vox--2020-05-01--Coronavirus’s new mystery: It’s causing strokes in healthy people

As more people around the world are infected with Covid-19 , we ’ re learning that the novel coronavirus can not only cause severe respiratory illness , but also can attack just about every major organ system in the body .
And lately doctors have been sounding the alarm about a disturbing new outcome : blood clots and strokes , which are striking even healthy young people with no known risk factors — and sometimes no other symptom of the virus .
An April 28 report from the New England Journal of Medicine details the cases of five people , @ @ @ @ @ @ @ York City who had strokes and subsequently tested positive for Covid-19 .
All of them had large-vessel strokes outside of the hospital before experiencing other severe symptoms of the virus ; one of them has since died .
“ It was very surprising to see the increase in this large-vessel stroke in young people , ” Thomas Oxley , a neurosurgeon at Mount Sinai in New York and a coauthor of the new report , tells Vox .
As he explains , “ The bigger the vessel , the bigger the stroke.
” @ @ @ @ @ @ @ emerging ” about Covid-19 , he adds .
The rate of large-vessel stroke victims under 50 they saw was seven times higher than before the pandemic .
Blood clots are also causing other unexpected problems for Covid-19 patients .
For example , Broadway actor Nick Cordero , who has been hospitalized since March with severe Covid-19 , had his right leg amputated earlier this month after doctors were unable to control clotting there .
And many patients are developing small-clots in their lungs , reducing the amount of oxygen they can move into @ @ @ @ @ @ @ blood is clogging dialysis machines ( which has been a problem due to the amount of kidney failure this illness is also causing ) .
“ I ’ m a hematologist , and this is unprecedented , ” says Jeffrey Laurence of Weill Cornell Medical College , who has been in the field for three decades .
“ This is not like a disease we ’ ve seen before.
” Nearly every patient he has seen for blood disorders in the past month and a half has had Covid-19 .
“ I ’ @ @ @ @ @ @ @ my life .
These people are clotting , and we can ’ t shut it off.
” Doctors and researchers are racing to figure out why this is happening , and how they might be able to best use existing therapies , such as blood thinners , to mitigate it in patients .
The clots and strokes also add to the list of potential symptoms some people with Covid-19 might experience early on — and gives another possible reason that the number of coronavirus-related deaths around the country is looking far larger than @ @ @ @ @ @ @ What causes blood to clot ?
When we get injured , we depend on our blood ’ s ability to clot to stop the flow of blood .
Clotting is a complex process that involves small cell fragments called platelets congregating and changing shape , proteins that help even more cells bind together , and the secretion of substances called blood clotting factors .
If any of these processes go off course , people can experience excessive bleeding , which can be life-threatening .
“ I ’ ve never had so many consults @ @ @ @ @ @ @ clotting , and we can ’ t shut it off ” On the other end of the spectrum , sometimes clots form inside blood vessels ( or , more rarely , arteries ) without an injury .
These can cause serious harm and sometimes death .
There are many risk factors for developing internal blood clots , including smoking , obesity , heart disease , and others .
And , now , it looks like Covid-19 is a risk factor as well .
Some clots remain in the place where they form and @ @ @ @ @ @ @ cause severe pain and swelling .
These clots can also travel to — or form in — a major organ , where they can do even more serious damage .
For example , a clot in the leg can travel up to the lungs , cutting off blood flow and causing a pulmonary embolism ( which can lead to death or permanent lung damage ) .
A clot can also flow to the heart , triggering a heart attack .
And one in or near the head can block blood flow in part @ @ @ @ @ @ @ stroke .
What clots are doing to Covid-19 patients Small early studies and case reports about the link between the novel coronavirus and blood clots are now pouring in .
For example , one team in the Netherlands followed 184 severe Covid-19 patients who were receiving treatment in three different intensive care units .
They found that 31 percent of these people had some sort of blood clotting issue , a percentage they call “ remarkably high.
” Other data is emerging with similar implications .
“ In patients with severe disease , @ @ @ @ @ @ @ to occur in 15-35 percent of patients , ” Behnood Bikdeli , a cardiology fellow at Columbia University Medical Center , tells Vox .
And these clots , especially the small ones , “ could impact the illness severity and involvement of many of the organs , ” he says .
( He and an international team of dozens of researchers published an April review of clotting issues in the Journal of the American College of Cardiology . )
Laurence has been studying small blood clots in HIV/AIDS patients for decades .
In @ @ @ @ @ @ @ photo of surprising skin lesions on a young man who was severely ill with Covid-19 .
Laurence was stunned .
“ It ’ s a picture of microvascular clotting , where you can see exactly where the vessels have clotted , ” he says .
He started wondering if something similar might be happening in the lungs .
Just hours later , another doctor called , giving him access to an autopsy of a different Covid-19 patient .
Not only did this individual have small clots in the skin — but also in @ @ @ @ @ @ @ colleagues published descriptions of these and three other cases of severe clotting in an April Translational Research paper . )
The presence of small clots in the lungs is disturbing , but it also might help to explain a puzzling trend medical staff have noticed in some Covid-19 patients .
When people develop more advanced illness , their lungs can become stiff , making breathing on their own very difficult .
This leads to a drop in oxygen in the blood if they are not on mechanical ventilation .
But health care workers @ @ @ @ @ @ @ levels but who still have fairly flexible lungs , Laurence explains .
This points to the presence of “ microvessel clots [ in the lungs ] shutting off the ability of people to bring oxygen into their blood , ” he says .
( He also noted that sustained time on a ventilator can , itself , increase lung stiffness , which could have been throwing off clinicians who were seeing that as an outcome of the illness , and perhaps along the way missing signs that something else was going on .
@ @ @ @ @ @ @ people sick with Covid-19 whose blood clots are plugging up the dialysis machines in their wards .
Beyond that , he says , even “ as the nurses are drawing their blood , it ’ s clotting in the tubes , and they ’ re on full doses of Heparin ” and other blood thinning medications .
“ Everyone is seeing a similar kind of thing , ” he says .
These observations also bear out in autopsies .
A team of Chinese researchers that looked at 183 people hospitalized with Covid-19 pneumonia @ @ @ @ @ @ @ of clotting in 71 percent of those who died — but less than 1 percent of those who didn ’ t .
( Notably , this paper was published early in the pandemic , in February . )
The prevalence of blood clots also raises the question of whether some stroke or heart attack home deaths in recent months were actually linked to Covid-19 .
It ’ s yet another possible way Covid-19 fatalities might be being undercounted .
Many people who have already died are not currently tested for Covid-19 ( often @ @ @ @ @ @ @ those who are still alive ) .
But more widespread posthumous testing could help clarify the full impact of this disease .
How Covid-19 might be causing blood clots Scientists still don ’ t understand exactly what is triggering this excessive blood clotting .
( Some viruses , such as the Ebola virus , cause extreme bleeding , but others , such as HIV , can trigger small clots . )
And it ’ s not yet clear if these changes in the blood are from the virus itself or the body ’ @ @ @ @ @ @ @ One of the hypotheses has to do with how the virus gains entry to our cells .
Researchers have found that this coronavirus manages to sneak into our cells via a specific type of receptor known as ACE2 .
These are prominently found in the lungs , which might explain why so much of the virus ’ s damage has been centered there .
But ACE2 receptors are also very common along the walls of blood vessels throughout the body , Oxley explains .
So it ’ s possible that its presence there @ @ @ @ @ @ @ , prompting the formation of blood clots .
Laurence also points to this inflammatory problem .
“ It is this insidious feedback loop of inflammation , ” he says .
And once it ’ s going , he says , “ you can ’ t intervene in that system effectively.
” To be sure , sustained immobility , such as in a hospital bed , can increase the risk for blood clots , but the rates currently being reported in Covid-19 patients is way above what would normally be expected , Laurence notes @ @ @ @ @ @ @ new evidence about this virus ’ s potential effect on the blood , doctors at many major medical facilities have begun administering low doses of preventative blood thinners to Covid-19 patients .
It ’ s a tricky move , though , because too much blood thinner can cause a patient to bleed internally and possibly die .
To gauge the best doses , many physicians are going off of a patient ’ s D dimer levels , which is a biomarker for the presence of blood clots .
New clinical trials have quickly @ @ @ @ @ @ @ in the US to test one type of powerful blood thinners , known as tPA ( tissue plasminogen activator ) in Covid-19 patients , as STAT News reports ) .
And Bikdeli and others have formed an international collective “ to provide interim consensus-based guidance , ” he says .
“ What is needed most is high-quality data.
” But these preventative treatments won ’ t help those who ’ ve had strokes or other major blood-clot complications before receiving medical care .
And that number , though still small , is real @ @ @ @ @ @ @ from New York City show .
“ It is this insidious feedback loop of inflammation ... you can ’ t intervene in that system effectively ” The New England Journal of Medicine report included all stroke patients under 50 at the Mount Sinai health system in New York City during a two-week period in late March and early April .
That this rate was nearly seven times the number of stroke patients in that age group during any average two-week period over the previous year indicates a very strong correlation between Covid-19 — @ @ @ @ @ @ @ the potential for major blood-clotting .
“ These patients were at home with mild or no symptoms who suddenly developed signs of stroke , ” Oxley says .
Some of these young patients had also been reluctant to seek medical attention — even after fairly severe stroke symptoms .
And it wasn ’ t unreasonable for them not to be on the lookout for such a major neurological issue .
“ These are people among the least likely statistically to have a stroke , ” J Mocco , a Mount Sinai neurosurgeon and @ @ @ @ @ @ @ Washington Post .
For example , the youngest , a 33-year-old woman , developed slurred speech and felt weak and numb on her left side for 28 hours before seeking help .
She ( as well as one other stroke patient in the study ) was “ concerned about going to a hospital during the pandemic , ” the authors noted — even though she had had a cough , headache and chills for a week .
She is one of the lucky ones .
She is the only one who has been @ @ @ @ @ @ @ 10 days in the hospital to treat her blood clotting , she was discharged to a rehabilitation center .
There is also a question of how long the danger of Covid-19-related clotting lasts .
Laurence notes that “ a lot of our cases , they went through their cough and fever , then all of a sudden , there ’ s a stroke or a blood clot in their legs.
” For other patients , after having recovered from their primary Covid-19 symptoms , “ their difficulty of breathing came back , signaling @ @ @ @ @ @ @ their lungs , ” he says .
“ So people have to be vigilant to look for possible signs ” — even after they feel like they have passed the worst of their illness .
Additionally , “ people at high risk for thrombotic events need to be vigilant , keep good hydration , and keep being active , especially during the period of social distancing and lockdown , ” Bikdeli says .
This group includes people with a family history of blood clots or strokes , obesity , some cancer treatments and @ @ @ @ @ @ @ methods , and others .
But major strokes can be treatable , Oxley notes .
It ’ s just a question of timing .
For best results treatment must begin within six hours of stroke .
As he told the Washington Post , “ the message we are trying to get out is if you have symptoms of stroke , you need to call the ambulance .
”

As more people around the world are infected with Covid-19, we’re learning that the novel coronavirus can not only cause severe respiratory illness, but also can attack just about every major organ system in the body. And lately doctors have been sounding the alarm about a disturbing new outcome: blood clots and strokes, which are striking even healthy young people with no known risk factors — and sometimes no other symptom of the virus.
An April 28 report in the New England Journal of Medicine details the cases of five people, ages 33 to 49, in New York City who had strokes and subsequently tested positive for Covid-19. All of them had large-vessel strokes outside of the hospital before experiencing other severe symptoms of the virus; one of them has since died.
“It was very surprising to see the increase in this large-vessel stroke in young people,” Thomas Oxley, a neurosurgeon at Mount Sinai in New York and a coauthor of the new report, tells Vox. As he explains, “The bigger the vessel, the bigger the stroke.”
“It’s the biggest story emerging” about Covid-19, he adds. The rate of large-vessel stroke victims under 50 they saw was seven times higher than before the pandemic.
Blood clots are also causing other unexpected problems for Covid-19 patients. For example, Broadway actor Nick Cordero, who has been hospitalized since March with severe Covid-19, had his right leg amputated earlier this month after doctors were unable to control clotting there.
And many patients are developing small clots in their lungs, reducing the amount of oxygen they can move into their bodies. For others, their blood is clogging dialysis machines (which has been a problem due to the amount of kidney failure this illness is also causing).
“I’m a hematologist, and this is unprecedented,” says Jeffrey Laurence of Weill Cornell Medical College, who has been in the field for three decades. “This is not like a disease we’ve seen before.”
Nearly every patient he has seen for blood disorders in the past month and a half has had Covid-19. “I’ve never had so many consults in my life. These people are clotting, and we can’t shut it off.”
Doctors and researchers are racing to figure out why this is happening, and how they might be able to best use existing therapies, such as blood thinners, to mitigate it in patients. The clots and strokes also add to the list of potential symptoms some people with Covid-19 might experience early on — and gives another possible reason that the number of coronavirus-related deaths around the country is looking far larger than those officially being counted in hospitals.
What causes blood to clot?
When we get injured, we depend on our blood’s ability to clot to stop the flow of blood. Clotting is a complex process that involves small cell fragments called platelets congregating and changing shape, proteins that help even more cells bind together, and the secretion of substances called blood clotting factors. If any of these processes go off course, people can experience excessive bleeding, which can be life-threatening.
“I’ve never had so many consults in my life. These people are clotting, and we can’t shut it off”
On the other end of the spectrum, sometimes clots form inside blood vessels (more rarely, in arteries) without an injury. These can cause serious harm and sometimes death. There are many risk factors for developing internal blood clots, including smoking, obesity, heart disease, and others. And, now, it looks like Covid-19 is a risk factor as well.
Some clots remain in the place where they form and are known as thrombosis. This can cause severe pain and swelling. These clots can also travel to — or form in — a major organ, where they can do even more serious damage.
For example, a clot in the leg can travel up to the lungs, cutting off blood flow and causing a pulmonary embolism (which can lead to death or permanent lung damage). A clot can also flow to the heart, triggering a heart attack. And one in or near the head can block blood flow in part of the brain, bringing on a stroke.
What clots are doing to Covid-19 patients
Small early studies and case reports about the link between the novel coronavirus and blood clots are now pouring in. For example, one team in the Netherlands followed 184 severe Covid-19 patients who were receiving treatment in three different intensive care units. They found that 31 percent of these people had some sort of blood clotting issue, a percentage they call “remarkably high.”
Other data is emerging with similar implications. “In patients with severe disease, various forms of blood clots are estimated to occur in 15-35 percent of patients,” Behnood Bikdeli, a cardiology fellow at Columbia University Medical Center, tells Vox. And these clots, especially the small ones, “could impact the illness severity and involvement of many of the organs,” he says. (He and an international team of dozens of researchers published an April review of clotting issues in the Journal of the American College of Cardiology.)
Laurence has been studying small blood clots in HIV/AIDS patients for decades. In March, a dermatologist sent him a photo of surprising skin lesions on a young man who was severely ill with Covid-19. Laurence was stunned. “It’s a picture of microvascular clotting, where you can see exactly where the vessels have clotted,” he says. He started wondering if something similar might be happening in the lungs.
Just hours later, another doctor called, giving him access to an autopsy of a different Covid-19 patient. Not only did this individual have small clots in the skin — but also in his lungs. (Laurence and his colleagues published descriptions of these and three other cases of severe clotting in an April Translational Research paper.)
The presence of small clots in the lungs is disturbing, but it also might help to explain a puzzling trend medical staff have noticed in some Covid-19 patients. When people develop more advanced illness, their lungs can become stiff, making breathing on their own very difficult. This leads to a drop in blood oxygen if they are not on mechanical ventilation.
But health care workers have seen many patients with low oxygen levels but who still have fairly flexible lungs, Laurence explains. This points to the presence of “microvessel clots [in the lungs] shutting off the ability of people to bring oxygen into their blood,” he says. (He also noted that sustained time on a ventilator can, itself, increase lung stiffness, which could have been throwing off clinicians who were seeing that as an outcome of the illness, and perhaps along the way missing signs that something else was going on.)
Laurence also describes the multitude of people sick with Covid-19 whose blood clots are plugging up the dialysis machines in their wards. Beyond that, he says, even “as the nurses are drawing their blood, it’s clotting in the tubes, and they’re on full doses of Heparin” and other blood-thinning medications. “Everyone is seeing a similar kind of thing,” he says.
These observations also bear out in autopsies.
A team of Chinese researchers that looked at 183 people hospitalized with Covid-19 pneumonia in Wuhan, China, found evidence of clotting in 71 percent of those who died — but less than 1 percent of those who didn’t. (Notably, this paper was published early in the pandemic, in February.)
The prevalence of blood clots also raises the question of whether some stroke or heart attack home deaths in recent months were actually linked to Covid-19. It’s yet another possible way Covid-19 fatalities might be being undercounted. Many people who have already died are not currently tested for Covid-19 (often in the interest of preserving tests for those who are still alive). But more widespread posthumous testing could help clarify the full impact of this disease.
How Covid-19 might be causing blood clots
Scientists still don’t understand exactly what is triggering this excessive blood clotting. (Some viruses, such as the Ebola virus, cause extreme bleeding, but others, such as HIV, can trigger small clots.) And it’s not yet clear if these changes in the blood are from the virus itself or the body’s immune response to the infection.
One of the hypotheses has to do with how the virus gains entry to our cells. Researchers have found that this coronavirus manages to sneak into our cells via a specific type of receptor known as ACE2. These are prominently found in the lungs, which might explain why so much of the virus’s damage has been centered there. But ACE2 receptors are also very common along the walls of blood vessels throughout the body, Oxley explains. So it’s possible that its presence there is spurring additional inflammation of the vessels, prompting the formation of blood clots.
Laurence also points to this inflammatory problem. “It is this insidious feedback loop of inflammation,” he says. And once it’s going, he says, “you can’t intervene in that system effectively.”
To be sure, sustained immobility, such as in a hospital bed, can increase the risk for blood clots, but the rates currently being reported in Covid-19 patients is way above what would normally be expected, Laurence notes.
What can be done
With the new evidence about this virus’s potential effect on the blood, doctors at many major medical facilities have begun administering low doses of preventative blood thinners to Covid-19 patients.
It’s a tricky move, though, because too much blood thinner can cause a patient to bleed internally and possibly die. To gauge the best doses, many physicians are going off of a patient’s D-dimer levels, which is a biomarker for the presence of blood clots. New clinical trials have quickly spun up (including a multi-state one in the US to test one type of powerful blood thinners, known as tPA (tissue plasminogen activator) in Covid-19 patients, as STAT News reports). And Bikdeli and others have formed an international collective “to provide interim consensus-based guidance,” he says. “What is needed most is high-quality data.”
But these preventive treatments won’t help those who’ve had strokes or other major blood-clot complications before receiving medical care. And that number, though still small, is real, as the five young stroke victims from New York City show.
“It is this insidious feedback loop of inflammation ... you can’t intervene in that system effectively”
The New England Journal of Medicine report included all stroke patients under 50 at the Mount Sinai health system in New York City during a two-week period in late March and early April. That this rate was nearly seven times the number of stroke patients in that age group during any average two-week period over the previous year indicates a very strong correlation between Covid-19 — even mild and asymptomatic cases — and the potential for major blood-clotting.
“These patients were at home with mild or no symptoms who suddenly developed signs of stroke,” Oxley says.
Some of these young patients had also been reluctant to seek medical attention — even after fairly severe stroke symptoms. And it wasn’t unreasonable for them not to be on the lookout for such a major neurological issue. “These are people among the least likely statistically to have a stroke,” J Mocco, a Mount Sinai neurosurgeon and coauthor of the report, told the Washington Post.
For example, the youngest, a 33-year-old woman, developed slurred speech and felt weak and numb on her left side for 28 hours before seeking help. She (as well as one other stroke patient in the study) was “concerned about going to a hospital during the pandemic,” the authors noted — even though she had had a cough, headache, and chills for a week.
She is one of the lucky ones. She is the only one who has been able to start speaking again. After 10 days in the hospital to treat her blood clotting, she was discharged to a rehabilitation center.
There is also a question of how long the danger of Covid-19-related clotting lasts. Laurence notes that “a lot of our cases, they went through their cough and fever, then all of a sudden, there’s a stroke or a blood clot in their legs.” For other patients, after having recovered from their primary Covid-19 symptoms, “their difficulty of breathing came back, signaling that they have a pulmonary embolism in their lungs,” he says. “So people have to be vigilant to look for possible signs” — even after they feel like they have passed the worst of their illness.
Additionally, “people at high risk for thrombotic events need to be vigilant, keep good hydration, and keep being active, especially during the period of social distancing and lockdown,” Bikdeli says. This group includes people with a family history of blood clots or strokes, obesity, some cancer treatments and surgeries, pregnancy, some birth control methods, and others.
But major strokes can be treatable, Oxley notes. It’s just a question of timing. For best results treatment must begin within six hours of stroke. As he told the Washington Post, “the message we are trying to get out is if you have symptoms of stroke, you need to call the ambulance.”


vox--2020-05-01--Minutes after pledging to not lie, the new White House press secretary lied a whole bunch

Kayleigh McEnany ’ s first briefing as White House press secretary started off on a hopeful note — with a promise from her not to lie .
That lasted for about all of 15 minutes .
“ I will never lie to you .
You have my word on that , ” McEnany said early during the briefing , in response from a question from the Associated Press ’ s Jill Colvin .
It ’ s a standard question White House reporters ask new press secretaries , and one that is rarely , if ever , lived up to fully @ @ @ @ @ @ @ questions for Trump ’ s fourth press secretary to break that promise .
McEnany ’ s first lie from the White House podium came in response to a question about comments President Donald Trump made earlier in the day characterizing Tara Reade ’ s sexual assault allegation against Joe Biden as “ far more compelling ” than the accusations made against Supreme Court Justice Brett Kavanaugh during his confirmation hearings .
Asked to flesh out Trump ’ s thinking , McEnany characterized the Kavanaugh allegations as “ verifiably false.
” “ I think @ @ @ @ @ @ @ with what happened with Justice Brett Kavanaugh .
There ’ s no need for me to bring up the salacious , awful , and verifiably false allegations that were made against Justice Kavanaugh , ” she said .
But that ’ s a lie .
Instead of being “ verifiably false , ” multiple accusations against Kavanaugh were found to be credible during the course of an investigation conducted by the New York Times that was published last fall , roughly a year after his confirmation to the court .
That wasn ’ @ @ @ @ @ @ @ her first briefing .
Asked about newly released FBI notes regarding the investigation of former National Security Adviser Michael Flynn that Trump and White House officials have been hyping as evidence that Flynn was “ set up , ” McEneny misquoted what the documents say to make them sound more incriminating than they are .
“ We have a handwritten FBI note that says , quote , we need to get Flynn to lie , quote , and get him fired , ” she said .
But the notes don ’ t say @ @ @ @ @ @ @ before a January 2017 interview with Flynn , one investigator wrote down , “ What is our goal ?
Truth/admission or to get him to lie , so we can prosecute him or get him fired ?
” It ’ s important to keep in mind that at the time , the FBI had already gathered evidence that Flynn had broken the law .
The notes do not indicate investigators went into the interview with the goal of manipulating Flynn into lying , as McEnany suggested .
There was more .
Asked later @ @ @ @ @ @ @ misconduct allegations against Trump from more than 20 women less credible than the one against Biden , McEnany said that “ the president has swiftly denied all of these allegations that were raised four years ago.
” While many of the allegations against Trump were made in 2016 , E. Jean Carroll accused Trump of rape just last year .
McEnany then concluded the briefing by reiterating her lies about the Flynn notes .
If her first outing is any indication , McEnany may end up being less hostile with reporters than Sarah @ @ @ @ @ @ @ than Sean Spicer .
She may be more available to the public than the recently departed Stephanie Grisham , who never held a press briefing , making Friday ’ s the first one held by a White House press secretary in more than a year .
But like her predecessors , McEnany will use the White House briefing room to tell lies — even right after she promises reporters she won ’ t .
Every day at Vox , we aim to answer your most important questions and provide you , and our @ @ @ @ @ @ @ that has the power to save lives .
Our mission has never been more vital than it is in this moment : to empower you through understanding .
Vox ’ s work is reaching more people than ever , but our distinctive brand of explanatory journalism takes resources — particularly during a pandemic and an economic downturn .
Your financial contribution will not constitute a donation , but it will enable our staff to continue to offer free articles , videos , and podcasts at the quality and volume that this moment requires @ @ @ @ @ @ @ Vox today .

Kayleigh McEnany’s first briefing as White House press secretary started off on a hopeful note — with a promise from her not to lie. That lasted for about all of 15 minutes.
“I will never lie to you. You have my word on that,” McEnany said early during the briefing, in response from a question from the Associated Press’s Jill Colvin.
"I will never lie to you. You have my word on that." -- Kayleigh McEnany during her first press briefing as press secretary (bookmark this) pic.twitter.com/PqUhRAWiun — Aaron Rupar (@atrupar) May 1, 2020
It’s a standard question White House reporters ask new press secretaries, and one that is rarely, if ever, lived up to fully. But it took all of two questions for Trump’s fourth press secretary to break that promise.
McEnany’s first lie from the White House podium came in response to a question about comments President Donald Trump made earlier in the day characterizing Tara Reade’s sexual assault allegation against Joe Biden as “far more compelling” than the accusations made against Supreme Court Justice Brett Kavanaugh during his confirmation hearings. Asked to flesh out Trump’s thinking, McEnany characterized the Kavanaugh allegations as “verifiably false.”
“I think it was a grave miscarriage of justice with what happened with Justice Brett Kavanaugh. There’s no need for me to bring up the salacious, awful, and verifiably false allegations that were made against Justice Kavanaugh,” she said.
"The salacious, awful, and verifiably false allegations that were made against Justice Kavanaugh" -- McEnany just lied, breaking the promise she made not to just minutes ago. The allegations against Kavanaugh were not verifiably false.
It was a good run. pic.twitter.com/XQp1yer9vt — Aaron Rupar (@atrupar) May 1, 2020
But that’s a lie. Instead of being “verifiably false,” multiple accusations against Kavanaugh were found to be credible during the course of an investigation conducted by the New York Times that was published last fall, roughly a year after his confirmation to the court.
That wasn’t the only lie McEnany told during her first briefing. Asked about newly released FBI notes regarding the investigation of former National Security Adviser Michael Flynn that Trump and White House officials have been hyping as evidence that Flynn was “set up,” McEneny misquoted what the documents say to make them sound more incriminating than they are.
“We have a handwritten FBI note that says, quote, we need to get Flynn to lie, quote, and get him fired,” she said.
MeEnany just lied again. She misquoted the FBI notes that Trump is hyping (misleadingly) as bombshell evidence exonerating Michael Flynn. Compare what she said with what the note actually says: https://t.co/fwS2XHsXjy pic.twitter.com/iuOPZuhLl9 — Aaron Rupar (@atrupar) May 1, 2020
But the notes don’t say that. All they reveal is that before a January 2017 interview with Flynn, one investigator wrote down, “What is our goal? Truth/admission or to get him to lie, so we can prosecute him or get him fired?” It’s important to keep in mind that at the time, the FBI had already gathered evidence that Flynn had broken the law. The notes do not indicate investigators went into the interview with the goal of manipulating Flynn into lying, as McEnany suggested.
There was more. Asked later why the public should find the sexual misconduct allegations against Trump from more than 20 women less credible than the one against Biden, McEnany said that “the president has swiftly denied all of these allegations that were raised four years ago.” While many of the allegations against Trump were made in 2016, E. Jean Carroll accused Trump of rape just last year.
McEnany's rationale for why Trump's denials of sexual assault are more convincing than Biden's is that "the president has swiftly denied all of these allegations that were raised four years ago. He's always told the truth on these issues." pic.twitter.com/dKKcmmEDpE — Aaron Rupar (@atrupar) May 1, 2020
McEnany then concluded the briefing by reiterating her lies about the Flynn notes.
If her first outing is any indication, McEnany may end up being less hostile with reporters than Sarah Sanders. She may be less gaffe-prone than Sean Spicer. She may be more available to the public than the recently departed Stephanie Grisham, who never held a press briefing, making Friday’s the first one held by a White House press secretary in more than a year.
But like her predecessors, McEnany will use the White House briefing room to tell lies — even right after she promises reporters she won’t.


vox--2020-05-02--This coronavirus model keeps being wrong. Why are we still listening to it?

This coronavirus model keeps being wrong .
Why are we still listening to it ?
Share All sharing options for : This coronavirus model keeps being wrong .
Why are we still listening to it ?
How many people are likely to die in the United States of Covid-19 ?
How many hospital beds is the country going to need ?
When will case numbers peak ?
To answer those questions , many hospital planners , media outlets , and government bodies — including the White House — relied heavily on one particular model out of the many that have @ @ @ @ @ @ @ : the University of Washington ’ s Institute for Health Metrics and Evaluation ( IHME ) .
The model first estimated in late March that there ’ d be fewer than 161,000 deaths total in the US ; in early April , it revised its projections to say that the total death toll through August was “ projected to be 60,415 ” ( though it acknowledged the range could be between 31,221 and 126,703 ) .
The model has been cited often by the White House and has informed its policymaking .
But @ @ @ @ @ @ @ : The IHME has consistently forecast many fewer deaths than most other models , largely because the IHME model projects that deaths will decline rapidly after the peak — an assumption that has not been borne out .
On Wednesday , the US death count passed the 60,000 mark that the IHME model had said was the likely total cumulative death toll .
The IHME on April 29 released a new update raising its estimates for total deaths to 72,433 , but that , too , looks likely to be proved an underestimate @ @ @ @ @ @ @ its upper bound on deaths — now listed as 114,228 by August — is questionable , as some other models expect the US will hit that milestone by the end of May , and most project it will in June .
One analysis of the IHME model found that its next-day death predictions for each state were outside its 95 percent confidence interval 70 percent of the time — meaning the actual death numbers fell outside the range it projected 70 percent of the time .
That ’ s not great !
( @ @ @ @ @ @ @ issue ; more on this below . )
This track record has led some experts to criticize the model .
“ It ’ s not a model that most of us in the infectious disease epidemiology field think is well suited ” to making projections about Covid-19 , Harvard epidemiologist Marc Lipsitch told reporters .
But if that ’ s the case , how has it risen to such prominence among policymakers ?
Other models have done better than IHME at predicting the course of the epidemic , and many of them use @ @ @ @ @ @ @ .
Yet it ’ s the IHME model that has generally guided policymakers , for the most part in the direction of focusing on a return to normal .
One potential explanation for its outsize influence : Some of the factors that make the IHME model unreliable at predicting the virus may have gotten people to pay attention to it .
For one thing , it ’ s more simplistic compared to other models .
That means it can be applied in ways more complicated models could not , such as providing state-level @ @ @ @ @ @ @ ) , which other modelers acknowledged that they didn ’ t have enough data to offer .
Meanwhile , its narrow confidence intervals for state-by-state estimates meant it had quotable ( and optimistic ) topline numbers .
A confidence interval represents a range of numbers where the model is very confident the true value will lie within that range .
A narrow range that gives “ an appearance of certainty is seductive when the world is desperate to know what lies ahead , ” a criticism of the IHME model published in the @ @ @ @ @ @ @ the numbers and precise curves the IHME is publishing “ suggests greater precision than the model is able to offer.
” The criticism of the IHME model , and an emerging debate over epidemiology models more broadly , has brought to light important challenges in the fight against the coronavirus .
Good planning requires good projections .
We ’ ll need models to help predict resurgences and spot a potential second wave .
Dissecting what the IHME model got wrong , what other models got right , and how the public and policymakers @ @ @ @ @ @ @ we want to create the best pandemic plans possible .
What ’ s wrong with the IHME model of the coronavirus ?
Models of disease spread are meant to help decision-makers in an environment where math will outperform intuitions .
Most people have a hard time thinking about exponential growth , which is why many were taken by surprise in March as the virus spread .
That ’ s why models can be useful .
Models usually lay out some foundational assumptions and offer projections based on those assumptions .
The IHME model @ @ @ @ @ @ @ rates assuming widespread social distancing and strong measures to prevent the spread of the virus .
Since testing is so unreliable at identifying the infection rate , the model uses only death rates as data ( though it ’ s worth noting that evidence from all-cause mortality data suggests we ’ re undercounting coronavirus-caused deaths , too ) .
That projected hospitalization rate was one of the things that set it apart , IHME researcher Ali Mokdad told me .
It was one of the few models that offered that projection , and @ @ @ @ @ @ @ grab on to and plan around .
Another reason it rose in prominence was IHME ’ s decision to model the effects of strong social distancing measures .
That choice proved correct — strong measures were indeed taken across the United States — and the lower death numbers the model churned out as a result reportedly led to it being received favorably by the Trump administration .
But as the weeks have passed , it has become clear that the IHME ’ s projections have been too optimistic , and slow to adjust @ @ @ @ @ @ @ plateaued rather than rapidly decreasing to zero .
The IHME has been regularly updating its model as new data comes in , but the updates have often been slow enough that the numbers are absurd by the time they ’ re changed in an update .
For example , in late April the model still stated that the expected total death toll was 60,000 , even as the US was clearly only a few days from that milestone .
Mokdad told me when we talked that a fix was in the works , @ @ @ @ @ @ @ later : The model now projects 73,433 deaths by August .
That , too , is probably an underestimate — most other models project that total will be reached next week .
In the IHME ’ s defense , it does offer a 95 percent confidence interval that is more accurate than the topline numbers .
That range goes from 59,343 — significantly less than the number of people already dead of the virus — to 114,228 .
That might sound like a wide range , but it ’ s still optimistic , @ @ @ @ @ @ @ to lie outside that range entirely .
MIT projects the US will surpass that upper bound in mid-June , and the Los Alamos forecast , like the MIT one , thinks the country will burst through the IHME ’ s 95 percent confidence interval in around six weeks .
That ’ s one of the core complaints about the IHME model : Its confidence intervals seem too narrow , both for its next-day and several-months-ahead predictions .
For instance , when used to predict the number of deaths in a state the very @ @ @ @ @ @ @ require any complex modeling of the long-term effects of uncertain policies ) , other researchers found that the true deaths were outside of the 95 percent confidence interval given by the model 70 percent of the time .
Those are embarrassingly poor results .
The day-to-day numbers are jumpy because some counties don ’ t report numbers every day , Mokdad told me .
“ The model is kind of confused because states are not reporting deaths consistently , ” he said , adding , “ The model assumes deaths will increase and @ @ @ @ @ @ @ so it reacts poorly when deaths instead vary day to day .
Smoothing over the course of a week — the latest update the IHME team made — means the model should be more predictive .
Indeed , the latest update to the model solves the problem where the next-day deaths are usually outside the model ’ s confidence interval by giving the model extremely wide confidence intervals .
That ’ s commendable .
It is better to be honest about your extremely high uncertainty than to claim certainty you don ’ t @ @ @ @ @ @ @ models with extremely wide ranges — 176 to 3,885 deaths today — but if that ’ s an accurate reflection of the state of uncertainty , then so be it .
It ’ s better to have a wide confidence interval , acknowledging your uncertainty , than to have a narrow one that is usually wrong .
And if next-day deaths are usually wrong , as IHME ’ s are , that undermines confidence in the long-term predictive value of the model .
An even bigger issue with the IHME model is that @ @ @ @ @ @ @ obscure its problems .
When it ’ s updated , it can be hard to see what its old , obviated predictions were .
A website , covid-projections.com , has been set up so that you can look at the predictions made by old versions of the IHME model ( and at the history of other models ) .
The IHME models are frequently fairly far off .
The IHME model is unusual compared to other epidemiological models for its design , too .
While most of the models use standard epidemiology modeling @ @ @ @ @ @ @ or use computer simulations , the IHME model is effectively just about fitting a curve from early data in China and Italy to the disease ’ s trajectory elsewhere .
The IHME model is based “ on a statistical model with no epidemiologic basis , ” the Annals of Internal Medicine critique argues .
The IHME team has defended its model against those complaints .
“ We ’ re willing to make a forecast .
Most academics want to hedge their bets and not be found to ever be wrong , ” IHME @ @ @ @ @ @ @ That ’ s not useful for a planner — you can ’ t go to a hospital and say you might need 1,000 ventilators , or you might need 5,000.
” The model has gotten better , but flaws remain The change to the model has certainly improved it , but some problems with the confidence intervals remain .
The new model acknowledges extremely high uncertainty about what will happen tomorrow — in my state , California , it says there will be between 5 and 103 deaths tomorrow .
That ’ s @ @ @ @ @ @ @ May 20 , the model is entirely sure there will be zero deaths .
The 95 percent confidence interval runs from zero deaths to ... zero deaths .
That ’ s an extremely strong claim .
I pressed the IHME team about whether they were sure of it .
Mokdad said that the model ’ s zero-deaths predictions were correct : “ Based on the graph , in certain states , yes — in California , May 17 , zero .
The virus is not circulating anymore ; you would expect it to @ @ @ @ @ @ @ is still circulating in California .
On April 28 , there were 1,187 new cases reported in the state .
Even if all infections in California stopped instantly today , it is likely that some already infected people would die in mid-May or later .
California ’ s case numbers ( like those of many states ) are declining , but only slowly , as social distancing has limited the spread of the virus but not fully stopped it .
That ties into another problem with the IHME model : It assumes that @ @ @ @ @ @ @ place , are always sufficient to rapidly decrease case numbers to zero .
In the report explaining the model , the researchers write that they look at four measures : “ School closures , non-essential business closures including bars and restaurants , stay-at-home recommendations , and travel restrictions including public transport closures .
Days with 1 measure were counted as 0.67 equivalents , days with 2 measures as 0.334 equivalents and with 3 or 4 measures as 0.
” In other words , the model has a built-in assumption that once three of @ @ @ @ @ @ @ , cases will rapidly fall to zero .
No new data can change that assumption , which is why the model continues to project zero deaths by mid-May in any area that hasn ’ t lifted social distancing restrictions , even though case numbers have only plateaued rather than declined in many areas .
There are many similar frustratingly confident predictions : On July 12 , the model says , we ’ ll need between 0 and 12 hospital beds in the whole US for coronavirus patients , and there will be 0 @ @ @ @ @ @ @ .
Why the IHME model ’ s problems shouldn ’ t be used as an indictment of epidemiology more broadly All these concerns make it frustrating to many epidemiologists that the IHME model is the one being widely used and cited .
“ That the IHME model keeps changing is evidence of its lack of reliability as a predictive tool , ” epidemiologist Ruth Etzioni told Stat News in an article about the problems with the model .
“ That it is being used for policy decisions and its results interpreted wrongly is @ @ @ @ @ @ @ Alex Merz , a microbiologist at the University of Washington ’ s School of Medicine , has written that “ @ IHME_UW ‘ s overly optimistic modeling projections contributed to this debacle ” of the coronavirus ’ s rapid spread in the US and condemned their “ amazing shrinking error band that , preposterously , constricts to zero uncertainty in mid-June .
That is not only bad science communication — it is bad science.
” The IHME model has also brought the discipline under fire from other fields .
Tyler Cowen , whose @ @ @ @ @ @ @ many of the papers demonstrating how poorly the IHME model works , argued “ now really is the time to be asking tough questions about epidemiology , and yes , epidemiologists.
” That ’ s not fair , many epidemiologists say — the model doesn ’ t use any of the standard tools of the discipline , and they hate it too .
“ That the IHME model keeps changing is evidence of its lack of reliability as a predictive tool ” “ The IHME approach departs from classic epidemiological modeling , ” @ @ @ @ @ @ @ Austin argued .
“ Rather than using systems of equations to project the person-to-person transmission of the virus , the [ IHME ] model postulates that COVID-19 deaths will rise exponentially and then decline in a pattern that roughly resembles a bell curve ( i.e.
, normal distribution ) .
” ( Their paper puts forward their own attempt at modeling the disease with a curve-fitting approach , but one tweaked to use better data and to better represent uncertainty . )
So it might not be fair to draw conclusions about the @ @ @ @ @ @ @ that mostly avoided using its standard tools .
Moreover , we should be aware that there are better models .
The Imperial College model that the British government relied on to inform the country ’ s coronavirus strategy has held up reasonably well , with case numbers loosely tracking the model ’ s predictions for what would happen if social distancing were implemented ( as it was shortly after the model was published ) .
It , too , has been frequently revised in response to new data and has come under criticism @ @ @ @ @ @ @ smaller and less pervasive , and the initial numbers before any revisions weren ’ t that far off .
Other models , employing more standard epidemiological approaches , perform even better — though usually in narrower domains , like trying to project just the peak of the outbreak , or just the rate of new cases .
The IHME team says their model performed pretty well on key problems like predicting the peak in most states ( it ’ ll be clear in coming weeks how true that is ) and that they @ @ @ @ @ @ @ make it better .
Meanwhile , as the debate over epidemiological models heats up , it ’ s fair to say that holding the shortcomings of the IHME model against epidemiology as a whole isn ’ t fair .
We don ’ t have all the answers But that still leaves the question : Why did the IHME model become so popular ?
The flaws I ’ ve noted above were also features that made the model appealing when it was first launched .
It was optimistic , projecting lower deaths than other @ @ @ @ @ @ @ , with narrow confidence intervals .
It projected hospitalizations , which few others were doing — though those projections turned out to be wrong because we didn ’ t know enough to project hospitalizations well at that stage .
In a time of uncertainty , the IHME model was compelling .
But it turns out the uncertainty being reflected in a lot of other , better models is showing up for a reason — there really is still a lot we don ’ t know about the course this disease will take .
@ @ @ @ @ @ @ , “ We need to value scientists and listen to experts , but part of listening means understanding that right now , what they ’ re saying is that they do not have all the answers.
” Given the enormous uncertainty we ’ re facing , responsible scientists are avoiding giving dramatic topline numbers that they ’ re unsure of , emphasizing the very wide confidence intervals on their estimates , and being careful not to publish results that the Trump administration or the public may interpret as definitive .
But people keep @ @ @ @ @ @ @ ! )
, and so any model that is presented more confidently will rise to prominence over models that are humbler and better reflect our confusion .
The IHME model is one of several released by researchers at the University of Washington , but it has been far more widely cited and discussed than the others — despite being less accurate .
Ultimately , the problem may not be that some models are inaccurate .
It was predictable that some models would be inaccurate , with the situation as confusing as it is @ @ @ @ @ @ @ ideally we ’ d be doing something like aggregating them , weighting each in our aggregation according to how well it has performed so far in predicting the crisis .
Our current process is almost exactly the opposite of that .
Most models are nearly ignored , while a few are cited by the White House and widely referred to in the press .
The process that makes some models prominent is not a process that picks out the best of the best — and in fact , it may actively be picking @ @ @ @ @ @ @ with notable numbers and statistics to cite , by leaning into the seductive “ appearance of certainty ” that researchers have warned us about .
In other words , the fact that many epidemiological models are performing badly isn ’ t great , but it could be part of a productive process of arriving at better models .
But the media , policymakers , and the public need to be conscious of what kind of models we gravitate to and why .
If we elevate the ones we like and quote numbers from @ @ @ @ @ @ @ , then the models will certainly end up shedding more heat than light .
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This coronavirus model keeps being wrong. Why are we still listening to it?
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How many people are likely to die in the United States of Covid-19? How many hospital beds is the country going to need? When will case numbers peak?
To answer those questions, many hospital planners, media outlets, and government bodies — including the White House — relied heavily on one particular model out of the many that have been published in the past two months: the University of Washington’s Institute for Health Metrics and Evaluation (IHME).
The model first estimated in late March that there’d be fewer than 161,000 deaths total in the US; in early April, it revised its projections to say the total death toll through August was “projected to be 60,415” (though it acknowledged the range could be between 31,221 and 126,703).
The model has been cited often by the White House and has informed its policymaking. But it may have led the administration astray: The IHME has consistently forecast many fewer deaths than most other models, largely because the IHME model projects that deaths will decline rapidly after the peak — an assumption that has not been borne out.
On Wednesday, the US death count passed the 60,000 mark that the IHME model had said was the likely total cumulative death toll. The IHME on April 29 released a new update raising its estimates for total deaths to 72,433, but that, too, looks likely to be proved an underestimate as soon as next week. Even its upper bound on deaths — now listed as 114,228 by August — is questionable, as some other models expect the US will hit that milestone by the end of May, and most project it will in June.
One analysis of the IHME model found that its next-day death predictions for each state were outside its 95 percent confidence interval 70 percent of the time — meaning the actual death numbers fell outside the range it projected 70 percent of the time. That’s not great! (A recent revision by IHME fixed that issue; more on this below.)
This track record has led some experts to criticize the model. “It’s not a model that most of us in the infectious disease epidemiology field think is well suited” to making projections about Covid-19, Harvard epidemiologist Marc Lipsitch told reporters.
But if that’s the case, how has it risen to such prominence among policymakers? Other models have done better than IHME’s at predicting the course of the epidemic, and many of them use approaches epidemiologists believe are more promising. Yet it’s the IHME model that has generally guided policymakers, for the most part, in the direction of focusing on a return to normal.
One potential explanation for its outsize influence: Some of the factors that make the IHME model unreliable at predicting the virus may have gotten people to pay attention to it. For one thing, it’s more simplistic compared to other models. That means it can be applied in ways more complicated models could not, such as providing state-level projections (something state officials really wanted), which other modelers acknowledged that they didn’t have enough data to offer.
Meanwhile, its narrow confidence intervals for state-by-state estimates meant it had quotable (and optimistic) topline numbers. A confidence interval represents a range of numbers wherein the model is very confident the true value will lie. A narrow range that gives “an appearance of certainty is seductive when the world is desperate to know what lies ahead,” a criticism of the IHME model published in the Annals of Internal Medicine argued. But the numbers and precise curves the IHME is publishing “suggests greater precision than the model is able to offer.”
The criticism of the IHME model, and an emerging debate over epidemiology models more broadly, has brought to light important challenges in the fight against the coronavirus. Good planning requires good projections. Models are needed to help predict resurgences and spot a potential second wave. Dissecting what the IHME model got wrong, what other models got right, and how the public and policymakers read these models is essential work in order to create the best pandemic plans possible.
What’s wrong with the IHME model of the coronavirus?
Models of disease spread are meant to help decision-makers in an environment where math will outperform intuitions. Most people have a hard time thinking about exponential growth, which is why many were taken by surprise in March as the virus spread. That’s why models can be useful.
Models usually lay out some foundational assumptions and offer projections based on those assumptions. The IHME model seeks to project death rates and hospitalization rates assuming widespread social distancing and strong measures to prevent the spread of the virus. Since testing is so unreliable at identifying the infection rate, the model uses only death rates as data (though evidence from all-cause mortality data suggests we’re undercounting coronavirus-caused deaths, too).
That projected hospitalization rate was one of the things that set it apart, IHME researcher Ali Mokdad told me. It was one of the few models offering that projection, and it was actionable information governments could grab on to and plan around.
Another reason it rose in prominence was IHME’s decision to model the effects of strong social distancing measures. That choice proved correct — strong measures were indeed taken across the US — and the lower death numbers the model churned out as a result reportedly led to it being received favorably by the Trump administration.
But as the weeks have passed, it has become clear that the IHME’s projections have been too optimistic, and slow to adjust to reflect the fact that deaths have plateaued rather than rapidly decreasing to zero. The IHME has been regularly updating its model as new data comes in, but the updates have often been slow enough that the numbers are absurd by the time they’re changed in an update. For example, in late April the model still stated the expected total death toll was 60,000, even as the US was clearly only a few days from that milestone.
Mokdad told me when we talked that a fix was in the works, and it went up a few days later: The model now projects 73,433 deaths by August. That, too, is probably an underestimate — most other models project that total will be reached next week.
In the IHME’s defense, it does offer a 95 percent confidence interval that is more accurate than the topline numbers. That range goes from 59,343 — significantly less than the number of people already dead of the virus — to 114,228. That might sound like a wide range, but it’s still optimistic, and the actual toll is on track to lie outside that range entirely. MIT projects the US will surpass that upper bound in mid-June, and the Los Alamos forecast, like the MIT one, predicts the country will burst through the IHME’s 95 percent confidence interval in around six weeks.
That’s one of the core complaints about the IHME model: its confidence intervals seem too narrow, both for its next-day and several-months-ahead predictions. For instance, when used to predict the number of deaths in a state the very next day (which doesn’t require any complex modeling of the long-term effects of uncertain policies), other researchers found the true deaths were outside of the 95 percent confidence interval given by the model 70 percent of the time. Those are embarrassingly poor results.
The day-to-day numbers are jumpy because some counties don’t report numbers every day, Mokdad told me. “The model is kind of confused because states are not reporting deaths consistently,” he said, adding, “The model assumes deaths will increase and then come back down,” and so it reacts poorly when deaths instead vary day to day. Smoothing over the course of a week — the latest update the IHME team made — means the model should be more predictive.
Indeed, the latest update to the model solves the problem where the next-day deaths are usually outside the model’s confidence interval by giving the model extremely wide confidence intervals. That’s commendable. It is better to be honest about your extremely high uncertainty than to claim certainty you don’t have.
That might lead to unsatisfying models with extremely wide ranges — 176 to 3,885 deaths today — but if that’s an accurate reflection of the state of uncertainty, then so be it. It’s better to have a wide confidence interval, acknowledging your uncertainty, than to have a narrow one that is usually wrong. And if next-day deaths are usually wrong, as IHME’s are, that undermines confidence in the long-term predictive value of the model.
(But while the IHME has made its day-to-day confidence intervals larger, it still has very narrow confidence intervals for its projections several weeks into the future — which is odd, as there should be even more uncertainty about those.)
An even bigger issue with the IHME model is that the way the model is published can obscure its problems. When it’s updated, it can be hard to see what its old, obviated predictions were. A website, covid-projections.com, has been set up so that you can look at the predictions made by old versions of the IHME model (and at the history of other models). The IHME models are frequently fairly far off.
The IHME model is unusual compared to other epidemiological models for its design, too. While most of the models use standard epidemiology modeling tactics like SEIR (susceptible-exposed-infected-recovered modeling) or use computer simulations, the IHME model is effectively just about fitting a curve from early data in China and Italy to the disease’s trajectory elsewhere.
The IHME model is based “on a statistical model with no epidemiologic basis,” the Annals of Internal Medicine critique argues.
The IHME team has defended its model against those complaints.
“We’re willing to make a forecast. Most academics want to hedge their bets and not be found to ever be wrong,” IHME director Christopher Murray told Politico. “That’s not useful for a planner — you can’t go to a hospital and say you might need 1,000 ventilators, or you might need 5,000.”
The model has gotten better, but flaws remain
The change to the model has certainly improved it, but some problems with the confidence intervals remain. The new model acknowledges extremely high uncertainty about what will happen tomorrow — in my state, California, it says there will be between 5 and 103 deaths tomorrow. That’s a really wide range.
But on May 20, the model is entirely sure there will be zero deaths. The 95 percent confidence interval runs from zero deaths to ... zero deaths.
That’s an extremely strong claim. I pressed the IHME team about whether they were sure of it. Mokdad said the model’s zero-deaths predictions were correct: “Based on the graph, in certain states, yes — in California, May 17, zero. The virus is not circulating anymore; you would expect it to go to zero.”
But the virus is still circulating in California. On April 28, there were 1,187 new cases reported in the state. Even if all infections in California stopped instantly today, it is likely that some already infected people would die in mid-May or later.
California’s case numbers (like those of many states) are declining, but only slowly, as social distancing has limited the spread of the virus but not fully stopped it. That ties into another problem with the IHME model: It assumes that social distancing measures, once put in place, are always sufficient to rapidly decrease case numbers to zero.
In the report explaining the model, the researchers write that they look at four measures: “School closures, non-essential business closures including bars and restaurants, stay-at-home recommendations, and travel restrictions including public transport closures. Days with 1 measure were counted as 0.67 equivalents, days with 2 measures as 0.334 equivalents and with 3 or 4 measures as 0.”
In other words, the model has a built-in assumption that once three of those measures have been put into place, cases will rapidly fall to zero. No new data can change that assumption, which is why the model continues to project zero deaths by mid-May in any area that hasn’t lifted social distancing restrictions, even though case numbers have only plateaued rather than declined in many areas.
There are many similar frustratingly confident predictions: On July 12, the model says, we’ll need between 0 and 12 hospital beds in the whole US for coronavirus patients, and there will be 0 deaths at any point after July 12.
Why the IHME model’s problems shouldn’t be used as an indictment of epidemiology more broadly
All these concerns make it frustrating to many epidemiologists that the IHME model is the one being widely used and cited. “That the IHME model keeps changing is evidence of its lack of reliability as a predictive tool,” epidemiologist Ruth Etzioni told Stat News in an article about the problems with the model. “That it is being used for policy decisions and its results interpreted wrongly is a travesty unfolding before our eyes.”
Alex Merz, a microbiologist at the University of Washington’s School of Medicine, has written that “@IHME_UW’s overly optimistic modeling projections contributed to this debacle” of the coronavirus’s rapid spread in the US, and condemned their “amazing shrinking error band that, preposterously, constricts to zero uncertainty in mid-June. That is not only bad science communication — it is bad science.”
The IHME model has also brought the discipline under fire from other fields. Tyler Cowen, whose widely read blog Marginal Revolution has linked many of the papers demonstrating how poorly the IHME model works, argued “now really is the time to be asking tough questions about epidemiology, and yes, epidemiologists.”
That’s not fair, many epidemiologists say — the model doesn’t use any of the standard tools of the discipline, and they hate it too.
“That the IHME model keeps changing is evidence of its lack of reliability as a predictive tool”
“The IHME approach departs from classic epidemiological modeling,” epidemiological modelers at the University of Texas at Austin argued. “Rather than using systems of equations to project the person-to-person transmission of the virus, the [IHME] model postulates that COVID-19 deaths will rise exponentially and then decline in a pattern that roughly resembles a bell curve (i.e., normal distribution).” (Their paper puts forward their own attempt at modeling the disease with a curve-fitting approach, but one tweaked to use better data and to better represent uncertainty.)
So it might not be fair to draw conclusions about the field as a whole from one model that mostly avoided using its standard tools.
Moreover, the public should be aware that there are better models. The Imperial College model that the British government relied on to inform their country’s coronavirus strategy has held up reasonably well, with case numbers loosely tracking the model’s predictions for what would happen if social distancing were implemented (as it was shortly after the model was published). It, too, has been frequently revised in response to new data and has come under criticism for overconfidence, but the inaccuracies are smaller and less pervasive, and the initial numbers before any revisions weren’t that far off.
Other models, employing more standard epidemiological approaches, perform even better — though usually in narrower domains, like trying to project just the peak of the outbreak, or just the rate of new cases.
The IHME team says their model performed pretty well on key problems like predicting the peak in most states (it’ll be clear in coming weeks how true that is) and that they’re continuing to revise it to make it better. Meanwhile, as the debate over epidemiological models heats up, it’s fair to say that holding the shortcomings of the IHME model against epidemiology as a whole isn’t fair.
We don’t have all the answers
But that still leaves the question: Why did the IHME model become so popular?
The flaws I’ve noted above were also features that made the model appealing when it was first launched. It was optimistic, projecting lower deaths than other models. It was clear and precise, with narrow confidence intervals. It projected hospitalizations, which few others were doing — though those projections turned out to be wrong because not enough was known to project hospitalizations well at that stage. In a time of uncertainty, the IHME model was compelling.
But it turns out the uncertainty being reflected in a lot of other, better models is showing up for a reason — there really is still a lot unknown about the course this disease will take.
As my colleague Matt Yglesias has written, “We need to value scientists and listen to experts, but part of listening means understanding that right now, what they’re saying is that they do not have all the answers.”
Given the enormous uncertainty ahead, responsible scientists are avoiding giving dramatic topline numbers they’re unsure of, emphasizing the very wide confidence intervals on their estimates, and being careful not to publish results that the Trump administration or the public may interpret as definitive.
But people keep searching for definitive answers (understandably so), and so any model that is presented more confidently will rise to prominence over models that are humbler and better reflect the confusion.
The IHME model is one of several released by researchers at the University of Washington, but it has been far more widely cited and discussed than the others — despite being less accurate.
Ultimately, the problem may not be that some models are inaccurate. It was predictable that some models would be inaccurate, with the situation as confusing as it is. There are dozens of models, and ideally we’d be doing something like aggregating them and weighting each according to how well it has performed so far in predicting the crisis.
The current process is almost exactly the opposite of that. Most models are nearly ignored, while a few are cited by the White House and widely referred to in the press. The process that makes some models prominent is not a process that picks out the best of the best — and in fact, it may actively be picking out worse models, by emphasizing ones with notable numbers and statistics to cite, by leaning into the seductive “appearance of certainty” that researchers have warned about.
In other words, the fact that many epidemiological models are performing badly isn’t great, but it could be part of a productive process of arriving at better models. But the media, policymakers, and the public need to be conscious of what kind of models we gravitate to and why. If we elevate the ones we like and quote numbers from them as if they’re definitive, then the models will certainly end up shedding more heat than light.
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vox--2020-05-02--The Senate doesn’t have enough coronavirus test kits — but it doesn’t want ones from the White House

House Speaker Nancy Pelosi and Senate Majority Leader Mitch McConnell have declined an offer from the Trump administration to be sent rapid coronavirus tests for congressional use as lawmakers prepare to return to the Capitol next week .
In a joint statement , released Saturday , the leaders appeared to be trying to avoid being seen as accepting special treatment , at a moment when testing capacity across the country is still far behind what ’ s needed to properly assess the spread of the virus .
“ Congress is grateful for the Administration ’ s generous offer to deploy @ @ @ @ @ @ @ , but we respectfully decline the offer at this time , ” the leaders wrote .
“ Our country ’ s testing capacities are continuing to scale up nationwide , and Congress wants to keep directing resources to the front-line facilities where they can do the most good the most quickly.
” The offer came as the Senate is expected to get back to work in Washington on Monday .
But testing capacity at the Capitol is far from adequate for monitoring cases among lawmakers and their staff — which is one reason @ @ @ @ @ @ @ plan to end its recess .
On Thursday , the Capitol ’ s attending physician , Brian Monahan , reportedly told lawmakers testing capacity was so limited that senators could only get tested if they appeared to be sick .
And according to Sen. Dianne Feinstein ( D-CA ) , Monahan told “ House leadership that he recommends against resuming their session ” earlier in the week due to concerns over testing and community spread .
Despite these concerns , McConnell didn ’ t alter his plan for the Senate to return to @ @ @ @ @ @ @ leadership , on the other hand , heeded Monahan ’ s advice ; it is unclear when that body will return to Washington .
A number of senators raised their concerns with McConnell ’ s plan , and seemingly in response , Secretary of Health and Human Services Alex Azar tweeted Friday that the administration was sending Abbott coronavirus tests — which can provide results in as few as five minutes — for the Senate to use as it got back to work .
“ Good news : as the Senate reconvenes to @ @ @ @ @ @ @ during this public health crisis , we have now received an initial request and are sending 3 Abbott point of care testing machines and 1,000 tests for their use , ” Azar tweeted .
Saturday morning , Trump boasted about the offer , tweeting that there was “ tremendous CoronaVirus testing capacity in Washington for the Senators returning to Capital [ sic ] Hill on Monday.
” Trump added , “ Likewise the House , which should return but isn ’ t because of Crazy Nancy P [ elosi ] .
” The @ @ @ @ @ @ @ ’ t have been sufficient for the Senate ’ s needs It ’ s unclear how much use the 1,000 tests would have been .
There are 100 senators , 435 members of the House , and across both chambers , there are tens of thousands of staff members , many of whom would be traveling across the country through high-risk areas like airports to get to Washington .
So an extra 1,000 tests doesn ’ t seem to be a game-changer in terms of helping ensure that everyone working in the Capitol @ @ @ @ @ @ @ Congress would need enough tests to institute the sort of testing regime used in the White House .
Trump and Vice President Mike Pence are reportedly tested more frequently than once a week , and a number of senior staff members like White House Chief of Staff Mark Meadows are tested weekly .
Anyone who comes into regular contact with Trump also gets tested .
Good testing in the Senate is imperative to minimize the chances of community spread , as Vox ’ s Li Zhou explained on Friday , because lawmakers are @ @ @ @ @ @ @ Capitol ’ s limited testing capacity is concerning in itself : Many senators are over the age of 65 and within the range of individuals who are more likely to experience severe coronavirus symptoms .
And while in the Capitol , their work inherently brings them , and staffers , into close contact with one another .
Prior to departing for recess , senators were spotted in tight groups on the chamber floor during a stimulus vote .
And when Sen. Rand Paul ( R-KY ) tested positive for Covid-19 , several of @ @ @ @ @ @ @ ( R-UT ) and Mike Lee ( R-UT ) had to self-isolate out of concerns about their exposure .
McConnell and Pelosi may have turned away tests that might have made the Hill slightly safer , but their judgment reflects political common sense : when across the country it ’ s still strikingly difficult to get tested , it might not be a great look to get a special set of tests as a favor from the White House .
Every day at Vox , we aim to answer your most important questions @ @ @ @ @ @ @ around the world , with information that has the power to save lives .
Our mission has never been more vital than it is in this moment : to empower you through understanding .
Vox ’ s work is reaching more people than ever , but our distinctive brand of explanatory journalism takes resources — particularly during a pandemic and an economic downturn .
Your financial contribution will not constitute a donation , but it will enable our staff to continue to offer free articles , videos , and podcasts at the quality @ @ @ @ @ @ @ Please consider making a contribution to Vox today .

House Speaker Nancy Pelosi and Senate Majority Leader Mitch McConnell have declined an offer from the Trump administration to be sent rapid coronavirus tests for congressional use as lawmakers prepare to return to the Capitol next week.
In a joint statement, released Saturday, the leaders appeared to be trying to avoid being seen as accepting special treatment, at a moment when testing capacity across the country is still far behind what’s needed to properly assess the spread of the virus.
“Congress is grateful for the Administration’s generous offer to deploy rapid COVID-19 testing capabilities to Capitol Hill, but we respectfully decline the offer at this time,” the leaders wrote. “Our country’s testing capacities are continuing to scale up nationwide, and Congress wants to keep directing resources to the front-line facilities where they can do the most good the most quickly.”
The offer came as the Senate is expected to get back to work in Washington on Monday. But testing capacity at the Capitol is far from adequate for monitoring cases among lawmakers and their staff — which is one reason why the House of Representatives does not plan to end its recess.
On Thursday, the Capitol’s attending physician, Brian Monahan, reportedly told lawmakers testing capacity was so limited that senators could only get tested if they appeared to be sick. And according to Sen. Dianne Feinstein (D-CA), Monahan told “House leadership that he recommends against resuming their session” earlier in the week due to concerns over testing and community spread.
Despite these concerns, McConnell didn’t alter his plan for the Senate to return to the Capitol on May 4. House leadership, on the other hand, heeded Monahan’s advice; it is unclear when that body will return to Washington.
A number of senators raised their concerns with McConnell’s plan, and seemingly in response, Secretary of Health and Human Services Alex Azar tweeted Friday that the administration was sending Abbott coronavirus tests — which can provide results in as few as five minutes — for the Senate to use as it got back to work.
“Good news: as the Senate reconvenes to do important work for the American people during this public health crisis, we have now received an initial request and are sending 3 Abbott point of care testing machines and 1,000 tests for their use,” Azar tweeted.
Saturday morning, Trump boasted about the offer, tweeting that there was “tremendous CoronaVirus testing capacity in Washington for the Senators returning to Capital [sic] Hill on Monday.” Trump added, “Likewise the House, which should return but isn’t because of Crazy Nancy P[elosi].”
The tests the Trump administration offered likely wouldn’t have been sufficient for the Senate’s needs
It’s unclear how much use the 1,000 tests would have been. There are 100 senators, 435 members of the House, and across both chambers, there are tens of thousands of staff members, many of whom would be traveling across the country through high-risk areas like airports to get to Washington. So an extra 1,000 tests doesn’t seem to be a game-changer in terms of helping ensure that everyone working in the Capitol is free of coronavirus.
Instead, Congress would need enough tests to institute the sort of testing regime used in the White House. Trump and Vice President Mike Pence are reportedly tested more frequently than once a week, and a number of senior staff members like White House Chief of Staff Mark Meadows are tested weekly. Anyone who comes into regular contact with Trump also gets tested.
Good testing in the Senate is imperative to minimize the chances of community spread, as Vox’s Li Zhou explained on Friday, because lawmakers are at particular risk for Covid-19:
The Capitol’s limited testing capacity is concerning in itself: Many senators are over the age of 65 and within the range of individuals who are more likely to experience severe coronavirus symptoms. And while in the Capitol, their work inherently brings them, and staffers, into close contact with one another. Prior to departing for recess, senators were spotted in tight groups on the chamber floor during a stimulus vote. And when Sen. Rand Paul (R-KY) tested positive for Covid-19, several of his colleagues including Sens. Mitt Romney (R-UT) and Mike Lee (R-UT) had to self-isolate out of concerns about their exposure.
McConnell and Pelosi may have turned away tests that might have made the Hill slightly safer, but their judgment reflects political common sense: when across the country it’s still strikingly difficult to get tested, it might not be a great look to get a special set of tests as a favor from the White House.


vox--2020-05-02--Trump’s purge of inspectors general continues. It’s an assault on good governance.

President Donald Trump ’ s purge of officials who question his leadership continues — this time targeting another watchdog , one who accurately described America ’ s dearth of medical supplies and tests as the country works to confront the coronavirus crisis .
Late on Friday , the Trump administration announced it had nominated Jason Weida — an assistant US attorney in Boston — for an inspector general position at the Department of Health and Human Services , one of the agencies tasked with responding to the country ’ s outbreak .
The problem is that position is already filled @ @ @ @ @ @ @ nomination had not publicly expressed a desire to step down as the department ’ s principal deputy inspector general .
So why the sudden change ?
The answer is easy enough to divine : her report released last month laid bare Trump ’ s feckless coronavirus response .
In late March , Grimm surveyed over 300 hospitals in nearly 50 states and territories to better understand what they faced as an influx of coronavirus patients flooded their facilities .
What she found was “ their most significant challenges centered on testing and caring @ @ @ @ @ @ @ safe .
Hospitals said that severe shortages of testing supplies and extended waits for test results limited hospitals ’ ability to monitor the health of patients and staff.
” “ They also reported that widespread shortages of personal protective equipment ( PPE ) put staff and patients at risk , ” she also wrote .
“ In addition , hospitals said that they were not always able to maintain adequate staffing levels or to offer staff adequate support.
” While the report didn ’ t directly question the federal government ’ s response @ @ @ @ @ @ @ serious concerns about their ability to care for patients many weeks after the first coronavirus case was detected in the US .
Trump was asked about this report during an April 6 press conference with the government ’ s coronavirus task force .
He fumed at the conclusions .
“ It ’ s just wrong .
Did I hear the word ‘ inspector general ’ ?
Really ?
It ’ s wrong .
And they ’ ll talk to you about it .
It ’ s wrong , ” he asserted .
And @ @ @ @ @ @ @ wrote the report .
“ Where did he come from — the inspector general ?
What ’ s his name ?
... No , what ’ s his name ?
What ’ s his name ?
...
If you find me his name , I ’ d appreciate it.
” When it became clear to him that Grimm served in the Obama administration , he derided the report as “ a typical fake-news deal.
” But , Grimm is a career official who ’ s been in government since the Clinton administration and @ @ @ @ @ @ @ Republicans , including Trump .
She took over her current role as acting inspector general after the last person in that position left .
None of that information seemed to sink in for Trump , as just one day later he assailed Grimm on Twitter .
It ’ d be one thing if Trump ’ s ire was reserved only for Grimm and her eventual dismissal was a one-off thing — as disturbing as such a one-off would be .
But this is clearly a pattern for the president , one that ’ @ @ @ @ @ @ @ of good governance in America .
In early April , Trump went on a firing rampage .
He pushed out Intelligence Community Inspector General Michael Atkinson , the oversight official responsible for forwarding the Ukraine whistleblower ’ s report to Congress and triggering Trump ’ s impeachment .
Days later , after attacking Grimm , news broke that Trump removed another inspector general , Glenn Fine , from his job overseeing coronavirus relief spending — abruptly and without explanation .
Vox ’ s Zack Beauchamp last month described what all of this really @ @ @ @ @ @ @ end point after Trump pressured Ukraine into opening investigations into former Vice President Joe Biden ’ s family last year without an indictment from the Senate .
The president has surely concluded he can now get away with anything , including punishing those who dare point out his presidency is anything other than a roaring success .
That , experts note , is scary .
“ This is a war on accountability , ” tweeted Walter Shaub , the former White House ethics chief who stepped down over Trump ’ s actions , @ @ @ @ @ @ @ t end until Trump destroys every last institutional safeguard against corruption.
” Based on Trump ’ s decision to replace Grimm , it ’ s a war the president seems to be winning .
Every day at Vox , we aim to answer your most important questions and provide you , and our audience around the world , with information that has the power to save lives .
Our mission has never been more vital than it is in this moment : to empower you through understanding .
Vox ’ s work is @ @ @ @ @ @ @ our distinctive brand of explanatory journalism takes resources — particularly during a pandemic and an economic downturn .
Your financial contribution will not constitute a donation , but it will enable our staff to continue to offer free articles , videos , and podcasts at the quality and volume that this moment requires .
Please consider making a contribution to Vox today .

President Donald Trump’s purge of officials who question his leadership continues — this time targeting another watchdog, one who accurately described America’s dearth of medical supplies and tests as the country works to confront the coronavirus crisis.
Late on Friday, the Trump administration announced it had nominated Jason Weida — an assistant US attorney in Boston — for an inspector general position at the Department of Health and Human Services, one of the agencies tasked with responding to the country’s outbreak.
The problem is that position is already filled by Christi Grimm, who before the nomination had not publicly expressed a desire to step down as the department’s principal deputy inspector general.
So why the sudden change? The answer is easy enough to divine: her report released last month laid bare Trump’s feckless coronavirus response.
Why Grimm’s coronavirus report made Trump so angry
In late March, Grimm surveyed over 300 hospitals in nearly 50 states and territories to better understand what they faced as an influx of coronavirus patients flooded their facilities. What she found was “their most significant challenges centered on testing and caring for patients with COVID-19 and keeping staff safe. Hospitals said that severe shortages of testing supplies and extended waits for test results limited hospitals’ ability to monitor the health of patients and staff.”
“They also reported that widespread shortages of personal protective equipment (PPE) put staff and patients at risk,” she also wrote. “In addition, hospitals said that they were not always able to maintain adequate staffing levels or to offer staff adequate support.”
While the report didn’t directly question the federal government’s response, it made clear hospitals still had serious concerns about their ability to care for patients many weeks after the first coronavirus case was detected in the US.
Trump was asked about this report during an April 6 press conference with the government’s coronavirus task force. He fumed at the conclusions.
“It’s just wrong. Did I hear the word ‘inspector general’? Really? It’s wrong. And they’ll talk to you about it. It’s wrong,” he asserted. And then he wanted to know exactly who wrote the report. “Where did he come from — the inspector general? What’s his name? ... No, what’s his name? What’s his name? ... If you find me his name, I’d appreciate it.”
When it became clear to him that Grimm served in the Obama administration, he derided the report as “a typical fake-news deal.” But, Grimm is a career official who’s been in government since the Clinton administration and has worked for two Democrats and two Republicans, including Trump. She took over her current role as acting inspector general after the last person in that position left.
None of that information seemed to sink in for Trump, as just one day later he assailed Grimm on Twitter.
Why didn’t the I.G., who spent 8 years with the Obama Administration (Did she Report on the failed H1N1 Swine Flu debacle where 17,000 people died?), want to talk to the Admirals, Generals, V.P. & others in charge, before doing her report. Another Fake Dossier! — Donald J. Trump (@realDonaldTrump) April 7, 2020
It’d be one thing if Trump’s ire was reserved only for Grimm and her eventual dismissal was a one-off thing — as disturbing as such a one-off would be. But this is clearly a pattern for the president, one that’s a troubling sign about the health of good governance in America.
Ethics experts call Trump’s recent staffing changes “a war on accountability”
In early April, Trump went on a firing rampage.
He pushed out Intelligence Community Inspector General Michael Atkinson, the oversight official responsible for forwarding the Ukraine whistleblower’s report to Congress and triggering Trump’s impeachment. Days later, after attacking Grimm, news broke that Trump removed another inspector general, Glenn Fine, from his job overseeing coronavirus relief spending — abruptly and without explanation.
Vox’s Zack Beauchamp last month described what all of this really meant:
Atkinson’s firing was not merely revenge for impeachment. It was a warning that, during the coronavirus crisis, Trump would not tolerate similarly vigorous oversight of his conduct. Official watchdogs are on notice: Doing your job too effectively could be career suicide. This is a war on the very concept of oversight — and a sign that the United States is not immune from a global trend of creeping coronavirus authoritarianism.
This was perhaps the logical end point after Trump pressured Ukraine into opening investigations into former Vice President Joe Biden’s family last year without an indictment from the Senate. The president has surely concluded he can now get away with anything, including punishing those who dare point out his presidency is anything other than a roaring success.
That, experts note, is scary. “This is a war on accountability,” tweeted Walter Shaub, the former White House ethics chief who stepped down over Trump’s actions, “and [it] won’t end until Trump destroys every last institutional safeguard against corruption.”
Based on Trump’s decision to replace Grimm, it’s a war the president seems to be winning.