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Review of calculated SARS-CoV-2 infection fatality rates: Good CDC science versus dubious CDC science, the actual risk that does not justify the "cure" - By Prof Joseph Audie

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Abstract

Introduction by Denis G. Rancourt: ---- In this letter to me, Joseph accomplishes the following points: • an explanation of the various kinds of fatality rates for a pathogen • a review of the measured infection fatality rates for SARS-CoV-2 • a demonstration that a recently changed CDC evaluation is most certainly incorrect, along with an illustration of how not to do a meta-analysis • his conclusion that “the absolute and relative ‘flu-like’ risk of death from a SARS-CoV-2 infection is far too low to rigorously justify governments imposing major disruptions to normal life, let alone the massive and indiscriminate ‘lockdown’ disruptions people have been forced to submit to and endure” ---- Joseph Audie, PhD (biophysics), MS (biomedical engineering), BS (bioengineering) is a professor of chemistry. He has performed original drug design and discovery research and has published in scientific journals. Joseph is adept at finding errors in scientific papers in the medical field.
See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/343889424
Review of calculated SARS-CoV-2 infection fatality rates: Good CDC science
versus dubious CDC science, the actual risk that does not justify the "cure" - By
Prof Joseph Audie
Technical Report · August 2020
DOI: 10.13140/RG.2.2.18432.46080
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Review of calculated SARS-CoV-2 infection fatality rates:
Good CDC science versus dubious CDC science, the actual risk
that does not justify the “cure”
By Prof. Joseph Audie
26 August 2020
Introduction by Denis G. Rancourt:
Prof Joseph Audie and I have been corresponding about the ongoing COVID episode for some months,
and he has previously written for the Ontario Civil Liberties Association, where I am a researcher.
Joseph Audie, PhD (biophysics), MS (biomedical engineering), BS (bioengineering) is a professor of
chemistry. He has performed original drug design and discovery research and has published in scientific
journals. Joseph is adept at finding errors in scientific papers in the medical field.
In this letter to me, Joseph accomplishes the following points:
an explanation of the various kinds of fatality rates for a pathogen
a review of the measured infection fatality rates for SARS-CoV-2
a demonstration that a recently changed CDC evaluation is most certainly incorrect, along
with an illustration of how not to do a meta-analysis
his conclusion that “the absolute and relative ‘flu-like’ risk of death from a SARS-CoV-2 infection
is far too low to rigorously justify governments imposing major disruptions to normal life, let
alone the massive and indiscriminate ‘lockdown’ disruptions people have been forced to submit
to and endure”
I have added a bibliography of the live links used in the text, below (in order of appearance).
Here is Joseph’s letter:
Hello Dr. Rancourt,
I would like to call your attention to the CDC’s second estimate for the infection fatality ratio (IFR) of
SARS-CoV-2 and its reliance on a weak and error-laden meta-analysis in a failed attempt to support it.
As I am sure you know, the crude infection fatality rate (cIFR) refers to the ratio of deaths associated
with or caused by a pathogen from among a sample of people with current or past infections that are
typically detected using RNA or serum antibody testing. This must be distinguished from the crude case
fatality rate (cCFR) which refers to the ratio of deaths associated with or caused by an infection from
among a sample of infected people who also meet some medical case definition, typically having to do
with the presence of clinical symptoms or hospitalization. The population IFR (pIFR) or population CFR
2
(pCFR) can be calculated when the sample data reflects a larger population or is adjusted to reflect a
larger population.
To calculate an accurate IFR, care must be taken in calculating both the denominator (number of
infections) and numerator (number of deaths). For example, according to the helpful Wikipedia
account, 14 deaths and 712 RNA confirmed infections are associated with the SARS-CoV-2 outbreak on
the Diamond Princess Cruise ship. Analysis of the press releases from the Japanese Ministry of Health
(JMH), however, confirms 13 SARS-CoV-2 associated deaths, which implies a cIFR = 1.8%. Further
analysis of the JMH press releases reveals that only 7 of the 13 people who died tested positive for
SARS-CoV-2 RNA, resulting in a lower cIFR estimate of 0.98%. Additional scrutiny of the JMH press
releases shows that only 4 of the deaths were directly attributed to or said to be caused by SARS-CoV-
2/COVID-19, which leads to an even lower cIFR estimate of 0.56%. The average age on the Diamond
Princess was about 58 years old, which does not reflect the age structure of a country like the US.
Working from the Diamond Princess data available to him in early March (700 infected and 7 associated
deaths), Dr. John Ioannidis of Stanford University calculated a point estimate for the pIFR of the US
population to be 0.125%. The take away lesson from this exercise is that distinctions must be made
(associated deaths, attributable deaths, etc.), apples and oranges (IFRs, CFRs, cIFRs, pIFRs, etc.) should
not be mixed, and SARS-CoV-2 probably has a pIFR in the range of a strong influenza.
Recently, Dr. Ioannidis, who has arguably emerged as the world’s leading expert on the SARS-CoV-2 IFR,
published a pre-print in which he critically reviews 43 IFR estimates from 36 antibody serology studies,
including his own study on Santa Clara County, CA. In the article, Dr. Ioannidis, for a variety of reasons,
cautions against doing a meta-analysis on the IFR data and deriving a single best IFR estimate and
instead opts for a more nuanced approach to data analysis, ultimately recommending the median pIFR
as the best indicator of central tendency. Across 32 locations, Dr. Ioannidis finds median raw and
corrected pIFR values of 0.27% and 0.24%, respectively. Dr. Ioannidis also provides pIFR results for 7
countries that have been reported in the media or in preliminary reports which yield median raw and
corrected pIFR values of 0.15% and 0.12%, respectively.
Included in Dr. Ioannidis’ review is the CDC’s first official pIFR estimate of 0.26%, an estimate that is
based on official CDC serology data and that can be readily calculated from officially reported CDC
estimates for the population symptomatic case fatality rate (psCFR) (0.4%) and asymptomatic infection
rate (35%) (0.26% = 0.4% x 0.65%). Importantly, the CDC’s pIFR estimate for SARS-CoV-2 is in excellent
agreement with the median corrected pIFR (0.18%) and crude mean corrected pIFR (0.35%) calculated
from the 8 non-CDC pIFR studies on US states and cities reviewed by Dr. Ioannidis. For context,
according to CDC data, the seasonal flu of 2010/2011 had a psCFR ≈ 0.18% which, assuming a 35%
asymptomatic infection rate, implies a seasonal flu pIFR of ≈ 0.12%.
Perhaps even more importantly, the CDC’s estimated pIFR of 0.26% is in excellent agreement with pIFR
estimates that derive from arguably the single most rigorous pIFR study conducted to date by Professor
Streeck and his team. The Streeck et al. study is based on data obtained from a spontaneous super
spreading event that infected ≈ 15.5% of people in the town of Gangelt, Germany (population of 12,597)
that occurred in pre-global lockdown February. The study’s pIFR estimate of 0.28%, as calculated from
such a natural experiment, probably provides the best available estimate for what can be thought of as
the natural lethality of SARS-CoV-2 in a broadly representative population. Moreover, the estimate may
represent an upper limit with respect to natural lethality, as Professor Streeck and his team were not
able to confirm that all 7 recorded deaths (average age = 80.8 years) were caused by SARS-CoV-2, there
were only 3 excess deaths with respect to the previous year, the number of infections does not reflect
infections that could have been detected using other methods such as mucosal antibody and reactive T-
3
cell testing, the ability to detect infections can fall off with the passage of time, and that super spreading
events tend to be on the deadlier side. Indeed, a simple excess death analysis suggests a pIFR ≈ 0.13%.
Other “natural experiments” that have occurred predominantly among working and retirement age
adults in meat packing plants, prisons, and on navy ships also point to a natural pIFR ≈ 0.1%-0.3%.
Several infection studies on diverse animals cats, ferrets, macaques, hamsters - have yet to produce a
single, unambiguous SARS-CoV-2 caused fatality and have universally failed to provide rigorous
satisfaction of Koch’s postulates, further confirming the modest natural lethality of SARS-CoV-2.
Ultimately, the tight agreement between the results of animal experiments, the natural pIFR estimates
calculated by Professor Streeck and from other “natural experiment” data sets, the median pIFR values
calculated across 32 and 7 global locations, respectively, the median and crude mean pIFR values
calculated from the 8 independent US studies, and the pIFR calculated by the CDC from their own
serology data is noteworthy and suggests the fundamental accuracy of the CDC estimate of pIFR ≈ 0.26%
and that SARS-CoV-2 has the lethality of a severe influenza, like the influenza pandemic of 1957.
Given the above, it came as somewhat of a shock to learn that the CDC recently presented a second and
higher pIFR estimate of 0.65%. The only justification provided by the CDC is that the pIFR is replacing
the psCFR because it provides “a more directly measurable parameter for disease severity for COVID-
19”. This attempted justification, however, fails because, as demonstrated above, the pIFR is readily
calculated from the CDC’s original estimates for the psCFR and asymptomatic infection rate. In addition,
the CDC does nothing to undermine let alone repudiate its original pIFR estimate of 0.26%. Indeed, the
CDC’s latest estimate for an asymptomatic infection rate of 40% implies an even lower pIFR estimate of
0.24%. Hence, we are left with as many as three official CDC estimates for the pIFR of SARS-CoV-2:
0.24%, 0.26% and 0.65%. The 0.65% estimate is an outlier, while the first two estimates of 0.24% and
0.26% are in excellent agreement and enjoy solid scientific support from multiple, independent studies.
As will be shown below, the 0.65% estimate is based on a single meta-analysis of dubious quality that
was recently published as a pre-print by Drs. Gideon Meyerowitz-Katz and Lea Merone.
The Meyerowitz-Katz and Merone article reports a point estimate for the population infection fatality
ratio (pIFR) of 0.68% based on a meta-analysis of 26 studies that were retrieved from the peer reviewed
and non-peer reviewed literature by 06/16/2020. The 26 studies report on the use of various
methodologies modeling, serological and observational to estimate the infection fatality rate of
SARS-CoV-2. While it represents a potentially helpful study, the study by Meyerowitz-Katz and Merone
suffers from a number of errors that biases it in the direction of a high pIFR estimate which calls into
question the CDC’s citing it as the only support for its’s new pIFR estimate of 0.65%. Indeed, it can be
argued that the meta-analysis is so flawed its pIFR estimate is useless. What follows is a short overview
of some of the more obvious errors that challenge the quality of the Meyerowitz-Katz and Merone
meta-analysis and the accuracy of its pIFR estimate.
To their credit, Meyerowitz-Katz and Merone acknowledge that “… any meta-analysis is only as reliable
as the quality of included studies …” By implication, excluding quality studies can also undermine the
reliability of a meta-analysis. Both errors of commission and omission undermine the Meyerowitz-Katz
and Merone study and are briefly discussed below.
Perhaps most importantly, Meyerowitz-Katz and Merone do not even mention, let alone critically
engage, the IFR review article of Dr. John Ioannidis (discussed above) which was available as a first and
second version pre-print on May 19 and June 8, respectively. This is a problem in its own right, as
authors should contextualize their own research and critically discuss it in light of previous research.
4
Moreover, the omission of Dr. Ioannidis review article has important analytical implications. For
example, Meyerowitz-Katz and Merone rejected the studies by Bryan et al. and Silveira et al. because
they claimed “it was difficult to determine the numerator (i.e. number of deaths) associated with the
seroprevalence estimate, or the denominator (i.e. population) was not well defined”. Prof. Ioannidis,
however, included both studies in his review and reported corrected pIFRs of 0.13% and 0.39%,
respectively.
Meyerowitz-Katz also rejected the study by Sood et al. because it supposedly “explicitly warned against
using its data to obtain an IFR”. A reading of the Sood et al. article, however, fails to reveal such an
obvious and explicit warning. Importantly, Dr. Ioannidis used the Sood et al. study to calculate a
corrected pIFR of 0.18%.
Meyerowitz-Katz also excluded several studies, including blood donor sero-prevalence/IFR studies
analyzed by Dr. Ioannidis, because “many studies only looked at targeted populations in their
seroprevalence data, and thus could not be used as population estimators of IFR (pIFR).” Despite this,
Meyerowitz-Katz included the study by Tian et al., which reports on the characteristics of a relatively
small and targeted sample of hospitalized patients in Beijing, China and reports what is best
characterized as a cCFR = 0.9% (as opposed to a pIFR).
Meyerowitz-Katz and Merone fail to even mention, let alone include in their analysis, the early pIFR
study by Mizumoto et al. which was available as a pre-print in February and has since passed peer
review and been published. Importantly, Mizumoto et al. used a modeling methodology to calculate a
low pIFR estimate for Wuhan, China of 0.12%. Similarly, Meyerowitz-Katz and Merone are totally silent
on the CDC’s implied pIFR estimate of 0.26% (discussed above) despite the fact that it was publically
available prior to 06/16/2020.
Having excluded several studies that reported relatively low pIFR values (0.13%, 0.39%, 0.18%, 0.12%,
0.26%) on dubious grounds and included at least one inappropriate study that reported a relatively high
estimate of 0.9%, Meyerowitz-Katz and Merone arrive at their point estimate of 0.68% by meta-
analyzing and quantitatively synthesizing results from the included 26 studies
Even had Meyerowitz-Katz and Merone included the low pIFR estimates and excluded the high and
inappropriate estimate mentioned above, their meta-analysis and quantitative synthesis would still have
suffered from serious problems. This is because the analyzed studies employed a range of disparate
methodologies (modeling, observational, seroprevalence), ranged from highly biased to unbiased,
suffered from high heterogeneity, favored Europe (with relatively high pIFRs) over other geographical
locations (with relatively low IFRs), and many studies used no doubt different and many times
frustratingly opaque criteria for classifying deaths. These are all red flags that undermine the case for
using meta-analysis to report an unbiased and accurate point estimate for the pIFR. Indeed, in his
review Prof. Ioannidis points out the many challenges of doing a meta-analysis and cautioned against
such an approach, something Meyerowitz-Katz and Merone would have known had they included Prof.
Ioannidis’ article as part of their analysis. Ultimately, the available evidence suggests the 0.68%
estimate put forth by Meyerowitz-Katz is, at best, highly biased on the high end of the pIFR range and, at
worst, totally useless.
Worth noting is that the results of the Meyerowitz-Katz analysis imply a naïve or crude average pIFR for
the United States of 0.58%. With the preceding in mind, it is not clear how the CDC used the results of
the Meyerowitz-Katz analysis to arrive at its own pIFR estimate for the US of 0.65%, a national estimate
5
that is higher or equal to pIFR estimates for especially hard hit New York and New York City, for which
corrected pIFR values of 0.54% and 0.65%, respectively, were calculated by Prof. Ioannidis.
To summarize: the CDC’s original estimate for the pIFR = 0.26% is based on official CDC serology data, is
consistent with the results of scientifically analyzed “natural experiments”, and agrees nicely with the
results derived from other studies that relied on international and US infection and fatality data.
Additionally, the CDC has failed to provide any reason, let alone a good reason, for rejecting its estimate
of pIFR = 0.26% and still makes the estimate available to the public. The only thing provided by the CDC
in support of its new pIFR = 0.65% estimate is a single pre-print article reporting on a meta-analysis that
suffers from numerous errors, including biased data inclusion and exclusion, resulting in a pIFR estimate
that should be interpreted as either shifted to the high end of the pIFR spectrum or as totally useless.
Hence, given the available evidence, the CDC’s original pIFR = 0.26% estimate should be taken as the
more accurate one.
In a very real sense, a discussion about whether or not the pIFR of SARS-CoV-2 is ≈ 0.26% or ≈ 0.6% is
only of academic interest. Put simply, the absolute and relative “flu-like” risk of death from a SARS-CoV-
2 infection is far too low to rigorously justify government’s imposing any major disruptions to normal
life, let alone the massive and indiscriminate “lockdown” disruptions people have been forced to submit
to and endure, as such disruptions will inevitably unleash innumerable forces, including deadly forces,
that will reverberate throughout society in predictable an unpredictable ways for years to come.
Consider, for example, that according to one analysis even for the relatively high risk 60-69 year old
demographic, a person’s one year probability of dying only increases from a baseline value of 1.3% to
1.8%, assuming an age-specific SARS-CoV-2 IFR of 0.49% and absurdly high attack rate of 100%. A more
reasonable but still high estimate for the increased one year probability of death from SARS-CoV-2 for
60-69 year olds, based on the Diamond Princesses’ observed attack rate of ≈ 20%, would be from 1.3%
to 1.4%. Does it really make sense for governments to impose unproven and massively disruptive
lockdown style interventions interventions all but guaranteed to destroy the religious, moral, legal,
political, economic, educational and psychosocial fabric of a nation in a quixotic attempt to reduce a
relatively high risk person’s odds of dying by 0.1%? Doesn’t it make more sense for people to do what’s
always been done and take voluntary, evidence-based, and personalized approaches to protect such
individuals that also respect absolute goods the inalienable right to work and secure sustenance - and
balance competing, relative goods going to a concert or ballgame? The questions answer themselves.
Indeed, it almost seems like a truism to point out that the imposition of coercive and unproven
lockdown measures, on an entire population, amounts to a mass social experiment that is fated to fail
and ultimately increase people’s baseline probabilities of mortality and morbidity in myriad ways that
will effectively negate and even reverse any hypothetical gains from mitigating SARS-CoV-2. There is
even reason to think that lockdown measures will increase people’s susceptibility to respiratory
infections, including SARS-CoV-2 infections, and that thwarting microbial exchange with other humans,
animals, and the natural environment could impair the proper function of people’s immune systems.
SARS-CoV-2 poses a significant risk to a well-defined, vulnerable population of elderly and infirmed
people and is a statistical non-issue for the vast majority of people. This is good news, for it empowers
communities to adopt targeted and scientifically-based mitigation strategies, ultimately allowing
everyone else to keep working to support their families, communities and the health care system,
voluntarily practice standard cold and flu mitigation strategies, and ultimately acquire natural immunity,
bring the epidemic to an end, preserve and perpetuate their way of life, and avoid the collateral damage
wrought by imposition of the many crude and draconian interventions subsumed under the general
term lockdown.
6
The above analysis and policy recommendation should be seen as controversial. Indeed, it is only
controversial because of mass hysteria. Rather, it follows logically from an accurate pIFR-based
understanding of the “flu-like” lethality of SARS-CoV-2 and a simple relative risk analysis; it reflects
common sense, universal practice, official pandemic mitigation planning, and the opinions of many
experts, including a former lockdown proponent and architect; it has been demonstrated to work during
the previous pandemics of 1957, 1968 and 2009; and perhaps most importantly, it has been shown to
work in present day non-lockdown countries and free US states - such as Sweden, Japan, Belarus,
Tanzania, Nicaragua, South Korea, and Taiwan, and North Dakota, South Dakota, Nebraska, Arkansas,
Wyoming, and Iowa, respectively.
Bibliography of links used (in order of appearance)
http://ocla.ca/criticism-of-chu-et-al-on-face-masks-for-covid-19-by-professor-joseph-audie/
Criticism of DK Chu et al. on face masks for COVID-19 by professor Joseph Audie,
ocla.ca, 14 July 2020
https://en.wikipedia.org/wiki/COVID-19_pandemic_on_cruise_ships
COVID-19 pandemic on cruise ships,
Wikipedia
https://en.wikipedia.org/wiki/COVID-19_pandemic_on_cruise_ships#cite_note-29
COVID-19 pandemic on cruise ships,
Wikipedia : Press releases, Japanese Ministry of Health.
https://www.foxnews.com/us/cruise-ship-data-helps-reveal-coronavirus-death-rate-researchers
Cruise ship data helps reveal coronavirus death rate: researchers,
By Maxim Lott | Fox News, 13 March 2020
https://www.statnews.com/2020/03/17/a-fiasco-in-the-making-as-the-coronavirus-pandemic-takes-
hold-we-are-making-decisions-without-reliable-data/
A fiasco in the making? As the coronavirus pandemic takes hold, we are making decisions without
reliable data,
By John P.A. Ioannidis,
StatNews, 17 March 2020
https://www.medrxiv.org/content/10.1101/2020.05.13.20101253v3
The infection fatality rate of COVID-19 inferred from seroprevalence data,
By John Ioannidis /
medRxiv 2020.05.13.20101253; doi: https://doi.org/10.1101/2020.05.13.20101253
https://www.youtube.com/watch?v=cwPqmLoZA4s&list=PLlGSlkijht5iO3PsUGTfOYcwVoOiwDctV&index
=20
Perspectives on the Pandemic | Dr. John Ioannidis Update: 4.17.20 | Episode 4,
Journeyman Pictures YouTube channel, 20 April 2020
7
https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scenarios-archive/planning-scenarios-2020-
05-20.pdf
COVID-19 Pandemic Planning Scenarios,
By CDC, 20 May 2020
https://www.cdc.gov/flu/about/burden/index.html
Disease Burden of Influenza,
By CDC, 17 April 2020
https://pubmed.ncbi.nlm.nih.gov/27191967/
Heterogeneous and Dynamic Prevalence of Asymptomatic Influenza Virus Infections,
By Furuya-Kanamori L, Cox M, Milinovich GJ, Magalhaes RJ, Mackay IM, Yakob L. / Emerg Infect Dis.
2016;22(6):1052-1056. doi:10.3201/eid2206.151080
https://www.medrxiv.org/content/10.1101/2020.05.04.20090076v2
Infection fatality rate of SARS-CoV-2 infection in a German community with a super-spreading event,
By Hendrik Streeck, Bianca Schulte, Beate Kuemmerer, Enrico Richter, Tobias Hoeller, Christine
Fuhrmann, Eva Bartok, Ramona Dolscheid, Moritz Berger, Lukas Wessendorf, Monika Eschbach-Bludau,
Angelika Kellings, Astrid Schwaiger, Martin Coenen, Per Hoffmann, Markus Noethen, Anna-Maria Eis-
Huebinger, Martin Exner, Ricarda Schmithausen, Matthias Schmid, Gunther Hartmann
medRxiv 2020.05.04.20090076; doi: https://doi.org/10.1101/2020.05.04.20090076
https://www.youtube.com/watch?v=vrL9QKGQrWk
German virologist: Covid-19 is less deadly than we thought [Professor Streeck],
UnHerd YouTube channel, 5 May 2020
https://www.youtube.com/watch?v=GBRcK-od50Q
Ep91 Emeritus Professor of Immunology...Reveals Crucial Viral Immunity Reality,
Ivor Cummins YouTube channel, 28 July 2020
https://swprs.org/studies-on-covid-19-lethality/
Studies on Covid-19 lethality,
Swiss Policy Research, Last updated: August 22, 2020; First published: May 12, 2020
https://www.komu.com/news/tuesday-covid-19-coverage-hy-vee-to-ration-meat-sales/
Tuesday COVID-19 Coverage: Local COVID-19 numbers updated,
By Claire Colby and Bill Finn, KOMU 8 Digital Producers, 5 May 2020
https://pubmed.ncbi.nlm.nih.gov/32462701/
Animal models of mechanisms of SARS-CoV-2 infection and COVID-19 pathology,
By Cleary SJ, Pitchford SC, Amison RT, et al., [published online ahead of print, 2020 May 27] / Br J
Pharmacol. 2020;10.1111/bph.15143. doi:10.1111/bph.15143
https://pubmed.ncbi.nlm.nih.gov/32571934/
Syrian hamsters as a small animal model for SARS-CoV-2 infection and countermeasure development,
By Imai M, Iwatsuki-Horimoto K, Hatta M, et al. / Proc Natl Acad Sci U S A. 2020;117(28):16587-16595.
doi:10.1073/pnas.2009799117
8
https://pubmed.ncbi.nlm.nih.gov/23260039/
Novel framework for assessing epidemiologic effects of influenza epidemics and pandemics,
By Reed C, Biggerstaff M, Finelli L, et al. / Emerg Infect Dis. 2013;19(1):85-91.
doi:10.3201/eid1901.120124
https://pubmed.ncbi.nlm.nih.gov/32033064/
The Rate of Underascertainment of Novel Coronavirus (2019-nCoV) Infection: Estimation Using Japanese
Passengers Data on Evacuation Flights,
By Nishiura H, Kobayashi T, Yang Y, et al. / J Clin Med. 2020;9(2):419. Published 2020 Feb 4.
doi:10.3390/jcm9020419
https://www.medrxiv.org/content/10.1101/2020.05.13.20101253v3.article-info
The infection fatality rate of COVID-19 inferred from seroprevalence data,
By John Ioannidis /
medRxiv 2020.05.13.20101253; doi: https://doi.org/10.1101/2020.05.13.20101253
https://pubmed.ncbi.nlm.nih.gov/32381641/
Performance Characteristics of the Abbott Architect SARS-CoV-2 IgG Assay and Seroprevalence in Boise,
Idaho,
By Bryan A, Pepper G, Wener MH, et al. / J Clin Microbiol. 2020;58(8):e00941-20. Published 2020 Jul 23.
doi:10.1128/JCM.00941-20
https://www.medrxiv.org/content/10.1101/2020.05.01.20087205v2
Repeated population-based surveys of antibodies against SARS-CoV-2 in Southern Brazil
By Mariangela Silveira, Aluisio Barros, Bernardo Horta, Lucia Pellanda, Gabriel Victora, Odir Dellagostin,
Claudio Struchiner, Marcelo Burattini, Andreia Valim, Evelise Berlezi, Jeovany Mesa, Maria Leticia Ikeda,
Marilia Mesenburg, Marina Mantesso, Marinel Dall'Agnol, Raqueli Bittencourt, Fernando P Hartwig, Ana
Maria Menezes, Fernando C Barros, Pedro Hallal, Cesar G Victora /
medRxiv 2020.05.01.20087205; doi: https://doi.org/10.1101/2020.05.01.20087205
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7235907/
Seroprevalence of SARS-CoV-2-Specific Antibodies Among Adults in Los Angeles County, California, on
April 10-11, 2020,
By Sood N, Simon P, Ebner P, et al. [published online ahead of print, 2020 May 18]. /
JAMA. 2020;323(23):2425-2427. doi:10.1001/jama.2020.8279
https://pubmed.ncbi.nlm.nih.gov/32112886/
Characteristics of COVID-19 infection in Beijing,
By Tian S, Hu N, Lou J, et al. /
J Infect. 2020;80(4):401-406. doi:10.1016/j.jinf.2020.02.018
https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-020-01691-x
Early epidemiological assessment of the transmission potential and virulence of coronavirus disease
2019 (COVID-19) in Wuhan City, China, JanuaryFebruary, 2020,
By Mizumoto, K., Kagaya, K. & Chowell, G. /
BMC Med 18, 217 (2020). https://doi.org/10.1186/s12916-020-01691-x
9
https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scenarios-archive/planning-scenarios-2020-
05-20.pdf
Ibid.
https://www.conservativereview.com/news/horowitz-cdc-confirms-remarkably-low-coronavirus-death-
rate-media/
Horowitz: The CDC confirms remarkably low coronavirus death rate. Where is the media?
By Daniel Horowitz, Conservative Review, 22 May 2020
https://www.aier.org/article/the-virus-doesnt-care-about-your-policies/
The Virus Doesn’t Care about Your Policies,
By Jeffrey A. Tucker, American Institute for Economic Research, 31 July 2020
https://pubmed.ncbi.nlm.nih.gov/21735402/
Physical interventions to interrupt or reduce the spread of respiratory viruses,
By Jefferson T, Del Mar CB, Dooley L, et al. /
Cochrane Database Syst Rev. 2011;2011(7):CD006207. Published 2011 Jul 6.
doi:10.1002/14651858.CD006207.pub4
https://pubmed.ncbi.nlm.nih.gov/32640177/
Psychosocial Vulnerabilities to Upper Respiratory Infectious Illness: Implications for Susceptibility to
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10
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In testimony before U.S. Congress on March 11, 2020, members of the House Oversight and Reform Committee were informed that estimated mortality for the novel coronavirus was ten-times higher than for seasonal influenza. Additional evidence, however, suggests the validity of this estimation could benefit from vetting for biases and miscalculations. The main objective of this article is to critically appraise the coronavirus mortality estimation presented to Congress. Informational texts from the World Health Organization and the Centers for Disease Control and Prevention are compared with coronavirus mortality calculations in Congressional testimony. Results of this critical appraisal reveal information bias and selection bias in coronavirus mortality overestimation, most likely caused by misclassifying an influenza infection fatality rate as a case fatality rate. Public health lessons learned for future infectious disease pandemics include: safeguarding against research biases that may underestimate or overestimate an associated risk of disease and mortality; reassessing the ethics of fear-based public health campaigns; and providing full public disclosure of adverse effects from severe mitigation measures to contain viral transmission.
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The coronavirus disease 2019 (COVID‐19) pandemic caused by SARS‐CoV‐2 infections has led to substantial unmet need for treatments, many of which will require testing in appropriate animal models of this disease. Vaccine trials are already underway, but there remains an urgent need to find other therapeutic approaches to either target SARS‐CoV‐2 or the complications arising from viral infection, particularly the dysregulated immune response and systemic complications which have been associated with progression to severe COVID‐19. At the time of writing, in vivo studies of SARS‐CoV‐2 infection have been described using macaques, cats, ferrets, hamsters, and transgenic mice expressing human angiotensin I converting enzyme 2 (ACE2). These infection models have already been useful for studies of transmission and immunity, but to date only partially model the mechanisms implicated in human severe COVID‐19. There is therefore an urgent need for development of animal models for improved evaluation of efficacy of drugs identified as having potential in the treatment of severe COVID‐19. These models need to recapitulate key mechanisms of COVID‐19 severe acute respiratory distress syndrome and reproduce the immunopathology and systemic sequelae associated with this disease. Here, we review the current models of SARS‐CoV‐2 infection and COVID‐19‐related disease mechanisms and suggest ways in which animal models can be adapted to increase their usefulness in research into COVID‐19 pathogenesis and for assessing potential treatments.
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The world faces an unprecedented SARS-CoV2 pandemic where many critical factors still remain unknown. The case fatality rates (CFR) reported in the context of the SARS-CoV-2 pandemic substantially differ between countries. For SARS-CoV-2 infection with its broad clinical spectrum from asymptomatic to severe disease courses, the infection fatality rate (IFR) is the more reliable parameter to predict the consequences of the pandemic. Here we combined virus RT-PCR testing and assessment for SARS-CoV2 antibodies to determine the total number of individuals with SARS-CoV-2 infections in a given population. Methods: A sero-epidemiological GCP- and GEP-compliant study was performed in a small German town which was exposed to a super-spreading event (carnival festivities) followed by strict social distancing measures causing a transient wave of infections. Questionnaire-based information and biomaterials were collected from a random, household-based study population within a seven-day period, six weeks after the outbreak. The number of present and past infections was determined by integrating results from anti-SARS-CoV-2 IgG analyses in blood, PCR testing for viral RNA in pharyngeal swabs and reported previous positive PCR tests. Results: Of the 919 individuals with evaluable infection status (out of 1,007; 405 households) 15.5% (95% CI: [12.3%; 19.0%]) were infected. This is 5-fold higher than the number of officially reported cases for this community (3.1%). Infection was associated with characteristic symptoms such as loss of smell and taste. 22.2% of all infected individuals were asymptomatic. With the seven SARS-CoV-2-associated reported deaths the estimated IFR was 0.36% [0.29%; 0.45%]. Age and sex were not found to be associated with the infection rate. Participation in carnival festivities increased both the infection rate (21.3% vs. 9.5%, p<0.001) and the number of symptoms in the infected (estimated relative mean increase 1.6, p=0.007). The risk of a person being infected was not found to be associated with the number of study participants in the household this person lived in. The secondary infection risk for study participants living in the same household increased from 15.5% to 43.6%, to 35.5% and to 18.3% for households with two, three or four people respectively (p<0.001). Conclusions: While the number of infections in this high prevalence community is not representative for other parts of the world, the IFR calculated on the basis of the infection rate in this community can be utilized to estimate the percentage of infected based on the number of reported fatalities in other places with similar population characteristics. Whether the specific circumstances of a super-spreading event not only have an impact on the infection rate and number of symptoms but also on the IFR requires further investigation. The unexpectedly low secondary infection risk among persons living in the same household has important implications for measures installed to contain the SARS-CoV-2 virus pandemic.
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The effects of influenza on a population are attributable to the clinical severity of illness and the number of persons infected, which can vary greatly between seasons or pandemics. To create a systematic framework for assessing the public health effects of an emerging pandemic, we reviewed data from past influenza seasons and pandemics to characterize severity and transmissibility (based on ranges of these measures in the United States) and outlined a formal assessment of the potential effects of a novel virus. The assessment was divided into 2 periods. Because early in a pandemic, measurement of severity and transmissibility is uncertain, we used a broad dichotomous scale in the initial assessment to divide the range of historic values. In the refined assessment, as more data became available, we categorized those values more precisely. By organizing and prioritizing data collection, this approach may inform an evidence-based assessment of pandemic effects and guide decision making.
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Influenza infection manifests in a wide spectrum of severity, including symptomless pathogen carriers. We conducted a systematic review and meta-analysis of 55 studies to elucidate the proportional representation of these asymptomatic infected persons. We observed extensive heterogeneity among these studies. The prevalence of asymptomatic carriage (total absence of symptoms) ranged from 5.2% to 35.5% and subclinical cases (illness that did not meet the criteria for acute respiratory or influenza-like illness) from 25.4% to 61.8%. Statistical analysis showed that the heterogeneity could not be explained by the type of influenza, the laboratory tests used to detect the virus, the year of the study, or the location of the study. Projections of infection spread and strategies for disease control require that we identify the proportional representation of these insidious spreaders early on in the emergence of new influenza subtypes or strains and track how this rate evolves over time and space.