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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



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.
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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
Technical Report · August 2020
DOI: 10.13140/RG.2.2.18432.46080
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D. G. Rancourt
Ontario Civil Liberties Association
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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
(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-
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.
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%,
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
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.
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.
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COVID-19 pandemic on cruise ships,
COVID-19 pandemic on cruise ships,
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Cruise ship data helps reveal coronavirus death rate: researchers,
By Maxim Lott | Fox News, 13 March 2020
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
The infection fatality rate of COVID-19 inferred from seroprevalence data,
By John Ioannidis /
medRxiv 2020.05.13.20101253; doi:
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Journeyman Pictures YouTube channel, 20 April 2020
COVID-19 Pandemic Planning Scenarios,
By CDC, 20 May 2020
Disease Burden of Influenza,
By CDC, 17 April 2020
Heterogeneous and Dynamic Prevalence of Asymptomatic Influenza Virus Infections,
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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
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BMC Med 18, 217 (2020).
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By Daniel Horowitz, Conservative Review, 22 May 2020
The Virus Doesn’t Care about Your Policies,
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Cochrane Database Syst Rev. 2011;2011(7):CD006207. Published 2011 Jul 6.
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Proc Natl Acad Sci U S A. 2013;110(46):18360-18367. doi:10.1073/pnas.1313731110
The Fact-Free Lockdown Hysteria | Thomas E. Woods, Jr.
misesmedia YouTube channel, 16 July 2020
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By Brown, R. (2020). /
Disaster Medicine and Public Health Preparedness, 1-24. doi:10.1017/dmp.2020.298
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By Robert Roos and Lisa Schnirring /
CIDRAP - Center for Infectious Disease Research and Policy, 1 February 2007
CDC advisor says ‘real’ fatality rate of COVID-19 is too low to justify ‘drastic crackdowns’,
The College Fix, 30 March 2020
Drs. Yealy and Shapiro Share COVID-19 Insights with State Senate Committees,
By Donald Yealy, M.D., and Steven Shapiro, M.D. /
UPMC Medical Media, 13 May 2020
UK lockdown was a ‘monumental mistake’ and must not happen again Boris scientist says: LOCKDOWN
will come to be seen as a "monumental mistake on a global scale" and must never happen again, a
scientist who advises the Government on infectious diseases says,
By Lucy Johnston, Sunday Express Health Editor /
EXPRESS, 26 August 2020
Elvis Was King, Ike Was President, and 116,000 Americans Died in a Pandemic,
By Jeffrey A. Tucker /
American Institute for Economic Research, 4 May 2020
Ep89 Viral Impacts Explained - The PANDA Pandemic Data & Analytics Group,
Ivor Cummins YouTube channel, 16 July 2020
Free States Maintain Survival Advantage Over Locked States Even After Restrictions Ease,
By Colleen Huber, NMD /
PrimaryDoctor.Org, 29 June 2020
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Full-text available
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.
Full-text available
Significance Since SARS-CoV-2 emerged in China, it has spread rapidly around the world. Effective vaccines and therapeutics for SARS-CoV-2−induced disease (coronavirus disease 2019;COVID-19) are urgently needed. We found that SARS-CoV-2 isolates replicate efficiently in the lungs of Syrian hamsters and cause severe pathological lesions in the lungs of these animals similar to commonly reported imaging features of COVID-19 patients with pneumonia. SARS-CoV-2−infected hamsters mounted neutralizing antibody responses and were protected against rechallenge with SARS-CoV-2. Moreover, passive transfer of convalescent serum to naïve hamsters inhibited virus replication in their lungs. Syrian hamsters are a useful small animal model for the evaluation of vaccines, immunotherapies, and antiviral drugs.
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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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Coronavirus disease-19 (COVID19), the novel respiratory illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is associated with severe morbidity and mortality. The rollout of diagnostic testing in the United States was slow, leading to numerous cases that were not tested for SARS-CoV-2 in February and March 2020, necessitating the use of serological testing to determine past infections. Here, we evaluated the Abbott SARS-CoV-2 IgG test for detection of anti-SARS-CoV-2 IgG antibodies by testing 3 distinct patient populations. We tested 1,020 serum specimens collected prior to SARS-CoV-2 circulation in the United States and found one false positive, indicating a specificity of 99.90%. We tested 125 patients who tested RT-PCR positive for SARS-CoV-2 for which 689 excess serum specimens were available and found sensitivity reached 100% at day 17 after symptom onset and day 13 after PCR positivity. Alternative index value thresholds for positivity resulted in 100% sensitivity and 100% specificity in this cohort. We tested 4,856 individuals from Boise, Idaho collected over one week in April 2020 as part of the Crush the Curve initiative and detected 87 positives for a positivity rate of 1.79%. These data demonstrate excellent analytical performance of the Abbott SARS-CoV-2 IgG test as well as the limited circulation of the virus in the western United States. We expect the availability of high-quality serological testing will be a key tool in the fight against SARS-CoV-2.
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From 29 to 31 January 2020, a total of 565 Japanese citizens were evacuated from Wuhan, China on three chartered flights. All passengers were screened upon arrival in Japan for symptoms consistent with novel coronavirus (2019-nCoV) infection and tested for presence of the virus. Assuming that the mean detection window of the virus can be informed by the mean serial interval (estimated at 7.5 days), the ascertainment rate of infection was estimated at 9.2% (95% confidence interval: 5.0, 20.0). This indicates that the incidence of infection in Wuhan can be estimated at 20,767 infected individuals, including those with asymptomatic and mildly symptomatic infections. The infection fatality risk (IFR)-the actual risk of death among all infected individuals-is therefore 0.3% to 0.6%, which may be comparable to Asian influenza pandemic of 1957-1958.
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Epidemiological studies suggest that living close to the natural environment is associated with long-term health benefits including reduced death rates, reduced cardiovascular disease, and reduced psychiatric problems. This is often attributed to psychological mechanisms, boosted by exercise, social interactions, and sunlight. Compared with urban environments, exposure to green spaces does indeed trigger rapid psychological, physiological, and endocrinological effects. However, there is little evidence that these rapid transient effects cause long-term health benefits or even that they are a specific property of natural environments. Meanwhile, the illnesses that are increasing in high-income countries are associated with failing immunoregulation and poorly regulated inflammatory responses, manifested as chronically raised C-reactive protein and proinflammatory cytokines. This failure of immunoregulation is partly attributable to a lack of exposure to organisms ("Old Friends") from mankind's evolutionary past that needed to be tolerated and therefore evolved roles in driving immunoregulatory mechanisms. Some Old Friends (such as helminths and infections picked up at birth that established carrier states) are almost eliminated from the urban environment. This increases our dependence on Old Friends derived from our mothers, other people, animals, and the environment. It is suggested that the requirement for microbial input from the environment to drive immunoregulation is a major component of the beneficial effect of green space, and a neglected ecosystem service that is essential for our well-being. This insight will allow green spaces to be designed to optimize health benefits and will provide impetus from health systems for the preservation of ecosystem biodiversity.
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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.
Background : Since the first case of a novel coronavirus (COVID-19) infection pneumonia was detected in Wuhan, China, a series of confirmed cases of the COVID-19 were found in Beijing. We analyzed the data of 262 confirmed cases to determine the clinical and epidemiological characteristics of COVID-19 in Beijing. Methods : We collected patients who were transferred by Beijing Emergency Medical Sevice to the designated hospitals. The information on demographic, epidemiological, clinical, laboratory test for the COVID-19 virus, diagnostic classification, cluster case and outcome were obtained. Furthermore we compared the characteristics between severe and common confirmed cases which including mild cases, no-pneumonia cases and asymptomatic cases, and we also compared the features between COVID-19 and 2003 SARS. Findings : By Feb 10, 2020, 262 patients were transferred from the hospitals across Beijing to the designated hospitals for special treatment of the COVID-19 infected by Beijing emergency medical service. Among of 262 patients, 46 (17.6%) were severe cases, 216 (82.4%) were common cases, which including 192 (73.3%) mild cases, 11(4.2%) non-pneumonia cases and 13 (5.0%) asymptomatic cases respectively. The median age of patients was 47.5 years old and 48.5% were male. 192 (73.3%) patients were residents of Beijing, 50 (26.0%) of which had been to Wuhan, 116 (60.4%) had close contact with confirmed cases, 21 (10.9%) had no contact history. The most common symptoms at the onset of illness were fever (82.1%), cough (45.8%), fatigue (26.3%), dyspnea (6.9%) and headache (6.5%). The median incubation period was 6.7 days, the interval time from between illness onset and seeing a doctor was 4.5 days. As of Feb 10, 17.2% patients have discharged and 81.7% patients remain in hospital in our study, the fatality of COVID-19 infection in Beijing was 0.9%. Interpretation : On the basis of this study, we provided the ratio of the COVID-19 infection on the severe cases to the mild, asymptomatic and non-pneumonia cases in Beijing. Population was generally susceptible, and with a relatively low fatality rate. The measures to prevent transmission was very successful at early stage, the next steps on the COVID-19 infection should be focused on early isolation of patients and quarantine for close contacts in families and communities in Beijing. Funding Beijing Municipal Science and Technology Commission and Ministry of Science and Technology.
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.