, most recently seen for example in the cancellation of microbiologist Prof. Sucharit Bhakdi’s Youtube account. Two months earlier, psychologist Dr. Rafael Bonelli had posted a critic of journalists aptly titled “When Intellectual Dwarfs Assess a Giant Like Sucharit Bhakdi”…
PCR technique for amplification of genetic sequence, resulting in 2^n copies after n cycles. Source: https://commons.wikimedia.org/wiki/File:Polymerase_chain_reaction-en.svg (CC BY-SA 4.0)
One of the most-cited deviations from the mainstream narrative in a traditional newspaper was the New York Times’s August 29 article “Your Coronavirus Test Is Positive. Maybe It Shouldn’t Be.” The article outlines the PCR technique as an amplification of genetic material with the all-important cycle threshold (Ct), which determines how often the material will be duplicated. The Times examined a dataset from multiple states and found that “up to 90 percent of people testing positive carried barely any virus”, meaning that they are likely not infectious. The author noted that among 45,000 new “cases” at that time, possibly only 4,500 should be required to self-isolate and be subject to contact tracing. A September 29 article in Science magazine, “One number could help reveal how infectious a COVID-19 patient is. Should test results include it?“, refers to a recently pre-published French study, which “found that 70% of samples with Ct values of 25 or below could be cultured, compared with less than 3% of the cases with Ct values above 35.” In other words, a positive test result after 25 or fewer cycles likely indicates an infectious carrier of SARS-CoV-2, while positive tests above 35 most likely should not be considered “cases”.
Across Canada, the provinces recommend different cycle thresholds, but nearly all of them are at or above 35, making a joke of your PCR test results. The Ct values used in the map below were collected by Marie Oakes, senior editor of the independent news outlet Westphalian Times, for her September 25 article “International experts suggest that up to 90% COVID cases could be false positives“.
Cartographers do not normally include symbols in the legend that do not appear on the map, yet to illustrate the recommended Ct values of up to 25 according to the French study or up to 30 according to the Westphalian Times article, I included green and yellow “aspirational” classes. The orange representing the least harmful over-testing in Canada applies to Newfoundland & Labrador(33), Nova Scotia (33-35), and Alberta (35). The worst offenders are the large provinces of Ontario (38-45) and Quebec (45). Ontario today reported 1,855 new “cases” – coincidentally found in a record number of 58,000 tests. If we apply the French study proportions for tests turning out positive after more than 35 cycles, then only 56 Ontarians (3% of 1,855 positive tests) should be considered newly “infected” in a meaningful way (including that they may be infectious), and take the corresponding precautions. The other 1,799 test-positives along with the 56,145 test-negatives and the rest of Ontario’s 14,500,000 residents should be given a break and return to their healthy, social lives and economic activities.
To help us get back to some kind of normality, lawyers around the globe are collaborating in the preparation of major law suits focused on the role of the PCR test in misjudging the pandemic. A couple of weeks ago, a Portuguese appeals court upheld a lower court’s ruling that declared a quarantine order unlawful. Importantly, the appeals court stated that a positive PCR test is not a reliable indication of a person’s infectiousness. And this week in Germany, lawyer Dr. Reiner Fuellmich submitted an injunction and claim for damages on behalf of lockdown critic Dr. Wolfgang Wodarg against the self-declared anti-fake news blog “Volksverpetzer” (literally “people’s snitch”). This court action also revolves in part around the validity of the PCR test for diagnostic purposes and could serve as a stepping stone to a larger class action lawsuit in North America.
Adapted figure caption from original: Comparison of delay between symptom onset and test to probability of successful cultivation of virus (probability positive) and SARS-CoV-2 PCR test (E gene) cycle threshold (Ct) value. Source: Bullard et al. (2020), https://doi.org/10.1093/cid/ciaa638
UPDATE: Things are developing fast these days. Within 24 hours of posting, I became aware that a higher administrative court in Germany has also confirmed that a positive PCR test does not mean that an individual is infectious. The court refers to a summary by the German Society for Neurology of a Canadian study titled “Predicting Infectious Severe Acute Respiratory Syndrome Coronavirus 2 From Diagnostic Samples” published as early as 22 May 2020 in the journal Clinical Infectious Diseases. Most interestingly, the researchers from the University of Manitoba and the National Microbiology Laboratory of the Public Health Agency of Canada found that positive test results with a Ct value above 24 could not be cultivated, thus setting an even stricter validity threshold for the PCR test than what I had found and used in the first part of this post. The authors also examine the impact of the delay between symptom onset and test on the patients’ infectiousness, as illustrated in the above figure.
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”.
Each of the 14 chapters critically examines the corona crisis with reference to the current discourse at the time of writing. Some chapters include analyses of government data and contain original maps and graphs. The book is framed by a short preface with background on the author’s motivation and a brief conclusion with thoughts about the reasons for the ongoing “coronoia” and how it may end. The text is written from a critical perspective to make room for dissenting opinions in the public debate of the pandemic and the government, media, and individual responses to it.
The 98-page paperback is printed in colour and available at https://www.amazon.ca/dp/B08NRP13QP. An eBook is currently not offered, since the original posts are available here for on-screen reading.
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.
With 330,000,000 residents and counting (Worldometers.info), the 10 or so million known infections amount to just about 3% of the population. Of course, most of these 3% never noticed any symptoms or were only mildly ill. Nevertheless, according to OurWorldInData.org, the US have exhibited weekly excess mortality from late March to late September in the order of 10% up to 45%. The normal death rate in the US is less than 1 in 100 per year, resulting in under 3 million annual deaths or about 50,000 to 60,000 deaths per week (CDC,gov). Weekly mortality in the spring of 2020 was between 60,000 and close to 80,000, and in the summer of 2020 between 55,000 and below 65,000. Thus, while every human fatality is tragic, the population-level numbers are not out of proportion and we always have to view big numbers in conjunction with related numbers, which will be even bigger for as large a country as the US.
Figure 2: Top-20 monthly mortality in Sweden, with April 2020 in 15th position. Source: Tweet by @HaraldofW, 27 June 2020, https://twitter.com/HaraldofW/status/1276875751225274369
In addition to comparing COVID-19 data to reference variables such as total population or total mortality, we also need to consider historical benchmarks. It is relatively easy to find tables or charts online that include last year’s data, or the last 3-5 years for comparison, but it is quite enlightening to look a bit further back in time. I learned this from reading discussions about Sweden on Twitter, where user @HaraldofW, a self-proclaimed “Citizen Producing Graphs”, presented monthly deaths statistics for the no-lockdown country, which show that April 2020 was only the 15th-deadliest month since 1990 (Fig. 2), and no other month of this year made it into the top-20. Without denying that a serious respiratory disease is going around, this comparison certainly should put to rest the claim that Sars-CoV-2 is a once-in-a-century pandemic.
Figure 3: Annual mortality in the United States in relation to total population, 1980 to 2019. Data sources: Combination of data from United Nations Statistics Division at http://data.un.org, Centers for Disease Control and Prevention at https://wonder.cdc.gov/, and Population Reference Bureau at https://www.prb.org/usdata/
I do not have the same monthly data for the US and it was difficult enough to find the annual death counts and total population numbers since 1980 shown in the chart in Fig. 3. Overall, annual mortality in the US (grey bars) has been increasing almost every year along with the significant growth in population (line chart). However, if you adjust the raw death count to the 2019 population (black bars), you can see that mortality was quite stable through the 1980s and 1990s, declined markedly through the 2000s, and has climbed again from a low in 2009 to the levels seen in the 1980s. Again, this is not intended to trivialize fatalities from COVID-19, from influenza, or from any other cause-of-death, yet it means that we need to extend our focus beyond the immediate context to gain a more balanced, proportionate perspective on the current pandemic. Once we add the final 2020 mortality into the graph, or the monthly and/or state-specific data to the corresponding timelines, we will be able to determine whether, and by how much, 2020 was different than previous years that we have considered “normal” by all accounts.
OurworldInData.org also notes that the total excess deaths of 275,000 is only partially explained by the confirmed COVID-19 deaths; while they insinuate on some of their maps and charts that the death toll of COVID-19 may be higher than what is known, I think it is more likely that we are seeing the impact of lockdowns. Just today, more anecdotal evidence for this concern came from Ontario, Canada, with a 40% increase in fatal opioid overdoses during the pandemic, from Berlin, Germany, with a 60-fold increase of emergency calls for attempted strangling/hanging from single-digit numbers in 2018 and 2019 to almost 300 so far in 2020, and from Arizona, where a school superintendent raises concerns about rising under-age suicides. The latest web site to collect news reports about the collateral damage from lockdowns is http://thepriceofpanic.com/. For a more systematic overview of possible deaths from the ongoing crisis management, I refer to Dr. John Ioannidis’ paper on “Global perspective of COVID‐19 epidemiology for a full‐cycle pandemic”. The table from that paper reproduced in Fig. 4 is particularly concerning with respect to the medium- and long-term horizon for excess deaths, which will make it difficult to assess the true cost of lockdowns.
Figure 4: Possible causes of excess deaths from pandemic response measures. Source: Ioannidis 2020, https://onlinelibrary.wiley.com/doi/10.1111/eci.13423, with minor modifications.
Another Twitter discovery brings me to my second main point for this post: the division(s) between the American states with respect to Sars-CoV-2 spread, impact, and response measures. The anonymous account @EthicalSkeptic and the associated blog at https://theethicalskeptic.com/ analyzes COVID-19 data from a number of unusual perspectives. For one of the recurring graphs, this analyst separates the states into hot, southern states and cooler northern states to reflect possible differences in seasonality of Sars-CoV-2. For example, the @EthicalSkeptic’s November 5th update shows the peak of daily cases in the northern states in mid-April compared to the much later peak in the south in mid-July, while we are normally presented a single composite curve that suggests two pandemic waves have already happened in the US. What follows is my attempt at replicating the north-south comparison along with examining another distinction between coastal and interior states.
Figure 5: Classification of states into northern/southern or coastal/interior, and resulting COVID-19 “case” and “death” curves from March to October 2020. Data sources: The COVID Tracking Project at https://covidtracking.com/data/download, Natural Earth Admin1 boundaries at https://www.naturalearthdata.com/downloads/110m-cultural-vectors/110m-admin-1-states-provinces/
Based on a separation of 36 northern states (about 214 million people, including Washington DC) and 15 southern states (about 115 million people), detected COVID-19 “cases” (i.e. PCR test-positives) in the northern US (green) form two waves with peaks in April and July and are currently (end of October) rising far above those peaks (see Fig. 5). Note that I don’t relate these counts to issues with the testing strategy and test reliability discussed in other posts! The southern states (orange) had their first peak in July and currently only show a modest increase. The COVID-19 “death” counts (i.e. fatalities from any cause but with a positive PCR test result) present two distinct peaks for the two groups of states. This shows the current disconnect between case detections and fatal outcomes, and overall, a country as large as the US in terms of population and geography should probably not be analyzed as a unit nor be subject to nation-wide pandemic response policies.
Figure 6: Recency index of daily “cases” based on counts normalized per state – the darker the more recent “cases”. Data sources: The COVID Tracking Project at https://covidtracking.com/data/download, Natural Earth Admin1 boundaries at https://www.naturalearthdata.com/downloads/110m-cultural-vectors/110m-admin-1-states-provinces/
Another experiment led me to the second classification above. I was interested in checking whether there was a geographic pattern in the recency of Sars-CoV-2 spread across the US. For that, I normalized the daily new “cases” for each state by that state’s maximum daily count. Then, I multiplied the normalized values by an index for each day, ranging from 1 for January 22nd to 292 for November 8th. The sum of these products will be larger the more (relative) “cases” occur late within the time frame. The result (see Fig. 6) seemed to suggest that coastal states (broadly defined!) tend to have later peaks than interior states, thus the second classification into 29 coastal states (226 million people) and 22 interior states (102 million people). However, in looking at the blue-brown graphs in Fig. 5, it appears that there is a greater difference in total numbers than in seasonality. Both groups already had two peaks in cases and deaths, with the coastal states displaying much higher counts and the interior states currently “catching up” in terms of “cases”. This could be due to including New York with its large but early peak in the coastal group. All this again points to the need for more localized analyses and response measures.
In fact, the pandemic response in the US is decentralized with different state governments taking rather distinct routes. One tool for examining these differences quantitatively is the Oxford Covid-19 Government Response Tracker (OxCGRT). Researchers at Oxford University created an index to measure the stringency of lockdowns across the globe and within the UK and US. The stringency index is one of several indices documented at https://github.com/OxCGRT/covid-policy-tracker. It combines eight sub-indices representing “containment and closure policies” (e.g., scope of school closures, cancellation of public events, etc.) and one sub-index representing “health system policies” (H1 – presence and extent of public information campaign). On the maps in Fig. 7, I display the daily average stringency index using the thickness of “prison bars” on top of case and death rates per million state population.
Figure 7: United States “cases” and “deaths” compared to average stringency index. Data sources: Natural Earth, Oxford COVID-19 Government Response Tracker, The COVID Tracking Project, US Census Bureau
Overall, the US presents a patchwork of low-to-high stringency combined with different levels of “cases” and “deaths”. For example, Maine, New York, and New Mexico have the highest average stringency index values combined with case rates in the lower half but highly variable death rates, including New York with the second-highest death rate in the US (as of November 8th, 2020). As another example, Oklahoma and Utah seem to have gotten away with relatively lax government responses and low death rates, yet their case rates are in the medium range. Any conclusions from these maps need to be drawn with great caution as we cannot determine causality between the two aggregated variables, both as a general rule and here specifically due to the temporal component. For example, in the corona believers’ favourite scenario of a strict lockdown and a flat curve, did government response actually come first or was the epidemic curve already on the decline?
The scatterplots in Fig. 8 illustrate the same data numerically rather than geographically. I also asked Excel to plot a trendline based on the data points. The first graph shows that greater stringency (averaged over the duration of the pandemic) has a marked correlation with lower case rates. However, the second graph illustrates that greater stringency does is not associated with lower death rates; in fact, states with stricter lockdowns have a slight tendency to have higher death rates!
Figure 8: Average lockdown stringency index (time-weighted based on daily values from January 1st to November 8th) compared to COVID-19 “case” rates and “death” rates (per million residents) for the United States. Data sources: Oxford COVID-19 Government Response Tracker, The COVID Tracking Project, US Census Bureau.
For the reasons already noted, it would be premature to draw definite conclusions from these maps and graphs. Statistical and geospatial analyses based on aggregate data can demonstrate correlations between variables, and spatial associations between high or low values within and between variables, but not causality. They can however suggest the direction of additional research to detect the underlying causes of a phenomenon such as infectious disease spread. The data that I used here certainly raise questions about the magnitude of the Sars-CoV-2 pandemic in historical context, the geographic and seasonal patterns of the epidemic curve, and the proportionality of government response measures. In addition, all population-level COVID-19 data rely on the PCR test for the presence of Sars-CoV-2 in healthy and ill individuals, a test that is increasingly scrutinized worldwide for what it can actually tell us, and what it cannot. While these questions are further studied, we should use long-established public health practices and common sense to restore our free, democratic societies.
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.
To my knowledge these overly cautious assessments have been dispelled by recent research. For example, 80% to 90% of Sars-CoV-2 infections remain asymptomatic or show only mild symptoms, which must be due to existing cross-immunity from memory T cells; with an IFR now pegged at 0.27%, COVID-19 is in the ballpark of a severe flu cycle; and lockdowns in many countries have been shown to have no effect on the epidemic curve whatsoever. The Lancet letter also notes concerns about long-term impacts of Sars-CoV-2 infections, so called “Long COVID”. While it is too early in the pandemic to gauge the existence and magnitude of this risk, I believe that all respiratory diseases can lead to long-term illness in a small proportion of patients. The letter concludes with an invitation to co-sign the “John Snow Memorandum”, which calls for “continuing restrictions” in light of recent increases of COVID-19 case counts in Europe, North America, and elsewhere, and explicitly rejects approaches that include letting the virus spread through the healthy, non-vulnerable parts of the population.
Figure 1 (a) Queen Victoria and Dr. John Snow according to PBS historic TV series “Victoria”. (b) Map of fatalities during the 1954 cholera outbreak in London’s Soho district. Sources: https://www.townandcountrymag.com/leisure/arts-and-culture/a26102384/queen-victoria-john-snow-doctor-cholera-true-story/ and https://www.pbs.org/wgbh/masterpiece/specialfeatures/victoria-s3-e4-history-in-images/
Now you might ask, who on earth is John Snow? In PBS’s historic TV drama “Victoria” about the eponymous British queen, Dr. John Snow makes an appearance in Episode 4 of Season 3 (Fig. 1a). In a backgrounder about the show, Town & Country magazine introduces him with reference to his (almost-)namesake from the Games of Thrones series:
“Before there was Jon Snow, King of the North, there was a real-life hero of the same name. Back in 1854, a Dr. John Snow made a medical breakthrough that earned him the title ‘The Father of Epidemiology’, and his recent portrayal in the period drama Victoria has given us the excuse to celebrate his legacy once again.”
Chloe Foussianes in Town & Country magazine, 4 February 2019
Dr. Snow is in fact well-known to students not only of Epidemiology but also Geography and Cartography. He was a medical doctor and surgeon in London UK in the 1830s to 1850s. In addition to developing anaesthetic procedures for childbirth, he had taken an interest in the recurring cholera outbreaks in the city. In 1854, he identified the public water pump on Broad Street as the possible source of a deadly outbreak, after talking to affected residents and noticing a cluster of fatalities near the pump. Snow’s map with the deceased marked at their home locations is one of the most famous maps in Geography (Fig. 1b). According to Wikipedia, the outbreak may already have been on the decline when the local council agreed to Snow’s request to remove the pump’s handle, yet this decisive action is likely what made the Lancet letter authors adopt his name for their memorandum.
If I recall the episode of “Victoria” correctly, Broad Street residents actually tried to stop government agents from removing the pump handle as they liked the taste of the water and the convenient location. If we equate Dr. Snow’s intervention with today’s pandemic response, e.g. travel restrictions or limits on group sizes, we also see grassroots concerns and opposition to the authoritative measures. Interestingly, Wikipedia credits Snow’s findings for “fundamental changes in the water and waste systems of London, which led to similar changes in other cities, and a significant improvement in general public health around the world.” In other words, the importance of Snow’s contribution is not measured by the immediate crisis response but by evidence-based long-term solutions that address the challenge holistically. Indeed, epidemiology and public health are about healthy long-term living conditions for all, not about short-term solutions for some. Our epidemiologists better consider all aspects of global health rather than stare down one select virus that isn’t even that exciting!
Figure 2. Florida Governor Ron DeSantis introducing the experts invited to a virtual roundtable about the state’s COVID-19 response, 24 September 2020. Source: Screenshot from video posted at https://rationalground.com/governor-desantis-roundtable-experts-advocate-for-normal-life-for-young-people/, which also has a transcript of the two-hour session.
In addition, 150+ years ago the authorities may have been expected to take drastic measures without consultation. Today, we have come to expect better communication from political decision-makers and their respect of individual rights and freedoms. In this context, I was hugely impressed by Florida governor Ron DeSantis. DeSantis held a virtual roundtable with infectious disease experts Drs. Jay Bhattacharya, Martin Kulldorff, and Michael Levitt on 24 September 2020. The impressive part was that this high-ranked politician personally led the conversation with these eminent academics in a purposeful way, demonstrated keen knowledge of COVID-19 related studies from around the globe, and even acted as a facilitator for questions from the media representatives in the room. I could not help but feel that none of the Canadian or German politicians that I know would have been able to pull this off. In addition, DeSantis decided the following day that the state would enter the final “reopening” phase, essentially dropping all restrictions on normal life for the majority of residents. He thereby demonstrated that politicians can listen to “the science” after all. Note that I had never heard of DeSantis before, and that I disagree with most of his other political positions.
Figure 3. The authors of the Great Barrington Declaration in an interview with alternative news site UnHerd. Source: screenshot from https://unherd.com/2020/10/covid-experts-there-is-another-way/
Two of the Florida roundtable participants, Drs. Bhattacharya and Kulldorff from Standford and Harvard universities, along with Dr. Sunetra Gupta from Oxford University, initiated the Great Barrington Declaration about two weeks later. The Declaration recommends “focused protection” of vulnerable groups rather than general restrictions of the healthy, that is, returning to the approach that was the essence of pandemic response planning in most countries prior to this year. The John Snow Memorandum outlined at the beginning of this post was likely written in response to the Great Barrington Declaration, as the latter is often misrepresented as arguing for a “herd immunity strategy”. Herd immunity is the situation where enough members of a community have acquired natural immunity from a resolved infection or immunity from vaccination to prevent further epidemic spread of a disease. It therefore cannot be characterized as a “strategy”, but it is the outcome of any epidemic, though different public health responses can yield herd immunity sooner or later, with direct implications on the death toll and collateral damage. For example, I recently saw a research report that found higher COVID-19 fatalities caused by lockdowns – in addition to the collateral damage (non-COVID deaths) also attributed to lockdowns.
My knowledge of Game of Thrones if severely limited to Season 8 of the TV adaptation, but my understanding is that Jon Snow, King in the North, worked selflessly to unite the seven kingdoms in a joint fight against a terrifying external enemy, the Night King’s army, despite long-standing animosities between the royal families of Westeros. Maybe we need a Jon Snow Memorandum rather than a John Snow Memorandum today? Winter is coming…, and with it, we can expect a new wave of respiratory illnesses, mostly likely including COVID-19 or its next genetic strain. Nevertheless, if acute-care physicians and nurses, health-care managers, public health officers, epidemiologists, immunologists, microbiologists, virologists, modellers, data scientists, and other experts could set aside their idiosyncrasies and work together, we might be able to return to our old normal along with monitoring of public health metrics that are already in place, ambulatory and hospital capacities ready to be activated and expanded, all treatment options seriously examined, immune system welfare and prevention in focus, and a generally more relaxed attitude towards Sars-CoV-2.
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.pdfFigure 1b. Weekly incidence of doctors’ visits per 100,000 persons for acute respiratory illness in Germany. Rate is shown by age groups (coloured curves) and total population (dotted black curve). Source: https://influenza.rki.de/Wochenberichte/2019_2020/2020-39.pdf
The survey data are extrapolated to the population of Germany and move within a weekly range of about 1% to 10% of people having a flu-like illness. The 2019/20 curve (blue line in Fig. 1a) peaked in weeks 9 and 11, around February 26 and March 11, 2020, at a level comparable to the two previous years, and dropped very quickly to a level markedly lower than the previous years. The third quarter of 2020 (black line) had a very similar trajectory to all three previous years. A similar seasonal pattern can be observed from the doctors’ consultation index shown by age group, with young children aged 0-4 years (red line in Fig. 1b) being over-represented and people 60 and over being under-represented. Note that these data are presented as rates out of 100,000 in the same age group, with the dotted black curve being the rate in the entire population. Overall, these data illustrate that 2019/20 was not a particularly severe year for respiratory diseases in Germany. Note that pandemic response measures started in week 11 and a lockdown — light by international comparison — was implemented in week 13, coincident with a burden of respiratory disease that had already plummeted.
Figure 2a. Number of ambulatory-care sentinel samples submitted per calendar week (left axis, grey bars) and test positivity rates for different virus families (right axis, coloured curves). Source: https://influenza.rki.de/Wochenberichte/2019_2020/2020-39.pdfFigure 2b. Weekly number of hospitalizations for severe acute respiratory illness and proportion of those also diagnosed with COVID-19, based on 69 sentinel hospitals. Source: https://influenza.rki.de/Wochenberichte/2019_2020/2020-39.pdf
The sentinel data show the number of samples received (Fig. 2a, left axis and grey bars) and the test positivity rates for five groups of respiratory viruses (right axis and coloured curves). Influenza viruses dominated the winter months with up to about 50% positivity, yet interestingly Rhinoviruses took over in the summer months with rates up to 80% positive. Interestingly, the Rhinovirus wave started about 4-5 weeks after orders to wear masks or face covering in public-access indoor settings were put in place. In contrast, Sars-CoV-2 is too insignificant in the sentinel program to be visible on the graph, and the endemic coronaviruses causing the common cold were not considered important enough to be included either.
Table 2 of the RKI report includes data for the entire 2019/20 flu season between week 40 (October) of 2019 and week 39 (September) of 2020. A total of 4,625 sentinel samples were submitted, of which 2,283 contained a respiratory virus. Influenza A or B were detected in 918 samples while Rhinoviruses were found in 827 samples. Since week 8 of 2020, the samples were also analyzed for Sars-CoV-2, yet only 14 of all 4,625 samples (0.3%) had the novel coronavirus. Of those, 13 were from weeks 11 to 15 (March 9 to April 12), with not a single Sars-CoV-2 detection since then until week 39 (September). This is why lockdown critics in Germany declared the pandemic over in mid-April. Media fact checkers disputed this conclusion with reference to the low numbers of submitted samples. Given that the flu surveillance programs are the authoritative approach to monitoring the public health threat from the annual flu cycle, discrediting its accuracy when the results do not fit the narrative of the COVID-19 pandemic appears to be rather disingenuous.
However, a look at a final graph from the RKI report (Fig. 2b) tells us that Sars-CoV-2 did not entirely disappear, as it was still present in patients hospitalized with severe acute respiratory disease throughout the 2020 spring and summer. The total number of hospitalizations in the 69 reporting hospitals declined from over 500 in March to around 100 in May/June and rose again to some 200 in September. However, COVID-19 diagnoses plummeted from near 30% to below 5% and remained there until the end of the 2019/20 reporting year. Therefore, Sars-CoV-2 did not entirely disappear (and it has in fact come back in hospital reports for October 2020), yet the proportion of COVID-19 — you know, the actual illness! — among respiratory disease patients in ambulatory or hospital care in Germany never looked significant or worthy of a pandemic based on these data!
Moving across the big pond, Canada’s respiratory disease surveillance program is called FluWatch. Like the German program, FluWatch has a number of reporting avenues that I do not want to detail here, please see https://www.canada.ca/en/public-health/services/diseases/flu-influenza/influenza-surveillance.html instead. We will look at two graphs from two final reports by the Public Health Agency of Canada (PHAC) on flu and other respiratory diseases, respectively, for the 2019/20 cycle. The graphs illustrate the seasonal patterns of influenza types (Fig. 3a) and other respiratory viruses being monitored (Fig. 3b) for the 2019/20 cycle. Note that the test positivity rate is included with the flu reporting, as would have been appropriate to do from early 2020 for COVID-19 PCR test results too.
Figure 3a: Number of positive influenza tests and percentage of positive tests, by type, subtype and reporting week, Canada, weeks 2019/35 to 2020/34. Source: https://www.canada.ca/en/public-health/services/publications/diseases-conditions/fluwatch/2019-2020/weeks-30-34-july-19-august-22-2020.htmlFigure 3b: Number positive laboratory tests for other respiratory viruses by reporting week, Canada, 2019-2020. Source: https://www.canada.ca/en/public-health/services/surveillance/respiratory-virus-detections-canada/2019-2020/week-34-ending-august-22-2020.html
The second graph includes “coronavirus” in grey, with a mid- to late-season distribution with up to around 15% of all non-influenza viruses found, or some 5% to 7% maximum if flu is included. The corona curve declines along with several other viruses and visually disappears around week 20. In the same PHAC report, the data table associated with the graph confirms that coronavirus detections are in the single digits from week 23 to the end of the cycle (week 34). Importantly however, the report includes a disclaimer that coronavirus here refers to the “normal”, seasonally recurring human coronaviruses, not to Sars-CoV-2. Therefore, we cannot deduct that the COVID-19 pandemic has ended in Canada, at least not from these data. Instead, we should ask the question, why PHAC did not add Sars-CoV-2 to the set of viruses monitored under the FluWatch program, since this would ensure comparable laboratory testing and allow for important comparisons with our well-known respiratory viruses.
Figure 4: Seasonal variation of selected upper respiratory tract infection pathogens. Chart adapted from Meneghetti (2020) at https://www.medscape.com/answers/302460-86798/what-are-the-seasonal-patterns-of-rhinoviral-coronaviral-enteroviral-and-adenoviral-upper-respiratory-tract-infections-uris
The chart in Fig. 4 (adapted from Meneghetti 2020) illustrates the seasonal fluctuations (presumably for the northern hemisphere) of the respiratory viruses that we humans have adapted to live with. It is reminiscent of a pollen calendar, though note that in contrast to pollen exposure we are subject to viruses year-round without respite! The chart reiterates the message that waves of respiratory disease pathogens come and go in an annual cycle, as seen in the Canadian (Fig. 3b) and German (Fig. 2a) flu surveillance programs.
The following map is based on the data presented in Table 2 of the Canadian “Respiratory Virus Report, week 34 – ending August 22, 2020” via https://www.canada.ca/en/public-health/services/surveillance/respiratory-virus-detections-canada.html. I attempted to replicate the colours used for each virus family in Fig. 4. Across Canada’s provinces, there is relative consistency in terms of the proportions of different lab-confirmed pathogens, with Influenza A and B making up between one third to over half of detections, and respiratory syncytial virus (RSV) and Entero- and Rhinoviruses contributing another one-fifth each. Note that Quebec does not seem to report the Entero/Rhino category, thereby skewing the proportion of the remaining viruses, and Alberta reported zero RSV for the year, leaving more “room” for other viruses, in particular Entero/Rhino. The pie charts are sized in proportion to the number of samples with at least one virus detected, a number ranging up to over 20,000 for Quebec. I am not providing the specific counts, since the surveillance program is based on a selection of laboratories and the data were not extrapolated to represent the entire Canadian population.
Figure 5. Prevalence of confirmed respiratory virus infections by province, total for 2019/20 reporting year based on select participating laboratories. Data source: Public Health Agency of Canada, Statistics Canada
So, when did the Sars-CoV-2 pandemic end in Canada and Germany? I am not sure that the national respiratory disease surveillance programs we looked at can answer this question. However, they help answer an even more fundamental question: Is there a Sars-CoV-2 pandemic at all? I am not a medical scientist or practitioner, nor an epidemiologist or immunologist or virologist or public health expert. But I do understand data and have a healthy dose of common sense. The relative magnitude of Sars-CoV-2 and COVID-19 in these and other datasets such as national and global mortality statistics do not support the view that 2020 is qualitatively different from the last two or three decades in terms of our respiratory disease burden. Instead, we seem to have forgotten that life and also death with viruses is part of our existence on planet earth. We need to redirect our fear-stricken gaze away from COVID-19 and tackle the complexities of the corona crisis and all the other challenges our society is facing using science, collaboration, and above all common sense.
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 choropleth map uses geographic areas, e.g. the polygons representing Canada’s provinces and territories, as the map symbol, by shading an entire area with a colour based on an associated data value. We can see this on our public broadcaster’s web site https://newsinteractives.cbc.ca/coronavirustracker/, where the CBC provides an interactive map as part of their “coronavirus tracker”. A red colour scheme is used to shade the provinces in proportion to the total number of COVID-19 cases that were confirmed since the beginning of the pandemic. For example, Quebec’s dark red represents over 100,000 cases while Ontario’s rose colour symbolizes around 75,000 cases.
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!
Embarrassingly, the web site of the Canadian Broadcasting Corporation (CBC), our public-service radio and TV network, is the last major news platform that still has not modified their COVID-19 map to use a suitable map projection. The following figures illustrate the difference in the size and shape of mapped areas between the Web Mercator projection on the left, which is used by the CBC’s mapping tool, and the Lambert Conformal Conic projection on the right. The differences become larger the more north you go. While the southern provinces are reasonably well represented under both projections, the northern territories appear more and more bloated the closer we get towards the North Pole. In fact, the CBC conveniently erased Canada’s Arctic Archipelago with Ellesmere Island from its map (see above) to cut off the most mis-shaped area in the far north!
Comparison of map projections for Canada: (a) Web-/Pseudo-Mercator, (b) Lambert Conformal Conic. Data source for provincial boundaries: Statistics Canada
A second major gaffe in the CBC’s corona map is the use of choropleth symbology for raw-count data such as total COVID-19 cases. We have already reviewed in detail at https://gis.blog.ryerson.ca/2020/03/26/the-graduated-colour-map-a-minefield-for-armchair-cartographers/, why the graduated colour map is more sophisticated than it looks. That is due to the nature of its cartographic symbols being identical to the underlying geographic areas with their different sizes. These sizes can have an undue influence on any statistic collected for each area. For example, we do not know how much of Quebec’s high COVID-19 case count on the CBC map is due to the size of the province (in terms of surface area and/or population) and how much is due to the actual spread of the disease. To overcome this issue, we need to normalize raw-count data by a suitable reference value. If we normalize by area, we arrive at a density variable, e.g. population density as the number of people in each spatial unit divided by its surface area. If we normalize by total population, we obtain a rate, e.g. COVID-19 prevalence as the number of cases within a unit divided by the number of people residing in the unit. Prevalence is often expressed as a rate out of a large number of residents, e.g. X cases per million people, or as a chance, e.g. one case in Y people.
I will use two maps from the web site and data repository OurWorldInData.org to illustrate the need to work with relative metrics. Below, on the left, you see a raw count variable, cumulative COVID-19 cases, mapped by countries as of November 2nd. On the right, the case counts were put in relation to total population by creating a normalized variable, cumulative COVID-19 cases per million people. One of the more obvious differences between the two maps concerns India and Russia. Based on raw case counts, India has clearly more cases than Russia. But based on the relative metric, Russia has more cases per million than India. The “lie” in the raw-count map is based on the fact that it suggests a greater risk of infection in India while arguably the risk is greater in Russia as you are more likely to run into an infected person. (Note that this reasoning is for illustration only, as it relies on the assumption that confirmed “cases” actually have a meaning in terms of infectiousness, which is debatable, and that testing regimes capture a sufficient number of infections, which is almost certainly not the case.)
Screenshots from https://ourworldindata.org/coronavirus
Believe it or not, at this point there are still three important concerns with choropleth maps that I want to discuss: (1) the misuse of alarming red colour schemes, (2) the misleading portrayal of large areas (provinces, countries) as homogenous, and (3) the arbitrary classification of the data values. All three issues were addressed in a series of articles for the Canadian Geographic magazine written in April by their eminent cartographer Chris Brackley (https://www.canadiangeographic.ca/author/chris-brackley). The issue of “sensationalist colour choices” when mapping the coronavirus was also discussed as early as February 25th by cartography wiz Kenneth Field at https://www.esri.com/arcgis-blog/products/product/mapping/mapping-coronavirus-responsibly/.
The juxtaposition of the world maps above demonstrates the potential impact of colours. Many cultures associate red with threats, risk, vulnerability, and other negative emotions and outcomes. In thematic mapping, we use lightness progression to represent the magnitude of a phenomenon, and typically the darker red a place is depicted the worse the situation. One of the above maps from OurWorldInData.org presents is an example of an ominous, blood-red COVID-19 map. However, their other map of normalized COVID-19 cases per million people uses less alarming blue colours. This graduated colour scheme still has a very dark tone at its high end, and the blue hue is not meaningfully associated with infectious disease (as far as I can tell). Therefore, I am using shades of grey for my map of COVID-19 case rates by province, as grey is certainly the most neutral colour option (and it is printer friendly as an added benefit).
Data sources: Esri Canada, Statistics Canada
Much of Canada’s population is concentrated in a narrow band near the border with the United States, and the provinces thus have a highly uneven (inhomogeneous) population distribution within their boundaries. Therefore, any population-related phenomenon such as a human infectious disease is improperly mapped if the cartographic symbols suggest that it occurs equally across urban and agricultural areas as well as the vast Canadian wilderness. The same would apply to a city map, where population should not be mapped within major parks or water features. To assist with displaying national-scale data where people actually live, Statistics Canada is offering the “Population Ecumene” dataset documented at https://www150.statcan.gc.ca/n1/pub/92-159-g/92-159-g2016001-eng.htm and shown through the semi-transparent red areas on top of the crowdsourced OpenStreetMap in the overview map below.
Data sources: OpenStreetMap, Statistics Canada
Using the inhabited areas as a mask, I can reduce the map symbols of my map to the places where COVID-19 actually occurs with any likelihood. Note that in other instances, where the mapped variable is dependent on the surface area, e.g. when visualizing population density, the values would need to be recalculated to the smaller ecumene areas.
Data sources: Esri Canada, Statistics Canada
Classification is the final aspect of how to lie with COVID-19 maps that I want to explore today. You can see in the above maps from OurWorldInData.org that the countries’ values are grouped into ranges, e.g. starting with 0-10 cases per million mapped with the lightest blue, followed by 10-50 cases p.m. with the next-lightest shade, and so on. The map-maker chose “nice” round class breaks, but hidden behind these is a pattern of exponentially increasing intervals. For example, the range of values grouped into the fifth class (500-1000) is ten times the range of values grouped into the third class (50-100). Their map of raw case counts has an even more abrupt increase in the last two classes, as shown in the red line of the following graph (note that the two lines each have their own y-axis).
My previous map above also uses a classification that progresses faster than linear. This is not necessarily “wrong” but we need to be aware that data classification occurs and that it can be used to influence the message of a map. At this point, we should credit CBC for one aspect of its COVID-19 map: they avoid classification issues by using an unclassed choropleth map. In the CBC map reproduced at the beginning of this post, note how the colour for each province is picked from a continuous, linear progression of shades from light to dark (red).
Confirmed COVID-19 cases per million population mapped for inhabited land portion of Canada’s provinces, shaded in relation to an international benchmark value of 30,000. Data sources: Esri Canada, Statistics Canada – values as of 22 October 2020.
My final map version employs the same unclassed approach using grey shades. Note that the legend symbols now do not represent class breaks but are just sample colours taken from the linear progression from light (white) to dark (black). In addition, I set the maximum value not to the largest value in the dataset but to a meaningful benchmark, the value of 30,000 COVID-19 cases per million that the United States are currently approaching. Of course, even this “large” value represents only 3% of the population. The subdued map appearance hopefully conveys the still limited scope of the Sars-CoV-2 “pandemic”. Now who would have known that shades of grey could be this sexy?
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.
At the time of writing, the November 2020 United States presidential election is less than 10 days away. The televised debates between the incumbent Donald Trump and main challenger Joe Biden was seen by many observers as a demonstration of a post-truth era, in which anyone can make any claims to scientific evidence, and facts be countered with “alternative facts” in the words of Trump’s former counsellor Kellyanne Conway. Extending the presidential face-off, a proxy debate is shaping up in the media between Trump’s newly appointed coronavirus advisor Dr. Scott Atlas and the director of the National Institute of Allergy and Infectious Diseases Dr. Anthony Fauci. In response to accusations of promoting falsehoods, Atlas has been referring explicitly to “the” (other) science, for example with respect to keeping schools open during the COVID-19 pandemic.
Tweets by Dr. Scott Atlas referring to science, facts, and truth about school re-opening
Fact is, there are good reasons to close schools, and better reasons yet to open them. As places of congregation of individuals from numerous families, it is logical to worry that schools could contribute to the spread of Sars-CoV-2 in a population. While the risk of serious disease from this virus has been known to be minimal for young ages, the concern was that children may infect more vulnerable persons among their teachers and family members at home. To my knowledge, recent studies like the Yale University research cited by the Wall Street Journal in Dr. Atlas’s tweet showed little risk for education or child care services to be sources of transmission. After reopening with strict testing protocols, there have been numerous positive Sars-CoV-2 tests among American college students, yet there were virtually no serious illness or deaths reported. Common sense thus suggests that letting the virus spread among healthy young individuals, as we have done every year before 2020, contributes to building herd immunity in a population.
With respect to acquiring immunity, some pundits doubt that a first infection with Sars-CoV-2 actually conveys immunity towards future infections. This seem silly to me – it’s just a coronavirus after all! However, like with all respiratory viruses we should expect Sars-CoV-2 to mutate, so future (re-)infections will be a matter of what the our tests will detect, or how (even, whether?) we want to monitor respiratory diseases as closely as in 2020 going forward.
October 21, 2020, edition of Markus Lanz talk show on German public TV channel ZDF (screenshot from https://www.zdf.de/gesellschaft/markus-lanz/markus-lanz-vom-21-oktober-2020-100.html)
Another ongoing example of the ever-changing science, 2020 edition, applies to protective masks and other face coverings. In an October 21st public TV talk show, moderator Markus Lanz could not believe his ears, when the president of the German Medical Association noted concerns with the effectiveness and side effects of wide-spread use of face masks (a statement recanted the following day under pressure from politicians and the media). In response, the moderator contrasted scientific evidence with personal opinion, and thereby ignored the essential step of an individual’s interpretation of evidence that may lead to diverging conclusions. When another show participant defended the principle of open discussion of a topic such as mask effectiveness, the Mr. Lanz argued that masks appeared to him as a topic that we should not be discussing any more as a society. The debate also touched on the point that evidence of effectiveness from lab-based studies may not directly translate to the everyday use of make-shift face coverings, which illustrates the vast range of possible conclusions that can be drawn from the same piece of scientific evidence.
The contentious issue of masks provides another example of science vs science. The story of the “Danish mask study” currently makes the rounds in alternative and some mainstream media, after investigators suggested that major medical journals are afraid of publishing their results, and the community suspects that it is because the results put the effectiveness of masks into question. The study with the full title “Reduction in COVID-19 Infection Using Surgical Facial Masks Outside the Healthcare System” is listed in the US National Library of Medicine’s clinical trial database (clinicaltrials.gov). In an extensive randomized trial, it examined COVID-19 outcomes in a group of healthy, working adults who wore face masks whenever they left their home, in comparison with a control group who also followed public health guidelines but did not wear masks where not required. At present, it is not clear whether the rejections from the Journal of the American Medical Association, Lancet, and New England Journal of Medicine were based on the normal peer review process and/or an editorial decision. But the suspicion alone that leading academic journals could reject important, timely research based on its controversial results is damaging to the very foundation of science.
Tweet by Dr. Kulvinder Kaur Gill, Brampton, Ontario
We shall conclude today’s exposé with a look at a thought-provoking tweet from Ontario doctor and lockdown critic, Dr. Kulvinder Kaur Gill. As Sweden’s response to COVID-19, which avoided most lockdown measures and let residents continue to live a mostly normal life, has come under fire again as a “failed experiment”, Dr. Gill reminds us that the experiment is what was done in all other countries but not Sweden. Isolating the infected and the vulnerable was long-established public health practice at times of infectious disease epidemics. By contrast, restricting or locking down the healthy has never been tested in the history of humankind, and it is more and more clear that lockdowns generate much unanticipated side effects in the form of missed medical examinations and treatments, mental illness, poverty, and hunger around the globe.
As a specialist in decision support systems, I am pleased to see increasing public acknowledgements of science as a foundation for important societal decisions to be made. However, decision-makers and the public need to be aware of the process and limitations of science as well. Scientist have an important role to play here, in particular by remaining cautious and restrained with communicating their own findings and interpreting other research results. One might also wish for scientists to hold a good portion of common sense and take a broader perspective on the possible implications of “their” science.
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.
Context and “first responders”
Among the first experts worldwide to raise concerns about the global panic reaction to the “killer virus” Sars-CoV-2 were Dr. Wolfgang Wodarg and Dr. Sucharit Bhakdi. Dr. Wodarg is a retired physician specializing in internal medicine and hygiene, who also served as medical officer of health and later as public health politician with the centre-left party SPD. He is particularly well known for his contributions to uncovering the role of the pharmaceutical industry in the 2009 swine flu scandal, as documented in the public TV film “Profiteers of Fear” [1], which shows eerie similarities between the 2009 and 2020 situations. The earliest post on Wodarg’s web site is dated March 1st, 2020, and tellingly called “Corona-Hype: Without PCR-Tests there would be no reason for special alarms.” Throughout his web site [2], Facebook account [3], and interviews with alternative media, Wodarg’s general stance is to reassure the public that individual and collective risks from this virus are in line with our normal exposure to respiratory viruses.
Similarly, Dr. Bhakdi, a retired, widely cited professor of immunology and microbiology, has been trying to instill more composure and comprehensive situation awareness into the debate. As early as March 29th, 2020, he posed five questions to German chancellor Merkel, asking about (1) the distinction between asymptomatic, infected and ill patients; (2) hospital usage in relation to past experiences with other coronaviruses; (3) representative studies of the actual spread of Sars-CoV-2 among the population; (4) distinction of deaths from COVID-19 from deaths with Sars-CoV-2; and (5) better public communication of the limited comparability of specific international COVID-19 hot spots with the situation in Germany. The Youtube video [4] has been watched close to 2.5 million times. Most notably, Bhakdi together with his wife and colleague Dr. Karina Reiss wrote a 150-page book [5] providing “data, facts, backgrounds” on the pandemic. Despite the question mark in the title “Corona false alarm?”, they describe the global Sars-CoV-2 response as a foolish and costly false alarm. The book was the German no. 1 non-fiction paperback bestseller for 13 consecutive weeks from June to September 2020.
Fig. 1: Infectious disease specialists and early critics of fear-mongering and government over-reaction to Sars-CoV-2 — Dr. Wolfgang Wodarg, Prof. Dr. Sucharit Bhakdi, and Dr. Bodo Schiffmann (Sources: modified screen captures from [2], [4], [7])
Political opposition, rallies, and the role of lawyers
Following in these footsteps was Dr. Bodo Schiffmann, a practicing otolaryngologist, who started an extensive video series in late March discussing the official COVID-19 data and the mismatched media reporting and political decisions taken. Schiffmann’s videos were repeatedly censored by Youtube which led him as one of the first corona critics to adopt the Bitchute [6] and Telegram [7] platforms. He was also one of the first to call for a new party, as there was no political opposition to the restrictions of constitutional freedoms rammed through federal or provincial parliaments. Schiffmann joined two others to found the new party “Widerstand2020” [“resistance 2020”, 8] based on four principles including freedom, constrained political powers, loving togetherness, and collective intelligence and problem-solving. The party web site famously showed over 100,000 registrations within days of its launch, although this number was never confirmed. Instead the founders disagreed on how to proceed on a legal, technical, and professional basis and Schiffmann regrouped to form a different new party, “WIR2020” [“we 2020”, 9] based on mindfulness, individual responsibility, and living democracy. WIR2020 appears to be developing slowly, although Schiffmann’s recent focus has shifted towards direct personal outreach at rallies and through the “corona info tour” [10], which currently takes him and Swiss entrepreneur and youtuber Samuel Eckert through Germany’s cities.
The first lawyer to publicly criticize the pandemic response measures as out-of-proportion, denouncing them as “coronoia”, was Beate Bahner [11]. On April 8th, 2020, she submitted an unsuccessful appeal to Germany’s Federal Constitutional Court requesting the cancellation of all provincial executive orders due the extent to which they suppressed individual freedoms and civil rights. Pro-freedom and anti-lockdown rallies across Germany started as grassroots initiatives by different civic groups, the best-known of which is Querdenken [“cross thinking”]. Querdenken711 [12] identifies the locale in Stuttgart founded by entrepreneur Michael Ballweg. The August rallies in Berlin drew between 20,000 and over one million participants, depending on the source. The fight for court approvals of rallies and the negotiation of required “hygiene concepts” with city officials is one of the points, where activist lawyers have become hugely important to the corona opposition. The best-known individuals are Ralf Ludwig [13], who also co-led the founding of the party Widerstand2020, and Markus Hainz [14], who both achieved the reinstatement of rally permits, manageable distancing and mask requirements at rallies, as well as modifications of mask requirements for students in some schools throughout the country. Around 60 lawyers participate in the non-profit KlagePATEN [“lawsuit patrons”, 15], which supports individuals fighting against mask orders, face coverings and PCR testing in schools, as well as restrictions on street protests.
Another major initiative founded by a group of four lawyers is the Corona-Ausschuss [“corona commission”, 16] based primarily in Berlin. They started with a petition for a base line study to collect representative data about the Sars-CoV-2 spread and prevalence. From that unsuccessful beginning, the group announced the commission on July 10th, 2020, [17] and moved swiftly to conduct, record, and publish interviews with over two dozen national and international experts across numerous fields of study including for example psychologists, economists, and publicists in addition to health scientists and practitioners. The commission examined the question whether the pandemic response was suitable, necessary, and proportionate, as required by public health laws, and whether the collateral damage was caused by gross negligence on the part of federal and provincial decision-makers. A preliminary report published September 14th, 2020, [18] finds that the threat of Sars-CoV-2 was significantly over-estimated, the collateral damage from the response measures not sufficiently considered, and governments are to blame for missing risk assessments. Using the work of the commission, one of the lawyers, Dr. Reiner Füllmich [19], is preparing a class action lawsuit to obtain compensation from the inventors and promoters of the PCR test for COVID-19, including the World Health Organization, which he blames for misleading politicians and public opinion in favour of a disproportionate, harmful pandemic response.
Fig. 2: Medical lawyer Beate Bahner, administrative lawyer Ralf Ludwig, and consumer protection lawyer Dr. Reiner Füllmich (Sources: modified screen captures from [11], [13], [19])
Other lobby groups and expertise
In addition to KlagePATEN, other emerging advocacy and peer support groups include Mediziner und Wissenschaftler für Gesundheit, Freiheit und Demokratie [“medical professionals and scientists for health, freedom, and democracy”, 20], Ärzte für Aufklärung [“doctors for enlightenment”, 21] with over 2,000 named supporters, Pädagogen für Aufklärung [“teachers for enlightenment”, 22], and Eltern stehen auf [“parents stand up”, 23], to name but these few. The doctors group also conducts another inquiry, the Außerparlamentarischer Corona Untersuchungsausschuss [“extra-parliamentary corona enquete commission”, 24] under the leadership of Dr. Heiko Schöning. One of their hearings is with a police officer who spoke at a rally and was subsequently suspended and harassed. Other public servants who were sidelined after expressing dissenting views include an employee of the federal ministry of the interior who wrote a report on the ongoing crisis mismanagement and a municipal Green Party politician who called for better informed decision-making. Two additional experts shall be mentioned as representatives of the possible contributions of their disciplines to the discussion: Austrian psychiatrist Dr. Raphael Bonelli analyzes the corona crisis from the perspective of behavioural psychology with the stated goal to de-escalate among rising hostility between government supports and critics [25], and German philosopher Gunnar Kaiser shares pointed observations, interviews, and reflections on his Youtube “TV” channel [26].
One of the earliest level-headed voices in the debate came from a business group, Corona-Initiative Deutscher Mittelstand [“corona initiative German SMEs”, 27]. Shortly after the start of lockdowns in Germany, they presented an exit plan based on key metrics including hospitalization and ICU rates as well as test positivity rates that were not (and still are not) sufficiently considered by political decision-makers. Based on government statistics and select research studies, they also put the corona pandemic into historical context and attempted to quantify the collateral damage from lockdowns, including mounting concerns around abuse of women and children as well as growing suicide reports. Surprisingly, this is the only business-based initiative known to me that argues for less invasive and more targeted pandemic response.
Fig. 3: Psychiatrist Dr. Rafael Bonelli, philosopher Gunnar Kaiser, and economist Prof. Dr. Stefan Homburg (Sources: modified screen captures from [25], [26], [34])
Mainstream/social media censorship and alternative media
In addition to risking their careers and livelihoods, many of the dissenting individuals and groups also face censoring and shadow-banning from social media platforms, not to mention personal attacks and threats by other social media users. For example, numerous videos were removed from Youtube when they were found or reported to contradict current WHO guidelines or general directives such as not to compare COVID-19 with influenza — despite the fact that the video contents were often based on government statistics and solid scientific evidence. Until early October, the public broadcasters and established newspapers in Germany, Austria, and Switzerland followed the government narrative to a tee. This has allowed some new and existing alternative media sites to gain traction, including but not limited to Achse des Guten [28], Bittel TV [29], Blauer Bote [30], KenFM [31], and Rubikon [32]. Another example is Punkt.Preradovic [33], a Youtube channel in which semi-retired journalist Milena Preradovic published a series of interviews in April and early May with economics professor Dr. Stefan Homburg [34], who showed that the German lockdown had no effect on the trajectory of Sars-CoV-2 infections. Homburg has since become a major representative of the corona opposition and, along with Dr. Bhakdi, is participating in the Corona-Qu4rtett [“corona foursome”], the first open discussion forum on a mainstream TV channel, Austria’s ServusTV [35].
Other German-speaking health scientists and practitioners who raised their concerns via alternative media or personal web sites include microbiologist Dr. Martin Haditsch, immunologist Prof. Stefan Hockertz, oncologist and internist Dr. Claus Köhnlein, psychologist Prof. Christof Kuhbandner, immunologist Prof. Beda Stadler, and psychologist and research methodologist Prof. Harald Walach. Their assessments were borne out of decade-long experience and/or COVID-19 data analyses, yet were often discredited by superficial and clearly biased “fact checkers” employed by public broadcasters and social media companies.
The mainstream media and politicians from the conservative/social-democrat government and nominal opposition parties (Green Party and The Left) were also quick to denounce the “corona deniers” as extreme right-wing activists, nationalists, and worse. This view may be partially based on the fact that the right-wing Alternative for Germany and some neo-nazi groups expressed their support for anti-corona rallies. However, based on eyewitness reports, live video, and public statements from organizers and participants, leading grassroots organizations such as Querdenken711 represent a wide spectrum of concerned citizens, from conservative to progressive and apolitical individuals united in a fight for democratic freedom and an end to what they consider unjustified, totalitarian control. In fact, at the Berlin rallies it was reported that rally participants and Antifa counter-protesters shouted “nazis out” at each other!
It is informative to follow the corona crisis in the German- as well as English-speaking worlds, and I would like to help bridge the awareness gap between the two language communities. Lack of time prevented me from also monitoring French and Spanish sources. However, the language I truly wish I could read and understand at this time is Swedish!
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.
Geographic scales play an important role in the ongoing corona crisis too. Sars-CoV-2 and COVID-19 statistics are being mapped by country at the global scale and by state or province at the national scale. You will also find within-city maps such as the “City of Toronto COVID-19 Summary” by neighbourhood shown below, which exemplifies two typical communication errors:
Toronto Public Health’s map conflates cases in the community with those from institutional outbreaks, which are assigned to the neighbourhood that the institution (e.g. longterm-care home) is located in, while the processes leading to transmissions and the resulting policy decisions are not comparable, see Chris North’s June 16th analysis of an earlier version of this map at https://storymaps.arcgis.com/stories/bd9104535000442ca2fb64a0f396712a.
In addition to refining our scales of observation, analysis, and policy-making when it comes to institutional settings such as longterm-care homes or schools, much of the medical research around COVID-19 is scaled further down to the individual person’s level or to even more granular detail such as the cell level. While trying to find commonalities within groups of subjects, the researchers examine individual responses of the human body to the virus; mechanisms of individual transmission or immunity; or people’s behavioural responses to emergency orders, to name a few examples. In contrast, epidemiologists will generally study populations using aggregated data and/or mathematical models to describe issues like disease spread and predict its future development.
With COVID-19, as with many other news topics, there are numerous threatening findings and personal horror stories to choose from. It is my impression that the media reporting and images focus on local hardship, such as an individual with extended illness or an overloaded hospital or an outbreak in a neighbourhood school. The more benign overview of the state of the pandemic is not presented with the same intensity, and in addition, metrics, graphs, and maps are often based on raw counts and cumulative data – in essence the combination of the individual stories – rather than putting the counts into perspective. I have already written about the lack of data normalization and issues with classifying data such as the deaths from, or with, COVID-19. Maps in particular may not support a proportionate situation assessment, as their appearance depends largely on value ranges and the cartographer’s classification and symbol choices.
Individual stories are typically negative, since newspaper readers and TV viewers do not seem interested in positive news. Recipients who prefer concrete stories over abstract data will therefore perceive only negative information. This bias likely includes journalists who then amplify this perspective by focusing on cautious, risk-averse expert opinions confirming simplistic messages such as “every life counts”. I therefore contend that the scale of perception, at which we consume information about the crisis, largely determines our attitude towards the virus and towards societal response measures such as lockdowns, distancing, and mask-wearing. In other words, people who perceive the pandemic through the dramatic videos from Northern Italy or New York City hospital chaos, focus on atypical death reports such as those of children dying, or read stories about #LongCovid, will call for new lockdowns, defend their physical distance, or report partying neighbours out of fear for their own or others’ health. Meanwhile, people who look at the pandemic at a coarser scale of epidemic curves or normalized test positivity rates or death rates will more likely realize that the situation is not quite as dire as initially predicted and adjust their personal response accordingly.
Coincidentally, the Ontario Minister of Long-Term Care, Dr. Merrilee Fullerton, was asked in parliament today about a Toronto Sun column comparing deaths from COVID-19 to Influenza, and is quoted in QP Briefing:
“The numbers do indicate — if you actually measured the flu season, from 2017 into 2018 — the numbers are comparable. But I don’t want to talk about numbers. You know, it is about people.”
Now, while society and politics sure are about people, important decisions are usually made based at least in part on data! Thus, the Minister’s statement would suggest that we do not act much differently from managing the annual flu cycle. Nevertheless, all bets are off on an imminent announcement of new restrictions on indoor dining and certain group activities due to perceived “spike” in COVID-19 “cases” in Ontario. Nevertheless, I am still hopeful that a larger scale of perception will prevail and the government stick to a moderate approach balancing health and prosperity, as argued for example in May in an open letter from a mixed group of experts with the MacDonald-Laurier Institute.
