RISKS-LIST: Risks-Forum Digest Tuesday 31 March 2020 Volume 31 : Issue 63

ACM FORUM ON RISKS TO THE PUBLIC IN COMPUTERS AND RELATED SYSTEMS (comp.risks) Peter G. Neumann, founder and still moderator

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Contents:
Covid-19 (Ninghui Li)
Covid-19 is nature's wake-up call to complacent civilisation
(George Monbiot)
Covid-19: 'Nature is sending us a message', says UN environment chief
(The Guardian)
Abridged info on RISKS (comp.risks)

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Date: Tue, 31 Mar 2020 00:53:15 -0400
From: Ninghui Li <ninghui@cs.purdue.edu>
Subject: COVID-19

Ninghui Li, Purdue University

COVID-19 is shaping up to be among the biggest disasters for humanity since World War 2. Hundreds of thousands (if not millions) will die from it, and
the financial damage is going to be trillions of dollars. Like the SARS coronavirus of 2003 and Middle East Respiratory Syndrome coronavirus of
2012, COVID-19 was believed to have originated from coronavirus in
Bats. Clearly, this is not going to be the last time such a pathogen
spillover event occurs, and we need to learn from the experiences with
COVID-19 in order to be better prepared for the inevitable future spillover events.

My March 8 Open Letter

On March 8, I wrote my first open letter on COVID-19, arguing for the urgent need for the United States Governments and the public to immediately adopt aggressive social distancing policies to contain the spreading of COVID-19.
In the letter, I noted that COVID-19 is extremely contagious. From data reported by China, S. Korea, Italy, Germany, Spain, which were the countries that had the most confirmed cases at that time, one can see that once
community spread takes hold, the number of cases starts exponential growth, doubling roughly every 3 days, until aggressive social distancing and other containment efforts were able to slow it down. On March 7, there were 435 confirmed cases in the US. Twenty days later, on Mar 27, that number
surpassed 100,000. In twenty days, the number of confirmed cases in the US grows 240 times, or doubles roughly 8 times. In the March 8 letter, I predicted that "the number of confirmed cases in US will increase at least
10 folds in 10 days, to 4000 or more (and possibly as high as 10,000) by Mar 17." The actual number was 6344. The main uncertainty I had when making
the prediction was to what extent the US testing capacity would pick up
during the 10 days period.

On March 12, I added an executive summary to the open letter, concluding
that: "COVID-19 is 20 times as deadly as flu, and will result in a very high hospitalization rate. Even if just 0.1% of the population (330,000 in the
US) are infected, the health care system will be collapsing. When spreading reaches that point, society and governments will have no choice but to
enforce drastic social distancing practices to curb its spread. This is inevitable. Acting earlier rather than later will lead to much smaller disruption and economic cost, shorter duration of drastic social distancing, and saving the life of thousands or more people."

In the letter, I laid out three scenarios. Scenario A is that "The US government takes decisive and proactive actions today and leads all
countries fighting the potential devastation by COVID-19 in a coordinated effort to enforce aggressive social distancing measures to contain the
spread. Looking at situations in China, this should be able to contain the virus in 4 to 6 weeks. Life should be able to return to normal by June or
July. Total number of cases in the US may be in the tens of thousands, with hundreds of deaths. There will be economic and other kinds of pains and suffering, but these are unavoidable."

Scenario B is that by March 22, the number of confirmed cases will top 10 thousands. Health care systems in states starting with Washington,
California, New York will be strained like Northern Italy today. US
government may have to adopt drastic social distancing measures similar to locking down entire cities. The best case scenario is that the spreading
can still be contained by these measures to be about 10 to 50 times the size
as under Scenario A, i.e., with hundreds of thousands or a few millions of people infected, and thousands or more deaths. It will take longer for the lockdown effort to be effective because of the scale of spreading. It may be August or September before life can return to normal. And the economic
damage will be a lot higher than Scenario A.

Scenario C, the worst-case scenario, is that spreading cannot be contained,
and we are looking at situations predicted by some experts, with up to 70%
of the population infected [1]. Local communities will still try any conceivable containment method. Economic and social activities will be
greatly disrupted. At least 20% of the population over the age of 70, as
well as significant fractions of other age groups, will die while waiting
for medical care, with family members desperately looking on. The situation looks to be at least as bad as the Spanish flu. We may be looking at the
worst humanity and economic disaster since World War 2. The remaining hope after the devastation is that either virus mutates to a milder form, or effective vaccines can be developed before the next wave hits.

In the early morning of March 9, I sent the letter to the Purdue University administration, Senators from Indiana, the congressman representing my district, and the white house website, hoping against hope that it would be able to wake up the government to the rapidly approaching disaster. Later I sent the letter to local news outlets and leading newspapers in the nation.
I was happy and emotional when on March 13 I saw the news conference where a national emergency in the United States was declared and leaders of the healthcare industry and government officials came together to unite to fight COVID-19. However, my optimism was short lived. I gradually realized that actions taken by governments at all levels and the society as a whole were still too little, too late.

My March 14 Open Letter.

By Mar 12, many people are sounding the alarm for immediate action, see,
e.g. [2]. At the same time, the United Kingdom and Germany appeared to be adopting the Herd Immunity approach of letting the virus spread. (They
changed course a few days later.) I had also received some questions and feedback from readers of my first letter. I therefore wrote a second open letter on Mar 14, aiming to convince the public that it is the duty and responsibility for every one of us to conduct the most aggressive social distancing measures we can afford.

In the letter I claimed that COVID-19 "Must and Will Be Eradicated Like SARS
of 2003". I wrote that "Letting COVID-19 spread will overwhelm the
healthcare system and lead to devastation humanity cannot afford. COVID-19
must be eliminated. In my opinion, this deadly combination of extreme contagiousness and heavy burden on the medical system also means that
COVID-19 will be eliminated, for the reason that humanity simply cannot let COVID-19 continue to exist."

To illustrate these points, I introduced a simplified, generational model to analyze the spreading of COVID-19. This model assumes that patients come in generations. There are three key parameters, with any two determining the third.

-- G, the number of days from when a patient is infected to the time when
one is no longer a patient. I assumed that the G value of COVID-19 is 14
days; the exact number does not affect the message of the analysis.

-- B, the number of new cases, on average, an infected person will cause
during their generation duration. The parameter B is closely related to
the reproduction number R0 commonly used when studying infectious
diseases. However, since my model is much simplified, it is not the same
thing.

-- Daily Transmission Rate (DTR), which is B/G, assuming that a patient is
contagious throughout the period. Roughly, the DTR is the average number
of new patients infected by one existing patient per day.

Using this simplified model for COVID-19, starting with X patients in Generation 0, then we have X*B number of patients 14 days later (Generation
1), and X*B*B patients 28 days later. Clearly, if B>1, we have an
exponential growth, and if B<1, we have exponential decay. For COVID-19 to double every 3 days under this model, we need DTR of (2^(14/3))/14, or about 1.8. A more realistic model should consider the dynamics of daily patient population and the changes of infectiousness over the period one is
infected. However, that would be more complex and difficult to explain.
The message is the same whichever model we use.

If the society can reduce DTR to 0.02. After one cycle of 14 days, the
patient population size is 0.02*14 = 0.28 that of the original. Even
starting from an infected population of 3.3 million, exponential decay can bring the number down pretty quickly.

Time
Day 0 Day 14 Day 28 Day 42 Day 56 Day 70 Day 84 Day 96 Infected population size
3,300,000 924,000 259,000 72,000 20,000 5769 1590 445

Somewhere around Day 60, when there are less than 20000 patients, if people
at risk are thoroughly tested so that the public can clearly identify almost all patients, then only the patients need to be isolated, the rest of the public can go back to normal life. After Day 96, it would take 3 more
cycles for the number of patients to be in single digits. But very few people's life needs to be affected in that phase.

Another observation from this simple model is that, due to the rapid exponential growth, the only rational choice is to apply all feasible aggressive social distancing (ASD) measures at once. No matter how much ASD effort we have already taken, any additional measure that has reasonable
cost will pay for itself, because it will greatly shorten the time it takes
to contain and eradicate COVID-19. If the measures already taken are unable
to reduce the DTR to less than 0.075, we will still see exponential growth, just at a slower rate. Below that, reducing DTR further will greatly
shorten the time it takes to eradicate it. The following table shows the
effect of different DTR in the generational model.

DTR 0.07 0.06 0.05 0.04 0.03 0.02 0.01 #cycles to half pop. size 34 4 2 1.2 0.8 0.54 0.35

Reducing DTR from 0.06 to 0.05 shortens the time it takes to contain
COVID-19 by half. Reducing DTR to 0.04, further shortens it by 40%.

Reducing Daily Transmission Rate (DTR) is something every one of us can contribute to. By reducing DTR, we help the society to contain and
eradicate COVID-19 faster, reducing the total cost caused by ASD
measures. Any action that risks transmission of COVID-19 adds cost to the society, because it extends the time it takes to contain the virus. The
letter thus calls for everyone to practice the most aggressive social distancing measure one can afford, and convince more people to do the same.

This analysis also illustrates the difficulty of the herd immunity approach. COVID-19 has a DTR of 1.8 (based on 14 days generation and doubling every 3 days). If we want to flatten the curve to the extent that the number of
cases doubles every 30 days instead of every 3 days, we need to reduce DTR
from 1.8 to 214/30 (about 0.1). This requires a very high reduction of the level of human contact, and can be achieved only with very high societal
cost. If the society is able to reduce it from 1.8 to 0.1, then a further reduction to 0.03 or lower will lead to rapid exponential decay of the
number of cases. Furthermore, no one knows the exact degree of
effectiveness of the different social distancing measures. If society is already paying the huge financial and societal cost of shutting down most of the business and other activities, the rational choice is to apply the most stringent ASD measures, in order to shorten the period of suffering.

As of the end of March, my prediction that COVID-19 will be eradicated
appears to be wrong. It has spread to everywhere in the world, and most responses are too late, too little. In my opinion, this is because COVID-19
is not deadly enough for societies to quickly mobilize and act decisively to eradicate it. Somewhat ironically, had COVID-19 been 5 times more deadly,
it probably would have been fully contained by now, because we would have
acted earlier and more decisively. Had COVID-19 been 10 times less deadly, letting it spread to gain herd immunity is perhaps the right approach.
Either situation would have resulted in much less cost to humanity than COVID-19 does now.

If COVID-19 cannot be eradicated, it appears that the most likely outcome is that aggressive social distancing mechanisms will bring it under control, followed by vigilant containment effort to keep it under control, until effective vaccines are developed and widely administered. While experts
insist that at minimal it takes between 12 and 18 months to deploy vaccines,
I want to note that while the 2009 pandemic HIN1 flu virus was identified in April 2009, vaccines for it were widely administered by November 2009.

Cybersecurity Knowledge for Analyzing Pandemics

My research interests are in cybersecurity and data privacy. While I was
aware of the impact of the COVID-19 in China, I started examining COVID-19 numbers carefully only on Feb 27. My son signed up for his high school orchestra's Spring break (Mar 14 to 21) trip to Orlando, FL, and I needed to decide whether he could go. He had been looking forward to the trip for a year, so I needed solid reasons if I decided that he could not go. The
case numbers in the US were low then. On Feb 29, CDC reported there were 22 domesticate cases and 47 cases from evacuees from Wuhan and the Diamond Princess cruise ship. However, the numbers from other countries where
COVID-19 started spreading earlier showed a clear exponential growth. Each
day the number of total cases is about 4 times the number of new cases for
that day, meaning approximately 33% daily growth rate. This pattern eerily held for every European country with significant number of cases every day.
The growth reminded me of the early phases of internet worms spreading.
When I saw the US case number started this trend on Mar 1, and the trend continued the next day, I immediately knew that the situation will be dire unless drastic actions are taken. While I easily convinced my son that he should not go (he understands the power of exponential growth), I was hooked
to looking at the numbers every day.

I started engaging others in the discussions on COVID-19 on March 3, mostly
on wechat groups, with my former classmates from high school and college,
and other professional groups and alumni groups. I found that few people
saw the same things as I did. I had resigned to simply sit back and watch
the situation develop. However, after reading Testimony of a Surgeon
working in Bergamo on Mar 8, I was deeply touched. The doctor's
first-person account turned the abstract numbers in my mind into vivid human suffering. I felt anxious and frustrated. I told my friends: "I am
watching a train wreck going to happen, yet can do nothing to help." After sending that message, I began to think maybe there is something I could
do. I could share my observations and analysis and try to influence the situation.

Later I reflected on why so many people did not see the danger. Most people
I interacted with about COVID-19 are highly educated and highly intelligent. Many have PhDs, and the majority of others are successful professionals in
high tech companies. Gradually I realized that my professional experiences
of learning, teaching and conducting research on cybersecurity does provide some perspectives that are not widely shared.

Perhaps the most important factor is my experiences teaching the spreading
of internet worms such as the Morris Worm, the Code Red worm, Nimda, the SQL Slammer, etc. That was one of my favorite topics to teach in courses on security. These provide vivid examples of pandemic. All follow the same
trend of rapid exponential growth until saturation. For example, CodeRed
worm infected more than 359,000 computers in less than 14 hours [3]. SQL Slammer, which targets Microsoft's SQL Server and Desktop Engine database products, was able to infect most of the 75,000 vulnerable targets on the internet in mere 10 minutes [4]. Having these concrete examples helps me
see the inevitability and danger of exponential spreading. Without concrete examples, even if one intellectually knows that exponential growth is occurring, one may not share the same conviction and urgency. In addition,
the main damages of these internet worms are in the form of resource exhaustion. For COVID-19, the critical resource is health care capacity.
Once I saw the accounts from the Italian doctor, I knew the situation is
only going to get worse, and is going to happen everywhere if COVID-19 is
not controlled.

There are other more subtle, but perhaps equally important influences from
my cybersecurity experiences. First, in security one does not just look at what has happened and is happening, but constantly need to think about what will happen under different adversarial capabilities and defense mechanisms. Indeed, when security is done right, what one sees is that nothing happens, because the bad things are prevented. On the contrary, many people just look
at what is happening now. This is amply reflected in the vast majority of journalistic reports on COVID-19. Second, when the situation is not dire,
many people just instinctively feel that the worst-case scenario won't
happen, even if there is no evidence to support that. However, the more experiences one has in security, the less room one has for wishful thinking regarding threats. When I taught cryptography, I often told students that
one of the goals of the course is to scare them enough so that they will not develop a cipher themselves and think it is secure, and will assume any algorithm that has not been subjected to rigorous scrutiny and cryptanalysis
is insecure. Similarly in software security, many vulnerabilities appear extremely difficult to be exploited, yet inevitably people find out ways to exploit them.

Suggestions for the Fields of Epidemiology and Infectious Diseases

In my opinion, many (perhaps most) experts in the fields of epidemiology and infectious diseases have underestimated the danger of COVID-19, at least
until COVID-19 hits their respective countries and regions hard. The same
is true for the general public and many politicians. In my opinion, one
reason is that the main terminology and concepts that are used in these
fields hide rather than illustrate the danger of fast-spreading viruses like COVID-19. These terminologies and concepts also permeate reporting and
public discussions on COVID-19, obscuring the true danger of COVID-19.
Below I list three suggestions for these fields.

Suggestion 1. Use the concept of Base Doubling Period for measuring the contagious diseases.

By Base Doubling Period, I mean the number of days it takes for the number
of cases to double without intervention. It is analogous to the notion of half-life for radioactive decay. Currently, the main measure of disease contagiousness is the base reproduction number R0, which is the average
number of people who will catch a disease from one contagious person. While
R0 is important, it fails to capture the time aspect of transmission, i.e.,
how long does it take for new patients to be infected. For example, HIV has
an estimated R0 of 2-5, which is actually higher than the estimation of COVID-19 (1.4 to 3.9) [5]. However, transmitting HIV to 2-5 patients may happen over years, and the COVID-19 transmissions take days or a few weeks
at the most.

Had the experts focused their attention on estimating the Base Doubling
Period instead of R0, the world likely would have realized earlier that the Base Doubling Period of COVID-19 is between 2 and 3 days. For the public
and government officials, something that doubles every 3 days is clearly an urgent matter that needs to be dealt with promptly. Something that has an
R0 of 3, on the hand, is unlikely to induce urgency, especially since many diseases have much higher R0 (e.g., measles has R0 between 12 and 18).

Suggestion 2. Use Cumulative Fatality Rate instead of Case Fatality Rate for diseases that are actively spreading.

Case fatality rate is computed by the number of deaths divided by the total number of confirmed cases. I have seen countless news articles discussing
the case fatality rates of this and that nations. For example, one article
on March 5 marvelled at the fact that S. Korea's death rate is just 0.6%,
far lower than in China. However, the total number of confirmed cases is
the sum of three numbers: death, recovered, and active. Since the active
cases will result in either death or recovery in the future, dividing by the total number gives a distorted and falsely optimistic picture of fatality.
By Cumulative Fatality Rate, I mean the number of deaths divided by the sum
of the number of deaths and the number of recovered. The following table
gives a snapshot as of Mar 29, from
https://www.worldometers.info/coronavirus/. I added the two fatality rates.

Country Total Cases Deaths Recovered Active Case_Fatality_R Cumu._Fatality_R USA 141,812 2,475 4,435 134,902 1.7% 35.8%
Italy 97,689 10,779 13,030 73,880 11.0% 45.3%
China 81,439 3,300 75,448 2,691 4.1% 4.4%
Spain 80,110 6,803 14,709 58,598 8.5% 31.6%
Germany 62,095 541 9,211 52,343 0.9% 5.5%
France 40,174 2,606 7,202 30,366 6.5% 26.6%
UK 19,522 1,228 135 18,159 6.3% 90.1%
S. Korea 9,583 152 5,033 4,398 1.6% 2.9%

For a nation where most of the cases have been resolved (such as China), the two rates are very similar. Of course, both rates will be distorted by how many people are tested; Cumulative Fatality Rate, however, does not artificially bias the number by implicitly assuming that all active cases
will live. An unusually high Cumulative Fatality Rate can be caused by
three factors: (1) under testing so that many mild or asymptomatic cases are not discovered; (2) a population with high percentage of older people; and
(3) hospital systems overwhelmed so that many patients that could have been saved are not. In my opinion, the above numbers from the USA and UK are primarily influenced by under testing, and the degree of under testing is
much worse in the UK than the USA. Italy is affected by all three factors. China locked down Wuhan and other cities in the Hubei province on January
23, forbidding people to leave the cities. At the same time, most other provinces are enforcing versions of Stay at Home, with various other containment mechanisms. While these actions have huge costs, both
economically and in terms of personal liberty, they resulted in low numbers
of cases in other provinces. As a result, while hospitals in Wuhan were overwhelmed in late January, China was able to mobilize 42,600 doctors and nurses from around the country to move to Hubei to treat the 67,000 patients there.

S. Korea conducted the most thorough testing. See [6] for daily reports from Korea CDC. In the 3 weeks from Mar 8 to Mar 29, S. Korea conducted around 205,600 tests, with 2449 positive cases, for a 1.2% positive rate. As of
Mar 29, S. Korea conducted 379,113 tests, with 9,583 positive cases, for a
2.5% positive rate. The low positive rates and the fact the number of new cases increases very slowly in S. Korea both suggest that few cases are
missed. Thus S. Korea data provides a good source for estimating the
eventual case fatality rate, which will be between 1.6% and 2.9%. My estimation is that it is likely to be close to 2.5% at the end.

Some researchers use the number of deaths divided by the number of confirmed cases on a day in the past (e.g., 14 days in the past) to deal with the
problem of incorrect estimation. That approach, however, requires
additional assumptions regarding the average length from detection of a case
to outcome (either death or recovered). It can result in fatality rate of higher than 100% when there were severe under testing at earlier stage.
The cumulative fatality rate is both easy to understand and easy for anyone
to compute based on one day's data. It also avoids the needs for estimating other parameters.

Suggestion 3. Use mortality rate instead of case fatality rate when
comparing a new virus (such as COVID-19) with existing diseases such as the seasonal flu.

According to Encyclopaedia Britannica, mortality rate is computed by
dividing the number of deaths by the population at risk during a certain
time frame. Many experts have used 0.1% as the fatality rate for seasonal
flu when comparing with COVID-19, and I did the same in my first open
letter. US CDC's estimation of 0.1% case fatality rate was obtained by
using estimated death due to flu for one year (averaging between 30,000 and 40,000) divided by the estimated number of flu cases, which is around 10% of the population in the US [9]. That is, the 0.1% rate is based on the
assumption that about 90% of the population do not get the flu in one year.
In other words, the mortality rate of flu in one year is 0.01%. I also want
to note that CDC's estimation of case fatality rate for the 2009 pandemic
H1N1 virus in the United States is 0.02%, based on the assumption that 60 million people were infected [10].

If one understands that the mortality rate of flu is 0.01%, one would not consider a fatality rate of between 1% or 2% to be low. When letting
COVID-19 spread for one year, it is reasonable to expect that at least 50%
of the population will be infected since people have no immunity and
COVID-19 spreads very fast. The WHO estimates a case fatality rate of 3.4%, and China's CDCP estimated numbers are 2.3%. Both estimates have tried to
take into account that there are asymptomatic cases that were missed, since China's cumulative fatality rate is 4.4%. These estimations are also
broadly consistent with what one can estimate using data from S. Korea.
Using a 2% fatality rate on 50% of the population yields 1% mortality rate, which is more than 100 times that of flu. Comparing COVID-19's 2% case fatality rate with the 0.02% case fatality rate of 2009 H1N1 virus also
yields a difference of 100 times.

If one assumes that even S. Korea's rigorous testing regime misses
asymptomatic cases, one can further adjust that using the data point from
the Diamond Princess Cruise Ship, where everyone was tested. Among the 712 persons who were tested positive, 331, or 46.5% were asymptomatic. As of
March 30, a total of 11 patients died, including 1 who died after returning
to Australia. Another 11 patients are still being treated on ventilators.
Even if one estimates 50% of infected have symptoms, and fatality rate of symptomatic cases is 2%, that yields an infection fatality rate of 1%, that still makes COVID-19 50 times more deadly than the seasonal flu.

Why COVID-19 is so Dangerous?

COVID-19 is so dangerous because it is about 50 to 100 times more deadly
than the seasonal flu, and has a Base Doubling Period of slightly less than
3 days. If we let COVID-19 spread without control, it would result in at
least 25% of the population being infected all at around the same time after
it spreads for a few months, because most patients are infected near the end
of the exponential growth. Using CDC's study from early COVID-19 cases in
the US [8], for every death, about 4 patients need ICU, and 11 need hospitalization. Even using a conservative 1% infection fatality rate, that means for every 1000 people, 27.5 need hospital beds and 10 need ICU. US
has 2.9 hospital beds per 1000 people, and the number of adult intensive
care beds is about 10% of that. This means that the vast majority of
patients cannot get hospital care.

Estimating the fatality rate for COVID-19 patients when they do not receive hospital care is difficult. My estimation is that it is at least 5 times
the normal fatality rate, i.e., 5 times of the normal rate, based on
comparing fatality data from Hubei province and other provinces in China. Assuming that 50% to 70% of population will be infected, and using a conservative 1% infection fatality rate, we are looking between 2.5% and
3.5% of population dying from COVID-19, which is similar to the estimated mortality rate of the 1918 Spanish Flu.

Is The Cost of Fighting COVID-19 Worse Than the Effect of COVID-19?

I was pointed to [11], a webpage in which several experts argue that the actions of governments and society are overreacting. I listened to the interview with the first expert, Dr. Sucharit Bhakdi, who according to [8],
is a professor at the Johannes Gutenberg University in Mainz and head of the Institute for Medical Microbiology and Hygiene and one of the most cited research scientists in German history. He claimed that "[The German government's anti-COVID19 measures] are grotesque, absurd and very
dangerous", and "All these measures are leading to self-destruction and collective suicide based on nothing but a spook."

Dr. Bhakdi's main arguments are as follows. As of the time of the interview (around Mar 17-18), there were about 10,000 cases in Germany and 30 deaths. Therefore, in the worst case, there will be 1 million infections and 3000 deaths over 100 days, for an average of 30 deaths per day. The high death rates in China and Italy were because those places have horrific air pollutions, and the population there have damaged lungs, making them more vulnerable.

I hope that the errors in the above analysis are obvious by now. As of Mar
30, Germany has 645 total deaths, and the daily number of deaths is 108 on
Mar 29 and 104 on Mar 30, surpassing the above "worst-case estimates" of 30 deaths per day. This is after Germany took a series of increasingly
aggressive social distancing actions from March 13 to March 22, culminating with forbidding the gatherings of more than two people in public starting
March 22. Allowing COVID-19 to spread at its native speed of doubling every
3 days would likely lead to 20 million infections in Germany by late April
and tens of thousands of death due to COVID-19 each day throughout May. Germany's aggregative social distancing policies, however, appear to be
working now, as the numbers of new cases each day have dropped from the peak
on March 29 and 30.

What blinded such an acclaimed scientist and expert to make such mistakes? First, he was looking at static data on one particular day, ignoring the exponential growth. Second, he used case fatality rate, which means nothing for a fast spreading pathogen like COVID-19 when it is spreading. In my opinion, textbooks in the field of Epidemiology and Infectious Diseases need
to be revised so that the next generation of scientists in the field will
not make the same mistakes.

Many experts have corrected pointed out that the social and economical costs
of the current measures is astronomical, and question whether it is worth
it. Yes, diseases that are more deadly have hit humanity before, without
the world mobilized to the extent we are seeing now. Yes, most deaths from COVID-19 will be from older people and people with other underlying

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