The RISKS Digest
Volume 31 Issue 89

Wednesday, 27th May 2020

Forum on Risks to the Public in Computers and Related Systems

ACM Committee on Computers and Public Policy, Peter G. Neumann, moderator

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Contents

Faulty Equipment, Lapsed Training, Repeated Warnings: How a Preventable Disaster Killed Six Marines
Propublica
A Case for Cooperation Between Machines and Humans
NYTimes
COVID-19: ‘Evidence Fiasco’
John P.A. Ioannidis
The Pandemic Is Exposing the Limits of Science
Bloomberg
COVID-19: Half of Canadians think their governments are deliberately hiding information
CA National Post
White House and Twitter
sundry sources
Re: Map Reveals Distrust in Health Expertise Is Winning …
anthony
Re: Misinformation
Amos Shapir
Info on RISKS (comp.risks)

Faulty Equipment, Lapsed Training, Repeated Warnings: How a Preventable Disaster Killed Six Marines (Propublica)

Gabe Goldberg <gabe@gabegold.com>
Wed, 27 May 2020 01:13:34 -0400

https://www.propublica.org/article/marines-hornet-squadron-242-crash-pacific-resilard

The Navy installed touch-screen steering systems to save money.

Ten sailors paid with their lives.

“Usually when we have a fault with that system,” Sanchez said, “their resolution is to reboot the system.”

https://features.propublica.org/navy-uss-mccain-crash/navy-installed-touch-screen-steering-ten-sailors-paid-with-their-lives/ https://features.propublica.org/navy-accidents/us-navy-crashes-japan-cause-mccain/ https://features.propublica.org/navy-accidents/uss-fitzgerald-destroyer-crash-crystal/


A Case for Cooperation Between Machines and Humans (NYTimes)

Gabe Goldberg <gabe@gabegold.com>
Wed, 27 May 2020 20:22:04 -0400

A computer scientist argues that the quest for fully automated robots is misguided, perhaps even dangerous. His decades of warnings are gaining more attention.

https://www.nytimes.com/2020/05/21/technology/ben-shneiderman-automation-humans.html


COVID-19: ‘Evidence Fiasco’ (John P.A. Ioannidis)

Henry Baker <hbaker1@pipeline.com>
Wed, 27 May 2020 11:15:44 -0700

We were warned about overreaction by an actual epidemic expert.

Note the date on this article: the same day that Prof. Ferguson presented his Imperial model to the UK PM Boris Johnson in person—and the infected Ferguson himself probably gave Boris his case of COVID-19! How ironic! Ferguson himself a superspreader?

(You can't make this stuff up. Netflix writers please note this delicious detail.)

https://www.statnews.com/2020/03/17/a-fiasco-in-the-making-as-the-coronavirus-pandemic-takes-hold-we-are-making-decisions-without-reliable-data/

John P.A. Ioannidis, A fiasco in the making? 17 Mar 2020 As the coronavirus pandemic takes hold, we are making decisions without reliable data

The current coronavirus disease, Covid-19, has been called a once-in-a-century pandemic. But it may also be a once-in-a-century evidence fiasco.

At a time when everyone needs better information, from disease modelers and governments to people quarantined or just social distancing, we lack reliable evidence on how many people have been infected with SARS-CoV-2 or who continue to become infected. Better information is needed to guide decisions and actions of monumental significance and to monitor their impact.

Draconian countermeasures have been adopted in many countries. If the pandemic dissipates—either on its own or because of these measures — short-term extreme social distancing and lockdowns may be bearable. How long, though, should measures like these be continued if the pandemic churns across the globe unabated? How can policymakers tell if they are doing more good than harm?

Vaccines or affordable treatments take many months (or even years) to develop and test properly. Given such timelines, the consequences of long-term lockdowns are entirely unknown.

The data collected so far on how many people are infected and how the epidemic is evolving are utterly unreliable. Given the limited testing to date, some deaths and probably the vast majority of infections due to SARS-CoV-2 are being missed. We don't know if we are failing to capture infections by a factor of three or 300. Three months after the outbreak emerged, most countries, including the U.S., lack the ability to test a large number of people and no countries have reliable data on the prevalence of the virus in a representative random sample of the general population.

This evidence fiasco creates tremendous uncertainty about the risk of dying from Covid-19. Reported case fatality rates, like the official 3.4% rate from the World Health Organization, cause horror—and are meaningless. Patients who have been tested for SARS-CoV-2 are disproportionately those with severe symptoms and bad outcomes. As most health systems have limited testing capacity, selection bias may even worsen in the near future.

The one situation where an entire, closed population was tested was the Diamond Princess cruise ship and its quarantine passengers. The case fatality rate there was 1.0%, but this was a largely elderly population, in which the death rate from Covid-19 is much higher.

Projecting the Diamond Princess mortality rate onto the age structure of the U.S. population, the death rate among people infected with Covid-19 would be 0.125%. But since this estimate is based on extremely thin data—there were just seven deaths among the 700 infected passengers and crew—the real death rate could stretch from five times lower (0.025%) to five times higher (0.625%). It is also possible that some of the passengers who were infected might die later, and that tourists may have different frequencies of chronic diseases—a risk factor for worse outcomes with SARS-CoV-2 infection—than the general population. Adding these extra sources of uncertainty, reasonable estimates for the case fatality ratio in the general U.S. population vary from 0.05% to 1%.

That huge range markedly affects how severe the pandemic is and what should be done. A population-wide case fatality rate of 0.05% is lower than seasonal influenza. If that is the true rate, locking down the world with potentially tremendous social and financial consequences may be totally irrational. It's like an elephant being attacked by a house cat. Frustrated and trying to avoid the cat, the elephant accidentally jumps off a cliff and dies.

Could the Covid-19 case fatality rate be that low? No, some say, pointing to the high rate in elderly people. However, even some so-called mild or common-cold-type coronaviruses that have been known for decades can have case fatality rates as high as 8% when they infect elderly people in nursing homes. In fact, such “mild” coronaviruses infect tens of millions of people every year, and account for 3% to 11% of those hospitalized in the U.S. with lower respiratory infections each winter.

These “mild” coronaviruses may be implicated in several thousands of deaths every year worldwide, though the vast majority of them are not documented with precise testing. Instead, they are lost as noise among 60 million deaths from various causes every year.

Although successful surveillance systems have long existed for influenza, the disease is confirmed by a laboratory in a tiny minority of cases. In the U.S., for example, so far this season 1,073,976 specimens have been tested and 222,552 (20.7%) have tested positive for influenza. In the same period, the estimated number of influenza-like illnesses is between 36,000,000 and 51,000,000, with an estimated 22,000 to 55,000 flu deaths.

Note the uncertainty about influenza-like illness deaths: a 2.5-fold range, corresponding to tens of thousands of deaths. Every year, some of these deaths are due to influenza and some to other viruses, like common-cold coronaviruses.

In an autopsy series that tested for respiratory viruses in specimens from 57 elderly persons who died during the 2016 to 2017 influenza season, influenza viruses were detected in 18% of the specimens, while any kind of respiratory virus was found in 47%. In some people who die from viral respiratory pathogens, more than one virus is found upon autopsy and bacteria are often superimposed. A positive test for coronavirus does not mean necessarily that this virus is always primarily responsible for a patient's demise.

If we assume that case fatality rate among individuals infected by SARS-CoV-2 is 0.3% in the general population—a mid-range guess from my Diamond Princess analysis—and that 1% of the U.S. population gets infected (about 3.3 million people), this would translate to about 10,000 deaths. This sounds like a huge number, but it is buried within the noise of the estimate of deaths from “influenza-like illness.” If we had not known about a new virus out there, and had not checked individuals with PCR tests, the number of total deaths due to “influenza-like illness” would not seem unusual this year. At most, we might have casually noted that flu this season seems to be a bit worse than average. The media coverage would have been less than for an NBA game between the two most indifferent teams.

Some worry that the 68 deaths from Covid-19 in the U.S. as of March 16 will increase exponentially to 680, 6,800, 68,000, 680,000 … along with similar catastrophic patterns around the globe. Is that a realistic scenario, or bad science fiction? How can we tell at what point such a curve might stop?

The most valuable piece of information for answering those questions would be to know the current prevalence of the infection in a random sample of a population and to repeat this exercise at regular time intervals to estimate the incidence of new infections. Sadly, that's information we don't have.

In the absence of data, prepare-for-the-worst reasoning leads to extreme measures of social distancing and lockdowns. Unfortunately, we do not know if these measures work. School closures, for example, may reduce transmission rates. But they may also backfire if children socialize anyhow, if school closure leads children to spend more time with susceptible elderly family members, if children at home disrupt their parents ability to work, and more. School closures may also diminish the chances of developing herd immunity in an age group that is spared serious disease.

This has been the perspective behind the different stance of the United Kingdom keeping schools open, at least until as I write this. In the absence of data on the real course of the epidemic, we don't know whether this perspective was brilliant or catastrophic.

Flattening the curve to avoid overwhelming the health system is conceptually sound—in theory. A visual that has become viral in media and social media shows how flattening the curve reduces the volume of the epidemic that is above the threshold of what the health system can handle at any moment.

Yet if the health system does become overwhelmed, the majority of the extra deaths may not be due to coronavirus but to other common diseases and conditions such as heart attacks, strokes, trauma, bleeding, and the like that are not adequately treated. If the level of the epidemic does overwhelm the health system and extreme measures have only modest effectiveness, then flattening the curve may make things worse: Instead of being overwhelmed during a short, acute phase, the health system will remain overwhelmed for a more protracted period. That's another reason we need data about the exact level of the epidemic activity.

One of the bottom lines is that we don't know how long social distancing measures and lockdowns can be maintained without major consequences to the economy, society, and mental health. Unpredictable evolutions may ensue, including financial crisis, unrest, civil strife, war, and a meltdown of the social fabric. At a minimum, we need unbiased prevalence and incidence data for the evolving infectious load to guide decision-making.

In the most pessimistic scenario, which I do not espouse, if the new coronavirus infects 60% of the global population and 1% of the infected people die, that will translate into more than 40 million deaths globally, matching the 1918 influenza pandemic.

The vast majority of this hecatomb would be people with limited life expectancies. That's in contrast to 1918, when many young people died.

One can only hope that, much like in 1918, life will continue. Conversely, with lockdowns of months, if not years, life largely stops, short-term and long-term consequences are entirely unknown, and billions, not just millions, of lives may be eventually at stake.

If we decide to jump off the cliff, we need some data to inform us about the rationale of such an action and the chances of landing somewhere safe.

John P.A. Ioannidis is professor of medicine and professor of epidemiology and population health, as well as professor by courtesy of biomedical data science at Stanford University School of Medicine, professor by courtesy of statistics at Stanford University School of Humanities and Sciences, and co-director of the Meta-Research Innovation Center at Stanford (METRICS) at Stanford University.

John P.A. Ioannidis <jioannid@stanford.edu> @METRICStanford


The Pandemic Is Exposing the Limits of Science (Bloomberg)

geoff goodfellow <geoff@iconia.com>
Wed, 27 May 2020 05:11:55 -1000

The financial crisis tarnished the field of economics. Will the coronavirus do the same for medicine?

The 2008 financial crisis led the public to discover the limits of economics. The Covid-19 pandemic risks having the same effect on scientists and medical doctors.

Since the start of the outbreak, citizens have struggled to get clear answers to some basic questions. Consider masks, for example: The World Health Organization said <https://www.who.int/emergencies/diseases/novel-coronavirus-2019/advice-for-public/when-and-how-to-use-masks> early on that there was no point in encouraging healthy people to use them, but now most doctors agree that widespread mask-wearing is a good idea. There was also confusion around lockdowns: In the U.K., scientists argued <https://www.bbc.com/news/science-environment-51892402> for weeks over the merits of closing businesses and keeping people at home—a quarrel that may have cost the country lives. And now that the outbreak is fading in Italy, there is growing debate between the country's public health experts and doctors over whether the virus has lost strength or remains just as deadly.

These disputes are only natural since we are dealing with a novel coronavirus that caught most Western health-care systems off-guard. Meanwhile, scientists across the world have raced to share data, and a number of companies have ramped up work <https://www.bloomberg.com/features/2020-coronavirus-drug-vaccine-status/> on a vaccine, which could be one of the fastest-developed in human history.

And yet, the pandemic has reminded us that science—and medicine in particular—has limits. In a way, the last few months have resembled what occurred in the 2008 crisis, as economists fought over the right response to the crash. The academic community split between those who said the U.S. government should save all large banks and those who said it should let Lehman Brothers go bust. In Europe, the controversy centered around whether countries should pursue austerity or run large-scale budget deficits. These divisions, and the ensuing policy mistakes, dented economists' reputation in the eyes of the general public. […]

https://www.bloomberg.com/opinion/articles/2020-05-25/coronavirus-the-pandemic-is-exposing-the-limits-of-scientists https://finance.yahoo.com/news/pandemic-exposing-limits-science-050003058.html


COVID-19: Half of Canadians think their governments are deliberately hiding information (CA National Post)

geoff goodfellow <geoff@iconia.com>
Wed, 27 May 2020 05:13:55 -1000

Some also believe conspiracy theories about where the novel coronavirus began

Half of Canadians believe they're not getting the whole truth from their governments about COVID-19, a new poll suggests, and some also believe conspiracy theories about where the novel coronavirus began.

The most recent survey from Leger and the Association for Canadian Studies found 50 per cent of respondents felt governments were deliberately withholding information about the pandemic of the novel coronavirus, which has killed thousands and ground the economy to a halt.

“It's staggering, in a period where I believe trust has never been as high,” said Leger vice-president Christian Bourque. […] https://nationalpost.com/news/canada/half-of-canadians-say-governments-are-hiding-something-about-covid-19-poll


White House and Twitter (sundry sources)

Lauren Weinstein <lauren@vortex.com>
Wed, 27 May 2020 14:53:49 -0700

White House urges harassment, attacks on Twitter employee https://www.engadget.com/twitter-employee-targeted-harassment-trump-fact-check-210300269.html

Twitter ‘Deeply Sorry’ about Trump's Morning Joe Tweets, Plans Policy ‘Changes’ https://www.nationalreview.com/news/twitter-deeply-sorry-about-trumps-morning-joe-tweets-plans-new-policy-changes-to-address-things-like-this/

[OK, that's a start—but talk and tweets are cheap. Let's see the details of the changes and how they are enforced. -L]

Trump threatens to shut down social-media platforms after Twitter put a fact-check warning on his false tweets https://www.businessinsider.com/trump-threatens-shut-down-platforms-after-tweets-tagged-warning-2020-5

[… the First Amendment is specifically designed to prevent such “close down” actions. … L]

Apparently for the first time, Twitter flags a tweet by Trump—this time his false rants about mail-in ballets—and added a “get the facts about mail-in ballots” link on his tweet.

Trump flips out on Twitter, right after Twitter fact-checked him for the first time (BoingBoing) https://boingboing.net/2020/05/26/trump-flips-on-on-twitter-fact.html


Re: Map Reveals Distrust in Health Expertise Is Winning … (Vilkaitis, RISKS-31.88)

anthony <antmbox@youngman.org.uk>
Wed, 27 May 2020 11:07:33 +0100

Denying “anecdata” as I call it is also a major problem. Years ago there was a program on Radio 4 where they said that government statistics claimed “no-one has died from the Rubella vaccine”. The program gave an example of a boy who had had the vaccine, gone home, slipped in to a coma, and died 4 weeks later. But because government guidelines state that “if it doesn't happen within three weeks, it's unrelated”, they were adamant that it wasn't down to the vaccine. Likewise an example given of a girl who walked in to the doctor's surgery for the vaccine, left in a wheelchair, and never walked again. But oh no, “it can't be the vaccine's fault”.

And I have personal experience of this within my circle of friends—a friend's son had his childhood vaccinations, came home and started behaving strangely. It took a week or two before they realised something really was wrong and took him to the doctor. To cut a long story short, he had Diabetes Insipidus, and despite it starting pretty much at the same time as his vaccinations the doctors were adamant that the two were unrelated.

> Why are the doctors not pushing C?

Things are changing, slowly … Aspirin is now recognised as a “must do” first response to a heart attack. I know other people who do what you do with vitamin C.

But it really doesn't help the cause of authority when they dismiss the vulgate's concerns, especially when those doing the dismissing probably are far less knowledgeable than those people who are concerned! “We know best” - except they rarely do.


Re: Misinformation (Maziuk, RISKS-31.88)

Amos Shapir <amos083@gmail.com>
Wed, 27 May 2020 18:26:16 +0300

With all due respect to Mr. Maziuk, Dr. Ladkin's point is about taking data out of context, then misrepresenting it, e.g., using a single number of deaths out of a model's worst case scenario, and presenting it as if that was a prediction of what would actually happen.

The “elephant in the room” is that such misinformation is done for the explicit purpose to denigrate scientists, insinuating that “these so-called experts don't know what they're talking about!”

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