COVID-19 media failures

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COVID-19 media failures

This is a thread I wish I had started in the spring. I want to examine how the media has covered and shaped public perception of covid 19. What kinds of things did they highlight? What did they omit? I'm going to start by taking on many of the common ideas, and present information that I think would have changed public perception and reaction. Here is my list:

1) The claim was we needed to act drastically because covid can overwhelm hospitals, but the flu does not. That is patently false.

Time Magazine, January 18, 2018:

“We are pretty much at capacity, and the volume is certainly different from previous flu seasons,” says Dr. Alfred Tallia, professor and chair of family medicine at the Robert Wood Johnson Medical Center in New Brunswick, New Jersey. “I’ve been in practice for 30 years, and it’s been a good 15 or 20 years since I’ve seen a flu-related illness scenario like we’ve had this year.”

Tallia says his hospital is “managing, but just barely,” at keeping up with the increased number of sick patients in the last three weeks. The hospital’s urgent-care centers have also been inundated, and its outpatient clinics have no appointments available.

The story is similar in Alabama, which declared a state of emergency last week in response to the flu epidemic. Dr. Bernard Camins, associate professor of infectious diseases at the University of Alabama at Birmingham, says that UAB Hospital cancelled elective surgeries scheduled for Thursday and Friday of last week to make more beds available to flu patients.


In California, which has been particularly hard hit by this season’s flu, several hospitals have set up large “surge tents” outside their emergency departments to accommodate and treat flu patients. Even then, the LA Times reported this week, emergency departments had standing-room only, and some patients had to be treated in hallways.

The Lehigh Valley Health System in Allentown, Pennsylvania, set up a similar surge tent in its parking lot on Monday, in response to an increase in patients presenting with various viral illnesses, including norovirus, respiratory syncytial virus (RSV) and the flu. “We’ve put it into operation a couples times now over the last few days,” said a hospital spokesperson. “I think Tuesday we saw upwards of about 40 people in the tent itself.”

Health Care Finance News, January 5, 2018:


Three hospitals in Rhode Island went on diversion status Wednesday after both cold weather accidents and flu sufferers overwhelmed their emergency departments. Rhode Island Hospital, Miriam Hospital and Roger Williams Medical Center were on diversion status at the same time Wednesday morning, according to the Providence Journal. State law, however, prohibits more than two hospitals from doing so at a time. Subsequently, all three were forced open.

The state health department declared the flu as being "widespread," which triggers a requirement that unvaccinated health workers must wear surgical masks.

Illinois has given the same designation to flu activity, and hospitals there have implemented special visitor restrictions as a result at HSHS St. Joseph's Hospital in Highland, HSHS Holy Family Hospital in Greenville and Anderson Hospital in Maryville, according to the Belleville News-Democrat.

At Anderson Hospital, visitors displaying any flu-like symptoms are prohibited, and patients younger than 18 are not allowed on patient floors. Also, visitors to the hospital's Pavilion for Women are restricted to the baby's siblings and four other guests designated by the mother, the report said, as the hospital cannot risk subjecting those vulnerable patients to illness.

Los Angeles Times, January 16, 2018:


Hospitals across the state are sending away ambulances, flying in nurses from out of state and not letting children visit their loved ones for fear they’ll spread the flu. Others are canceling surgeries and erecting tents in their parking lots so they can triage the hordes of flu patients.

“Those are all creative things we wouldn’t typically do, but in a crisis like this, we’re looking at,” said Michelle Gunnett, a nurse who oversees emergency services for a Southern California hospital system.


When Candysse Miller took her 88-year-old father, who lives in Redlands, to a nearby emergency room on Jan. 6, it was standing-room only. Many people crammed in the small space were sneezing and violently coughing, she said.

“It was like a flu war zone,” said Miller, a writer. “I’m not a germophobe or anything, but that will quickly make you one.”

War zone? Where have we heard that description before?

CNN, January 13, 2018:

The flu virus has reached nearly every corner of the nation.

Influenza activity is widespread in all states except Hawaii (and the District of Columbia), according to the weekly flu report released Friday by the US Centers for Disease Control and Prevention.

"Flu is everywhere in the US right now," said Dr. Dan Jernigan, director of the CDC's influenza branch. "This is the first year we've had the entire continental US at the same level (of flu activity) at the same time." It has been an early flu season that seems to be peaking now, he said, with a 5.8% increase in laboratory-confirmed cases this week over last.

There were 11,718 new laboratory-confirmed cases during the week ending January 6, bringing the season total to 60,161. These numbers do not include all people who have had the flu, as many do not see a doctor when sick.

Seven additional pediatric deaths were reported during the week ending January 6, bringing the total for the season to 20.


2) What about Italy?

Most polluted towns and cities in Italy, January 22, 2019:


Smog and pollution are choking Italian cities year-round and many towns are exceeding their limits on fine particles and other pollution, according to a new report from Italian environmental authority Legambiente.

Its annual report, published this week, warned that 2018's figures were a “red alert" for Italy.

At least one of two daily pollution limits, on fine particles and ozone emissions, was exceeded in 55 of Italy’s regional capitals.

Unsurprisingly many of Italy’s big cities exceeded the limits. Venice was ranked fourth, closely followed by Milan, Turin and Padua. Rome was 28th and Naples 29th on the list.

Almost all of the worst affected cities are in northern Italy. Frosinone, south of Rome and an emerging centre of industry, falls outside Italy's traditional “industrial triangle” in the north-west, as does Macerata in Campania and the Sicilian town of Enna.

Air pollution exposure, May 2016:

The Lombardy region in northern Italy ranks among the most air polluted areas of Europe. Previous studies showed air pollution short-term effects on all-cause mortality. We examine here the effects of particulate matter with aerodynamic diameter ≤10 µm (PM10) and nitrogen dioxide (NO2) exposure on deaths and hospitalizations from specific causes, including cardiac, cerebrovascular and respiratory diseases.

In researching this particular post, I also found many articles linking high pollution to higer death rates. I have not posted these articles because I want to show that this issue was being discussed before this year.

ICU capacity per capita, March 12, 2020

Note in this graphic that Italy is at 12.5, well below Germany and the United States. This is important, because it was mainly fear of what happened in Italy with hospitals being overwhelmed that scared the rest of the industrialized world into acting the way they did. As usual, the media neglected to add any additional context to explain why Italy in particular was badly hit. I don't think any country in the industrialized world saw their health care systems knocked over the way it happened in Italy. It certainly has not happened in Sweden, a country that everyone likes to claim has had a disastrous response. Context matters. Italy was never a cautionary tale about what would happen unless drastic action was taken. There were specific things that went wrong in Italy that guaranteed a bad outcome.


3) The second wave of the Spanish Flu was more deadly than the first, and there will be several waves of coronavirus.

I've already discussed at length why comparisons between the 1918 influenza pandemic and the 2020 covid pandemic are not necessarliy valid. Quick recap: natural selection generally favours lower lethality, as a virus that kills people cannot spread. Cramming seriously ill soldiers in trains and field hospitals during WWI flipped that around, and gave the deadlier strains that advantage. Once the war was over and everyone went home, it followed the usual lethality we would expect.

By the way, the Spanish Flu pandemic killed 10s of millions of people. Today, with  more people in the world, the death count from covid has yet to reach 1 million worldwide. Hyperbolic much?


4) How do we know how many people will be infected? We have models to predict that. Apparently the model that many of us relied upon was done by Neil Ferguson of the Imperial College in London. Take a look:


When Neil Ferguson visited the heart of British government in London’s Downing Street, he was much closer to the COVID-19 pandemic than he realized. Ferguson, a mathematical epidemiologist at Imperial College London, briefed officials in mid-March on the latest results of his team’s computer models, which simulated the rapid spread of the coronavirus SARS-CoV-2 through the UK population. Less than 36 hours later, he announced on Twitter that he had a fever and a cough. A positive test followed. The disease-tracking scientist had become a data point in his own project.

Ferguson is one of the highest-profile faces in the effort to use mathematical models that predict the spread of the virus — and that show how government actions could alter the course of the outbreak. “It’s been an immensely intensive and exhausting few months,” says Ferguson, who kept working throughout his relatively mild symptoms of COVID-19. “I haven’t really had a day off since mid-January.”

Research does not get much more policy-relevant than this. When updated data in the Imperial team’s model1 indicated that the United Kingdom’s health service would soon be overwhelmed with severe cases of COVID-19, and might face more than 500,000 deaths if the government took no action, Prime Minister Boris Johnson almost immediately announced stringent new restrictions on people’s movements. The same model suggested that, with no action, the United States might face 2.2 million deaths; it was shared with the White House and new guidance on social distancing quickly followed (see ‘Simulation shock’).

Over 2 million deaths in the United States unless drastic action was taken? Wow, that is a big problem. Surely action is necessary. I mean, models always take into account every variable:


One of the paradoxes of the coronavirus crisis is that the need for public scrutiny of government policy has never been greater, but there’s less tolerance for dissent than usual. That’s particularly true of the work of Professor Neil Ferguson and his team at Imperial College. Anyone questioning Professor Ferguson’s analysis is likely to be met with howls of disdain. Witness the furious reaction provoked by Professor Sunetra Gupta and her team at Oxford when they published a paper suggesting that the Imperial model might have underestimated the percentage of the population that has already been infected. The Financial Times printed a critical letter co-signed by a group of scientists that was reminiscent of left-wing academics denouncing one of their colleagues for dissenting from woke orthodoxy. They used the word “dangerous” in their description of the Oxford research, as if merely challenging Imperial’s model would cost lives, and Professor Ferguson has made the same argument to condemn other critics of his work. “It is ludicrous, frankly, to suggest that the severity of this virus is comparable to seasonal flu – ludicrous and dangerous,” he said.

A more prudent approach would be for the Government not to place too much confidence in any one model, or set of models, but to encourage different teams of experts, working independently, to come up with predictions of their own and challenge their rivals. That’s the tried-and-tested scientific method and it has been bizarre to see respected pundits simultaneously argue that we should be strictly guided by “the science” and that any scientist expressing dissent from the prevailing orthodoxy is behaving “irresponsibly”. That was the same argument used by the Chinese authorities for silencing the doctors who first raised the alarm in Wuhan. They were arrested and forced to confess to “spreading rumours” that “severely disturbed the social order.” Shutting down dissent during an actual war might make sense, but in a war against a virus it is vital that we should stick to the scientific method. As Sir Karl Popper said: “The point is that whenever we propose a solution to a problem, we ought to try as hard as we can to overthrow our solution, rather than defend it.”

Maybe a second look is necessary before shutting down everything based on someone who has a track record of incorrect predictions:

In August 2000, Ferguson’s team had predicted that there could be up to 136,000 cases of this disease in the UK (and disturbingly, this article mentions that Ferguson and his team had previously predicted 500,000 cases).

A rival team at London’s School of Hygiene and Tropical Medicine developed their own model which predicted there would be up to 10,000 cases, with a “few thousand” being the best case scenario. Ferguson pooh-poohed the work of this rival team, saying it was “unjustifiably optimistic”.

I should note that Ferguson had made some lower predictions as well – in fact he made a wide range of predictions based on whether various factors, such as incubation periods, applied. But the fact that he laid into the rival team in this way tells us that he thought we were looking at the high end of the range.

Seeing as pretty much everyone who gets vCJD dies from it, this was serious.

So how many people died from vCJD in the UK in the two decades since? 178.

My point here isn’t just that Ferguson’s model was stupendously wrong (or, if you want to emphasise the very large range of predictions he made, useless for most practical purposes). The point is that even the team that performed better still greatly overestimated the number of deaths. Their model only looked good compared to Ferguson’s – his model wasn’t even in the right universe – but it was itself highly inaccurate and misleading, and not at all up to the job we required to be done.


Bridge-building models also have other advantages over epidemiological models. The principles of physics and chemistry that are involved are very well established, and have been worked on for a very long time by very many, and many great, scientists. Also, the basic principles of physics and chemistry they deal with don’t change. Some things in the field do change, of course – for example, new materials are constantly developed, and one must take account of construction techniques varying from place to place, and that mixtures of materials are not always quite right, and so on. But there is a great advantage in the fact that, for example, the laws of gravity don’t change.

Epidemiology, on the other hand, is dealing with things that are, in general, far less locked down, and which can change from decade to decade. Diseases have more-or-less different structures from one another. They don’t all behave alike. Countries vary from one another in various relevant respects (temperature, sanitary conditions, crowd behaviour, and so on). Medicine improves, but it’s not always well known how a modern medicine interacts with a certain disease. There is little that is fixed in a physics-like way with disease, and even for those things that are somewhat fixed our knowledge of some of the important detail is lacking.

The basics behind bridge-building models are not completely set in stone, of course, but they are much more settled than epidemiological models, which are trying to model far messier situations, with many more unknown parameters and influences.


Continuing with the theme of the last post, there was also a claim that the US could see 96 million cases:


Documents from a leading doctor show US hospitals should be preparing for 96 million coronavirus infections and nearly half a million deaths from the outbreak.

The spread of the deadly disease could be far worse than officials claim, with 480,000 Americans expected to die from the virus and 4.8 million hospitalized, according to a presentation hosted by The American Hospital Association (AHA) in February.

This puts the crisis on a level more than 10 times greater than that seen in a severe flu season.

The shock figures fly in the face of claims made by President Trump who has maintained on many occasions that the risk to Americans is 'low'.

Note that the article doesn't give a timeline over which these deaths can be expected to take place. Are we talking 3 months? 6 months? 1 year? 2 years? As of today, 6 months into the pandemic, the total number of covid cases world-wide is nowhere even close to half of that. It's ture that the US badly mishandled the pandemic, and there are probably more covid cases than the official stats picked up. But are we really expecting that the cases in the US are suddenly going to go up to reach that number? Well, it's because we locked down that we haven't seen these high cases. Lockdowns were not evenly implimented in the United States. There were varying degrees of lockdowns, some quasi-lockdowns, and some states even locked down without locking down. So there is enough regional varition in the US with the state responses that we should have an idea of how lockdowns impacted spread. Even with the lack of lockdowns, we are still nowhere near this 96 million infected Americans doomsday scenario. Furthermore, the current trajectory of new cases throughout most of the US is down, and has been for weeks.


5) Iran, the forgotten country

Iran was one of the first global hotspots for the pandemic, and they have never really cooled off. With the exception of the spring, Iran has been logging thousands of new cases per day, as per Worldometers. Are sanctions playing a role?


Iran is another country that was hit particularly hard by the pandemic. According to official figures, there are more than 100,000 confirmed cases of COVID-19 in Iran and some 6,500 people have succumbed to the disease. Experts and officials both in Iran and abroad, however, have cast doubts over these figures, saying the real numbers of cases and deaths are likely even higher than reported. 

The Trump administration, seemingly in denial about its own shambolic response to the crisis, tried to blame the pandemic's high toll in Iran on the incompetence and corruption of the Iranian government. The Iranian leadership indeed made some mistakes in its handling of this public health emergency, such as initially underestimating the threat posed by the virus and failing to close the country's borders in time. It is, however, impossible to claim that the Iranian government is solely responsible for the devastation the pandemic has caused in the country.

Ever since Trump unilaterally withdrew the US from the Joint Comprehensive Plan of Action and embarked on a maximum pressure strategy against Iran some two years ago, the Islamic Republic has been under strict economic sanctions that limit its ability to trade with other nations. These sanctions, which choked of Iran's oil exports, crippled its economy and practically pushed it out of the international banking system, made it impossible for the country to swiftly take the necessary medical, economic and social measures to protect its citizens from the coronavirus.


In Iran, the government admitted that the sanctions make it difficult to obtain vital medical supplies and equipment to treat COVID-19 patients and called for their immediate lifting. Iran's Foreign Minister Javad Zarif, for example, tweeted that the US has moved from "economic terrorism" to "medical terror" by declining to lift the sanctions after the beginning of the outbreak in Iran in mid-February, and urged the international community to stop aiding "war crimes" by obeying "illegal and immoral" sanctions.

This from The Lancet:

Even before COVID-19, Iran's health system was feeling the effect of the sanctions.


Their impact is now severe because they restrict the government's ability to raise funds or to import essential goods. Of the ten countries with the highest number of recorded cases of COVID-19 to date, Iran is the poorest.


In 2019, Iran had the lowest rate of economic growth (–9·5%) and highest rate of inflation (35·7%) recorded in the country for the past 20 years. This financial situation makes the funding of adequate prevention, diagnosis, and treatment of COVID-19 impossible, and the country cannot take the same measures adopted in other countries to strengthen responses, such as paying the full cost of obtaining treatment.


Essential medicines and medical equipment are technically exempt from sanctions, but their availability is restricted by the effect of sanctions on the commercial sector, reducing manufacturing and trade capacity, and on foreign exchange. Consequently, although approximately 184 000 hospital and primary health-care staff are working to fight COVID-19, their efforts are thwarted by shortages of test kits, protective equipment, and ventilators. WHO has provided crucial supplies, sufficient equipment for 31 000 workers, but supplies are still substantially short of what is needed.

I think now is the perfect time to push for the lifting of sanctions on Iran.



There is a claim circulating on social media that 80% mask wearing would cut covid infections by 92%. I think that claim deserves more scrutiny. Here's one example:


De Kai’s solution, along with his team, was to build a computer forecasting model they call the masksim simulator. This allowed them to create scenarios of populations like those in Japan (that generally wear masks) and others (that generally don’t), and to compare what happens to infection rates over time. Masksim takes sophisticated programming used by epidemiologists to track outbreaks and pathogens like COVID-19, Ebola, and SARS, and blended this with other models that are used in artificial intelligence to take into account the role of chance, in this case the randomness and unpredictability, of human behavior—for instance, when a person who is infected decides to go to a beach. De Kai’s team have also added some original programming that takes into account mask-specific criteria, such as how effective certain masks are at blocking the invisible micro-droplets of moisture that spray out of our mouths when we exhale or speak, or our noses when we sneeze, which scientists believe are significant vectors for spreading the coronavirus.

Along with the masksim site, the team is also releasing a study that describes their model in detail as well as their contention that masksim’s forecasts support a growing body of pro-mask evidence. “What’s most important about wearing masks right now,” said Guy-Philippe Goldstein, an economist, cybersecurity expert, and lecturer at the Ecole de Guerre Economique in Paris—and a masksim collaborator, “is that it works, along with social distancing, to flatten the curve of infections as we wait for treatments and vaccines to be developed—while also allowing people to go out and some businesses to reopen.”

While all models have limitations and are only as good as their assumptions, this one is “a very thorough model and well done,” said William Schaffner, an infectious disease specialist at Vanderbilt University, who reviewed the De Kai team’s paper. “It supports a notion that I advocate along with most other infectious disease experts: that masks are very, very important.” Jeremy Howard, founding researcher at and a distinguished research scientist at the University of San Francisco, also assessed the paper. “It’s almost overkill how careful they were with this modeling,” said Howard, who also coauthored and spearheaded a study last month (recently submitted to the journal PNAS) that reviewed dozens of papers assessing the effectiveness of masks.

This particular study was published in April of this year:

We present two models for the COVID-19 pandemicpredicting the impact of universal face mask wearingupon the spread of the SARS-CoV-2 virusone employ-ing a stochastic dynamic network based compartmen-tal SEIR (susceptible-exposed-infectious-recovered) ap-proach, and the other employing individual ABM (agent-based modelling) Monte Carlo simulationindicating (1)significant impact under (near) universal masking when atleast 80% of a population is wearing masks, versus min-imal impact when only 50% or less of the population iswearing masks, and (2) significant impact when universalmasking is adopted early, by Day 50 of a regional out-break, versus minimal impact when universal masking isadopted late. These effects hold even at the lower filteringrates of homemade masks. To validate these theoreticalmodels, we compare their predictions against a new empirical data set we have collected that includes whetherregions have universal masking cultures or policies, theirdaily case growth rates, and their percentage reductionfrom peak daily case growth rates. Results show a nearperfect correlation between early universal masking andsuccessful suppression of daily case growth rates and/orreduction from peak daily case growth rates, as predictedby our theoretical simulations.Taken in tandem, our theoretical models and empirical results argue for urgent implementation of universalmasking in regions that have not yet adopted it as policy or as a broad cultural norm. As governments plan how to exitsocietal lockdowns, universal masking is emerging as oneof the key NPIs (non-pharmaceutical interventions) forcontaining or slowing the spread of the pandemic. Com-bined with other NPIs including social distancing andmass contact tracing, a “mouth-and-nose lockdown” is farmore sustainable than a “full body lockdown”, from eco-nomic, social, and mental health standpoints. To provideboth policy makers and the public with a more concretefeel for how masks impact the dynamics of virus spread,we are making an interactive visualization of the ABMsimulation available online at We recommend immediate mask wearing recommendations, official guidelines for correct use, and awareness cam-paigns to shift masking mindsets away from pure self-protection, towards aspirational goals of responsibly pro-tecting one’s community.


I hate to drive ratings and views for this video, but new modelling has predicted 17 500 possible corona deaths by the end of the year. Oh my God, this is bad! It is disruptive! We need to do something!

Take a look at the death count for Canada. As of September 23 (the date of this post) we have experienced 9,243 deaths (per Wikipedia). That is over a period of a little more than 6 months, in a wave that hit the elderly population first. This current wave is mainly happening in younger people, who are far less likely to die from coronavirus. Furthermore, we have just over 3 months to the end of the year, half the time over which these deaths have happened. That means that in half the time we would have to experience almost the same number of deaths we already have to this point. It also assumes that the trajectory of cases continues to go up, which it may not.

People in this country are generally bad at math. Most cannot do long multiplication or division without a calculator, let alone being equipped to understand the parameters that go into mathematical modelling or their limitations. You have the odd one that is accurate, for example the one that predicted BC would see 100 new cases a day by the start of September, but for the most part the models are just an attempt to make people more afraid of the virus. If the comment section on that video is any indication, people are starting to catch on.

Left Turn Left Turn's picture

Three comments at this point:

1) It wan't just the media that treated COVID as a major threat. The WHO also pleaded with governments to take COVID seriously, to prevent hospitals being overwhelmed. That's why "flattening the curve" became such a mantra in the early weeks of the pandemic.

2) I'll agree that the comparisons to the 1918 influenza were over the top. Not because there was no comparison to be made, but because we couldn't be sure if COVID would follow the same path as the 1918 flu. As you've pointed out, the first several months of the 1918 flu pandemic coincided with the final several months of WWI, which affected the 1918 pandemic in ways that would not be replicated with COVID. Plus, our response as a society would affect the course of COVID, and we wern't necessarily going to respond as they did in 1918.

3) Most of the projection models were based on "worst case scenarios" where regular levels of social interaction would be resumed after a short period, and mitigation measure completely abandoned. I didn't support the pushing of these models, and was quite happy that BC chose not to push such a model. To the extent that outcomes have been better than what these models predicted, it's largely due to the levels of reduced social activity, physical distancing, and other mitigation measures that have been followed. As case numbers rise, the media is very focused on those who are not mitigating their risk of getting COVID in any way, and yet those who are mitigating their risk of COVID are keeping case numbers lower than they otherwise would be.


Left Turn wrote:
The WHO also pleaded with governments to take COVID seriously, to prevent hospitals being overwhelmed. That's why "flattening the curve" became such a mantra in the early weeks of the pandemic.

For one, the opening post in this thread talks about hospitals having been overwhelmed by the flu a couple of years ago. We've just forgotten about that. As for the WHO declaring covid to be a pandemic, you have to remember that they have a global picture of things, and would have been concerned about the virus making it into countries that struggled with health care infrastructure. It was never going to be as big a threat to the industrialized world as the poorer countries. Plus, the WHO issues all kinds of statements of concern that we don't notice because they don't affect us. To give an example, the next alert level below a pandemic is a public health emergency of international concern. The WHO made that declaration about the coronavirus on January 30, and it was the 6th time they had ever done so. Without having to look it up, can you tell me when were the other five?

Left Turn wrote:
Most of the projection models were based on "worst case scenarios" where regular levels of social interaction would be resumed after a short period, and mitigation measure completely abandoned. I didn't support the pushing of these models, and was quite happy that BC chose not to push such a model. To the extent that outcomes have been better than what these models predicted, it's largely due to the levels of reduced social activity, physical distancing, and other mitigation measures that have been followed. As case numbers rise, the media is very focused on those who are not mitigating their risk of getting COVID in any way, and yet those who are mitigating their risk of COVID are keeping case numbers lower than they otherwise would be.

If that's true, you know much more about how these models came to be than most people. Even still there was reason to question them. You mentioned no mitigation measures being taken? There are parts of the US where that was exactly what happened, and still the country came nowhere close to the 96 million that I cited above. Can we really know for sure that mitigation measures actually prevented that many cases, or should we consider that to be an exaggerated claim that never should have been taken seriously?