Coronavirus: Thread

grarpamp grarpamp at gmail.com
Mon Dec 21 22:47:29 PST 2020


https://www.aier.org/article/lockdowns-do-not-control-the-coronavirus-the-evidence/

Lockdowns Do Not Control The Coronavirus: The Evidence

The use of universal lockdowns in the event of the appearance of a new
pathogen has no precedent. It has been a science experiment in real
time, with most of the human population used as lab rats. The costs
are legion.

The question is whether lockdowns worked to control the virus in a way
that is scientifically verifiable. Based on the following studies, the
answer is no and for a variety of reasons: bad data, no correlations,
no causal demonstration, anomalous exceptions, and so on. There is no
relationship between lockdowns (or whatever else people want to call
them to mask their true nature) and virus control.

Perhaps this is a shocking revelation, given that universal social and
economic controls are becoming the new orthodoxy. In a saner world,
the burden of proof really should belong to the lockdowners, since it
is they who overthrew 100 years of public-health wisdom and replaced
it with an untested, top-down imposition on freedom and human rights.
They never accepted that burden. They took it as axiomatic that a
virus could be intimidated and frightened by credentials, edicts,
speeches, and masked gendarmes.

The pro-lockdown evidence is shockingly thin, and based largely on
comparing real-world outcomes against dire computer-generated
forecasts derived from empirically untested models, and then merely
positing that stringencies and “nonpharmaceutical interventions”
account for the difference between the fictionalized vs. the real
outcome. The anti-lockdown studies, on the other hand, are
evidence-based, robust, and thorough, grappling with the data we have
(with all its flaws) and looking at the results in light of controls
on the population.

Much of the following list has been put together by data engineer Ivor
Cummins, who has waged a year-long educational effort to upend
intellectual support for lockdowns. AIER has added its own and the
summaries. The upshot is that the virus is going to do as viruses do,
same as always in the history of infectious disease. We have extremely
limited control over them, and that which we do have is bound up with
time and place. Fear, panic, and coercion are not ideal strategies for
managing viruses. Intelligence and medical therapeutics fare much
better.

(These studies are focused only on lockdown and their relationship to
virus control. They do not get into the myriad associated issues that
have vexed the world such as mask mandates, PCR-testing issues, death
misclassification problem, or any particular issues associated with
travel restrictions, restaurant closures, and hundreds of other
particulars about which whole libraries will be written in the
future.)

1. “A country level analysis measuring the impact of government
actions, country preparedness and socioeconomic factors on COVID-19
mortality and related health outcomes” by Rabail Chaudhry, George
Dranitsaris, Talha Mubashir, Justyna Bartoszko, Sheila Riazi.
EClinicalMedicine 25 (2020) 100464. “[F]ull lockdowns and wide-spread
COVID-19 testing were not associated with reductions in the number of
critical cases or overall mortality.”

2. “Was Germany’s Corona Lockdown Necessary?” by Christof Kuhbandner,
Stefan Homburg, Harald Walach, Stefan Hockertz. Advance: Sage
Preprint, June 23, 2020. “Official data from Germany’s RKI agency
suggest strongly that the spread of the coronavirus in Germany receded
autonomously, before any interventions became effective. Several
reasons for such an autonomous decline have been suggested. One is
that differences in host susceptibility and behavior can result in
herd immunity at a relatively low prevalence level. Accounting for
individual variation in susceptibility or exposure to the coronavirus
yields a maximum of 17% to 20% of the population that needs to be
infected to reach herd immunity, an estimate that is empirically
supported by the cohort of the Diamond Princess cruise ship. Another
reason is that seasonality may also play an important role in
dissipation.”

3. “Estimation of the current development of the SARS-CoV-2 epidemic
in Germany” by Matthias an der Heiden, Osamah Hamouda. Robert
Koch-Institut, April 22, 2020. “In general, however, not all infected
people develop symptoms, not all those who develop symptoms go to a
doctor’s office, not all who go to the doctor are tested and not all
who test positive are also recorded in a data collection system. In
addition, there is a certain amount of time between all these
individual steps, so that no survey system, no matter how good, can
make a statement about the current infection process without
additional assumptions and calculations.”

4. Did COVID-19 infections decline before UK lockdown? by Simon N.
Wood. Cornell University pre-print, August 8, 2020. “A Bayesian
inverse problem approach applied to UK data on COVID-19 deaths and the
disease duration distribution suggests that infections were in decline
before full UK lockdown (24 March 2020), and that infections in Sweden
started to decline only a day or two later. An analysis of UK data
using the model of Flaxman et al. (2020, Nature 584) gives the same
result under relaxation of its prior assumptions on R.”

5. “Comment on Flaxman et al. (2020): The illusory effects of
non-pharmaceutical interventions on COVID-19 in Europe” by Stefan
Homburg and Christof Kuhbandner. June 17, 2020. Advance, Sage
Pre-Print. “In a recent article, Flaxman et al. allege that
non-pharmaceutical interventions imposed by 11 European countries
saved millions of lives. We show that their methods involve circular
reasoning. The purported effects are pure artefacts, which contradict
the data. Moreover, we demonstrate that the United Kingdom’s lockdown
was both superfluous and ineffective.”

6. Professor Ben Israel’s Analysis of virus transmission. April 16,
2020. “Some may claim that the decline in the number of additional
patients every day is a result of the tight lockdown imposed by the
government and health authorities. Examining the data of different
countries around the world casts a heavy question mark on the above
statement. It turns out that a similar pattern – rapid increase in
infections that reaches a peak in the sixth week and declines from the
eighth week – is common to all countries in which the disease was
discovered, regardless of their response policies: some imposed a
severe and immediate lockdown that included not only ‘social
distancing’ and banning crowding, but also shutout of economy (like
Israel); some ‘ignored’ the infection and continued almost a normal
life (such as Taiwan, Korea or Sweden), and some initially adopted a
lenient policy but soon reversed to a complete lockdown (such as Italy
or the State of New York). Nonetheless, the data shows similar time
constants amongst all these countries in regard to the initial rapid
growth and the decline of the disease.”

7. “Impact of non-pharmaceutical interventions against COVID-19 in
Europe: a quasi-experimental study” by Paul Raymond Hunter, Felipe
Colon-Gonzalez, Julii Suzanne Brainard, Steve Rushton. MedRxiv
Pre-print May 1, 2020. “The current epidemic of COVID-19 is
unparalleled in recent history as are the social distancing
interventions that have led to a significant halt on the economic and
social life of so many countries. However, there is very little
empirical evidence about which social distancing measures have the
most impact… From both sets of modelling, we found that closure of
education facilities, prohibiting mass gatherings and closure of some
non-essential businesses were associated with reduced incidence
whereas stay at home orders and closure of all non-businesses was not
associated with any independent additional impact.”

8. “Full lockdown policies in Western Europe countries have no evident
impacts on the COVID-19 epidemic” by Thomas Meunier. MedRxiv Pre-print
May 1, 2020. “This phenomenological study assesses the impacts of full
lockdown strategies applied in Italy, France, Spain and United
Kingdom, on the slowdown of the 2020 COVID-19 outbreak. Comparing the
trajectory of the epidemic before and after the lockdown, we find no
evidence of any discontinuity in the growth rate, doubling time, and
reproduction number trends. Extrapolating pre-lockdown growth rate
trends, we provide estimates of the death toll in the absence of any
lockdown policies, and show that these strategies might not have saved
any life in western Europe. We also show that neighboring countries
applying less restrictive social distancing measures (as opposed to
police-enforced home containment) experience a very similar time
evolution of the epidemic.”

9. “Trajectory of COVID-19 epidemic in Europe” by Marco Colombo,
Joseph Mellor, Helen M Colhoun, M. Gabriela M. Gomes, Paul M McKeigue.
MedRxiv Pre-print. Posted September 28, 2020. “The classic
Susceptible-Infected-Recovered model formulated by Kermack and
McKendrick assumes that all individuals in the population are equally
susceptible to infection. From fitting such a model to the trajectory
of mortality from COVID-19 in 11 European countries up to 4 May 2020
Flaxman et al. concluded that ‘major non-pharmaceutical interventions
— and lockdowns in particular — have had a large effect on reducing
transmission’. We show that relaxing the assumption of homogeneity to
allow for individual variation in susceptibility or connectivity gives
a model that has better fit to the data and more accurate 14-day
forward prediction of mortality. Allowing for heterogeneity reduces
the estimate of ‘counterfactual’ deaths that would have occurred if
there had been no interventions from 3.2 million to 262,000, implying
that most of the slowing and reversal of COVID-19 mortality is
explained by the build-up of herd immunity. The estimate of the herd
immunity threshold depends on the value specified for the infection
fatality ratio (IFR): a value of 0.3% for the IFR gives 15% for the
average herd immunity threshold.”

10. “Effect of school closures on mortality from coronavirus disease
2019: old and new predictions” by Ken Rice, Ben Wynne, Victoria
Martin, Graeme J Ackland. British Medical Journal, September 15, 2020.
“The findings of this study suggest that prompt interventions were
shown to be highly effective at reducing peak demand for intensive
care unit (ICU) beds but also prolong the epidemic, in some cases
resulting in more deaths long term. This happens because covid-19
related mortality is highly skewed towards older age groups. In the
absence of an effective vaccination programme, none of the proposed
mitigation strategies in the UK would reduce the predicted total
number of deaths below 200 000.”

11. “Modeling social distancing strategies to prevent SARS-CoV2 spread
in Israel- A Cost-effectiveness analysis” by Amir Shlomai, Ari Leshno,
Ella H Sklan, Moshe Leshno. MedRxiv Pre-Print. September 20, 2020. “A
nationwide lockdown is expected to save on average 274 (median 124,
interquartile range (IQR): 71-221) lives compared to the ‘testing,
tracing, and isolation’ approach. However, the ICER will be on average
$45,104,156 (median $ 49.6 million, IQR: 22.7-220.1) to prevent one
case of death. Conclusions: A national lockdown has a moderate
advantage in saving lives with tremendous costs and possible
overwhelming economic effects. These findings should assist
decision-makers in dealing with additional waves of this pandemic.”

12. Too Little of a Good Thing A Paradox of Moderate Infection
Control, by Ted Cohen and Marc Lipsitch. Epidemiology. 2008 Jul;
19(4): 588–589. “The link between limiting pathogen exposure and
improving public health is not always so straightforward. Reducing the
risk that each member of a community will be exposed to a pathogen has
the attendant effect of increasing the average age at which infections
occur. For pathogens that inflict greater morbidity at older ages,
interventions that reduce but do not eliminate exposure can
paradoxically increase the number of cases of severe disease by
shifting the burden of infection toward older individuals.”

13. “Smart Thinking, Lockdown and COVID-19: Implications for Public
Policy” by Morris Altman. Journal of Behavioral Economics for Policy,
2020. “The response to COVID-19 has been overwhelmingly to lockdown
much of the world’s economies in order to minimize death rates as well
as the immediate negative effects of COVID-19. I argue that such
policy is too often de-contextualized as it ignores policy
externalities, assumes death rate calculations are appropriately
accurate and, and as well, assumes focusing on direct Covid-19 effects
to maximize human welfare is appropriate. As a result of this approach
current policy can be misdirected and with highly negative effects on
human welfare. Moreover, such policies can inadvertently result in not
minimizing death rates (incorporating externalities) at all,
especially in the long run. Such misdirected and sub-optimal policy is
a product of policy makers using inappropriate mental models which are
lacking in a number of key areas; the failure to take a more
comprehensive macro perspective to address the virus, using bad
heuristics or decision-making tools, relatedly not recognizing the
differential effects of the virus, and adopting herding strategy
(follow-the-leader) when developing policy. Improving the
decision-making environment, inclusive of providing more comprehensive
governance and improving mental models could have lockdowns throughout
the world thus yielding much higher levels of human welfare.”

14. “SARS-CoV-2 waves in Europe: A 2-stratum SEIRS model solution” by
Levan Djaparidze and Federico Lois. MedRxiv pre-print, October 23,
2020. “We found that 180-day of mandatory isolations to healthy <60
(i.e. schools and workplaces closed) produces more final deaths if the
vaccination date is later than (Madrid: Feb 23 2021; Catalonia: Dec 28
2020; Paris: Jan 14 2021; London: Jan 22 2021). We also modeled how
average isolation levels change the probability of getting infected
for a single individual that isolates differently than average. That
led us to realize disease damages to third parties due to virus
spreading can be calculated and to postulate that an individual has
the right to avoid isolation during epidemics (SARS-CoV-2 or any
other).”

15. “Did Lockdown Work? An Economist’s Cross-Country Comparison” by
Christian Bjørnskov. SSRN working paper, August 2, 2020. “The
lockdowns in most Western countries have thrown the world into the
most severe recession since World War II and the most rapidly
developing recession ever seen in mature market economies. They have
also caused an erosion of fundamental rights and the separation of
powers in a  large part of the world as both democratic and autocratic
regimes have misused their emergency powers and ignored constitutional
limits to policy-making (Bjørnskov and Voigt, 2020). It is therefore
important to evaluate whether and to which extent the lockdowns have
worked as officially intended: to suppress the spread of the
SARS-CoV-2 virus and prevent deaths associated with it. Comparing
weekly mortality in 24 European countries, the findings in this paper
suggest that more severe lockdown policies have not been associated
with lower mortality. In other words, the lockdowns have not worked as
intended.”

16.”Four Stylized Facts about COVID-19” (alt-link) by Andrew Atkeson,
Karen Kopecky, and Tao Zha. NBER working paper 27719, August 2020.
“One of the central policy questions regarding the COVID-19 pandemic
is the question of which non-pharmeceutical interventions governments
might use to influence the transmission of the disease. Our ability to
identify empirically which NPI’s have what impact on disease
transmission depends on there being enough independent variation in
both NPI’s and disease transmission across locations as well as our
having robust procedures for controlling for other observed and
unobserved factors that might be influencing disease transmission. The
facts that we document in this paper cast doubt on this premise…. The
existing literature has concluded that NPI policy and social
distancing have been essential to reducing the spread of COVID-19 and
the number of deaths due to this deadly pandemic. The stylized facts
established in this paper challenge this conclusion.”

17. “How does Belarus have one of the lowest death rates in Europe?”
by Kata Karáth. British Medical Journal, September 15, 2020.
“Belarus’s beleaguered government remains unfazed by covid-19.
President Aleksander Lukashenko, who has been in power since 1994, has
flatly denied the seriousness of the pandemic, refusing to impose a
lockdown, close schools, or cancel mass events like the Belarusian
football league or the Victory Day parade. Yet the country’s death
rate is among the lowest in Europe—just over 700 in a population of
9.5 million with over 73 000 confirmed cases.”

18. “Association between living with children and outcomes from
COVID-19: an OpenSAFELY cohort study of 12 million adults in England”
by Harriet Forbes, Caroline E Morton, Seb Bacon et al., by MedRxiv,
November 2, 2020. “Among 9,157,814 adults ≤65 years, living with
children 0-11 years was not associated with increased risks of
recorded SARS-CoV-2 infection, COVID-19 related hospital or ICU
admission but was associated with reduced risk of COVID-19 death (HR
0.75, 95%CI 0.62-0.92). Living with children aged 12-18 years was
associated with a small increased risk of recorded SARS-CoV-2
infection (HR 1.08, 95%CI 1.03-1.13), but not associated with other
COVID-19 outcomes. Living with children of any age was also associated
with lower risk of dying from non-COVID-19 causes. Among 2,567,671
adults >65 years there was no association between living with children
and outcomes related to SARS-CoV-2. We observed no consistent changes
in risk following school closure.”

19. “Exploring inter-country coronavirus mortality“ By Trevor Nell,
Ian McGorian, Nick Hudson. Pandata, July 7, 2020. “For each country
put forward as an example, usually in some pairwise comparison and
with an attendant single cause explanation, there are a host of
countries that fail the expectation. We set out to model the disease
with every expectation of failure. In choosing variables it was
obvious from the outset that there would be contradictory outcomes in
the real world. But there were certain variables that appeared to be
reliable markers as they had surfaced in much of the media and
pre-print papers. These included age, co-morbidity prevalence and the
seemingly light population mortality rates in poorer countries than
that in richer countries. Even the worst among developing nations—a
clutch of countries in equatorial Latin America—have seen lighter
overall population mortality than the developed world. Our aim
therefore was not to develop the final answer, rather to seek common
cause variables that would go some way to providing an explanation and
stimulating discussion. There are some very obvious outliers in this
theory, not the least of these being Japan. We test and find wanting
the popular notions that lockdowns with their attendant social
distancing and various other NPIs confer protection.”

20. “Covid-19 Mortality: A Matter of Vulnerability Among Nations
Facing Limited Margins of Adaptation” by Quentin De Larochelambert,
Andy Marc, Juliana Antero, Eric Le Bourg, and Jean-François Toussaint.
Frontiers in Public Health, 19 November 2020. “Higher Covid death
rates are observed in the [25/65°] latitude and in the [−35/−125°]
longitude ranges. The national criteria most associated with death
rate are life expectancy and its slowdown, public health context
(metabolic and non-communicable diseases (NCD) burden vs. infectious
diseases prevalence), economy (growth national product, financial
support), and environment (temperature, ultra-violet index).
Stringency of the measures settled to fight pandemia, including
lockdown, did not appear to be linked with death rate. Countries that
already experienced a stagnation or regression of life expectancy,
with high income and NCD rates, had the highest price to pay. This
burden was not alleviated by more stringent public decisions. Inherent
factors have predetermined the Covid-19 mortality: understanding them
may improve prevention strategies by increasing population resilience
through better physical fitness and immunity.”

21. “States with the Fewest Coronavirus Restrictions” by Adam McCann.
WalletHub, Oct 6, 2020. This study assesses and ranks stringencies in
the United States by states. The results are plotted against deaths
per capita and unemployment. The graphics reveal no relationship in
stringency level as it relates to the death rates, but finds a clear
relationship between stringency and unemployment.

22. The Mystery of Taiwan: Commentary on the Lancet Study of Taiwan
and New Zealand, by Amelia Janaskie. American Institute for Economic
Research, November 2, 2020. “The Taiwanese case reveals something
extraordinary about pandemic response. As much as public-health
authorities imagine that the trajectory of a new virus can be
influenced or even controlled by policies and responses, the current
and past experiences of coronavirus illustrate a different point. The
severity of a new virus might have far more to do with endogenous
factors within a population rather than the political response.
According to the lockdown narrative, Taiwan did almost everything
‘wrong’ but generated what might in fact be the best results in terms
of public health of any country in the world.”

23. “Predicting the Trajectory of Any COVID19 Epidemic From the Best
Straight Line” by Michael Levitt, Andrea Scaiewicz, Francesco Zonta.
MedRxiv, Pre-print, June 30, 2020. “Comparison of locations with over
50 deaths shows all outbreaks have a common feature: H(t) defined as
loge(X(t)/X(t-1)) decreases linearly on a log scale, where X(t) is the
total number of Cases or Deaths on day, t (we use ln for loge). The
downward slopes vary by about a factor of three with time constants
(1/slope) of between 1 and 3 weeks; this suggests it may be possible
to predict when an outbreak will end. Is it possible to go beyond this
and perform early prediction of the outcome in terms of the eventual
plateau number of total confirmed cases or deaths? We test this
hypothesis by showing that the trajectory of cases or deaths in any
outbreak can be converted into a straight line. Specifically
Y(t)≡−ln(ln(N/X(t)),is a straight line for the correct plateau value
N, which is determined by a new method, Best-Line Fitting (BLF). BLF
involves a straight-line facilitation extrapolation needed for
prediction; it is blindingly fast and amenable to optimization. We
find that in some locations that entire trajectory can be predicted
early, whereas others take longer to follow this simple functional
form.”

24. “Government mandated lockdowns do not reduce Covid-19 deaths:
implications for evaluating the stringent New Zealand response” by
John Gibson. New Zealand Economic Papers, August 25, 2020. “The New
Zealand policy response to Coronavirus was the most stringent in the
world during the Level 4 lockdown. Up to 10 billion dollars of output
(≈3.3% of GDP) was lost in moving to Level 4 rather than staying at
Level 2, according to Treasury calculations. For lockdown to be
optimal requires large health benefits to offset this output loss.
Forecast deaths from epidemiological models are not valid
counterfactuals, due to poor identification. Instead, I use empirical
data, based on variation amongst United States counties, over
one-fifth of which just had social distancing rather than lockdown.
Political drivers of lockdown provide identification. Lockdowns do not
reduce Covid-19 deaths. This pattern is visible on each date that key
lockdown decisions were made in New Zealand. The apparent
ineffectiveness of lockdowns suggests that New Zealand suffered large
economic costs for little benefit in terms of lives saved.”


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