Tracking coronavirus in animals takes on new urgency

Authors: Ariana Eunjung Cha  May 20, 2022 The Washington Post

Researchers Sarah Hamer and Lisa Auckland donned their masks and gowns as they pulled up to the suburban home in College Station, Tex. The family of three inside had had covid a few weeks earlier, and now it was time to check on the pets.

Oreo the rabbit was his usual chill self, and Duke the golden retriever was a model patient, lying on his back as Hamer and Auckland swabbed their throats and took blood samples. But Ellie, a Jack Russell terrier, wiggled and barked in protest. “She was not exactly happy with us,” Auckland recalled. “But we’re trying to understand how transmission works within a household, so we needed samples from everyone.”

The questions driving the researchers goes far beyond pet welfare. They’re investigating whether animals infected with the coronavirus might become reservoirs for the evolution of new variants that might jump back into humans — an issue with huge implications for both human and animal health.

In year three of the pandemic, scientists have confirmed that the virus believed to have first spilled over to humans from bats or possibly pangolins has already spread to at least 20 other animal species, including big cats, ferrets, North American white-tailed deer and great apes. To date, incidents of animals infecting humans are rare. Only three species — hamsters in Hong Kong, mink in the Netherlands and, possibly, also white-tailed deer in the United States and Canada — have transmitted a mutated, albeit mostly benign, version of the virus back to humans. But those cases are spurring concern.

The search for infected animals in Texas — led by Texas A&M University in conjunction with the U.S. Centers for Disease Control and Prevention — is part of a scattered but growing global effort to monitor pets, livestock and wildlife for new, potentially more dangerous coronavirus variants and stop them from wreaking havoc on humans.

Tracking coronavirus in animals takes on new urgency

The World Health Organization warned in March that animal reservoirs could lead to “potential acceleration of virus evolution” and new variants. The agency noted the large numbers of infected animals, and it urged countries to increase their monitoring of mammalian species for SARS-CoV-2 and suspend the sale of live, wild mammals in food markets as an emergency measure. The CDC this year also endorsed efforts to track the virus in animals, even as it described the risk of transmission to humans as “low.”

In March, a city in China ordered the killing of pets whose owners had tested positive, but the action was put on hold after a public outcry. (There was no evidence these pets were spreading the virus.) Earlier this year, Hong Kong’s first outbreak in months is believed to have been caused by hamsters imported from Europe.

Meanwhile, U.S. scientists estimated late last year that a third of white-tailed deer in several states appear to carry antibodies to the coronavirus, suggesting recent infection, and three snow leopards at the Lincoln Children’s Zoo in Nebraska died of covid-19. In 2020, Denmark had culled millions of mink after they were infected and the virus spilled back into humans.

Most new variants are simply scientific curiosities, and they die out. The challenge for scientists is to create a system to identify the dangerous ones — the ones that are more transmissible, more deadly or more likely to break through vaccines — and attempt to halt their transmission.

“We need to be very aware there are going to be more epidemics and pandemics and plan ahead,” said Pamela Bjorkman, a professor at the California Institute of Technology.

In the span of more than two years, the coronavirus itself has been evolving faster than most anyone expected — creating an evolutionary “super-tree” with major new branches seeming to sprout every few months. The environment in which these variants are forming, researchers surmise, is likely one that allows the virus to live longer and thereby make more copies of itself, increasing the prospect of new mutations.

One leading theory is immunocompromised patients, such as those with cancer or HIV, who can harbor the infection for many weeks or months, as compared with mere days for most people. But another more daunting possibility is that the virus is finding hosts among the more than 1 million animal species, many still not catalogued, that inhabit Earth.

“It’s a scary thing.” Bjorkman, who has been working on a universal coronavirus vaccine, said there has long been viral transmission between humans and animals that nobody pays attention to.

The problem is that “every once in a while, there is transmission that catches on” and will explode if it spills into the human population, she said.

Animal reservoirs

Scientists believe most major outbreaks of disease serious enough to be deemed epidemics or pandemics have begun with animals.

H5N1, a highly pathogenic flu that occurs in wild birds, sent fear through the medical community after a young boy died of it in Hong Kong in May 1997. (The first U.S. case was reported in Colorado in April.) SARS1, which caused an outbreak in Asia from 2002 to 2004, infecting more than 8,000 people, is believed to have jumped from civets, a catlike mammal, to humans.

The H1N1 influenza virus that hit the world in 2009 and is estimated to have infected as many as 1.4 billion people is believed to be what scientists call a “reassortment” of flu that has been found in birds, pigs and humans. The MERS virus, first reported in Saudi Arabia in 2012 with a fatality rate as high as 35 percent, is believed to have emerged from camels.

SARS-CoV-2, the pathogen terrorizing the world since early 2020, has higher potential for transmission to animals than many other known viruses because it invades the body by latching onto a receptor known as ACE2, which is found in a number of species. In recent weeks, researchers reported evidence to support early suspicions that the original coronavirus that jumped into humans sometime before January 2020 may have come from wet market animals, perhaps bats, perhaps raccoon dogs, or another animal used for food or fur in Wuhan, China.

As of April, scientists had logged 675 coronavirus outbreaks in animals, affecting 23 species in 36 countries, and other species have been shown to be vulnerable in lab experiments. But there are likely many thousands more that are susceptible. A University of California at Davis study of the potential vulnerability of different species to coronavirus infection — based on modeling of which ones had ACE2 cellular receptors similar to those in humans, because that’s how the virus enters the body — ranked animals as diverse as giant anteaters and bottlenose dolphins as high risk, and Siberian tigers, sheep and cattle as medium risk.

So far, most of the coronavirus transmission appears to have jumped from animals to humans. Scientists have documented infection going in the other direction only three times: from mink to humans, hamsters to humans, and one likely case of deer to humans. None of those three events is believed to have introduced dangerous variants.

In its monthly situation report in late April, the World Organization for Animal Health said that although the main driver of international viral spread is still human-to-human transmission, animal cases “continue to rise.” The big question is not whether certain animals can be infected, researchers say. It is which might act as so-called reservoirs that can serve as sources of new variants that could pose greater threats to humans.

Hong Kong

Leo Poon recalled feeling immediately uneasy in January when he got word of a new coronavirus infection in the northern part of the island.

Hong Kong had been quiet for months, and the delta wave that had devastated much of the world seemed to wash over the city with few cases. The city had implemented a strict — some it called draconian — “zero covid” policy that had kept the islands infection-free for long periods. Poon, head of the division of public health laboratory science at the University of Hong Kong, had been helping the government sequence SARS-CoV-2 samples from patients to find out where infections originated and which close contacts were at risk.

This new patient was a 23-year-old saleswoman with mild symptoms of headache and fever who had not had contact with anyone with an infection. Nor had she traveled or been in contact with anyone who had. When Poon checked the global databank that scientists are using to track the evolution of the virus, he was surprised to find that some of the genetic mutations in the young woman’s sample appeared to be novel and had never been documented in any other human sample.

As he delved into the case report, Poon noticed the woman worked at a pet shop, and that’s when it hit him: Could she have gotten covid from an animal?

A flurry of hurried phone calls and emails followed, and Hong Kong authorities locked down the store, Little Boss in the shopping district of Causeway Bay, and a related warehouse, swabbing the nearly 200 animals they found.“We really have to highlight the concept of one health. It’s not only about human health. We have to consider health in animals and the environment. And if you don’t look after these areas, we are the one that suffer at the end.” Leo Poon, head of the division of public health laboratory science at the University of Hong Kong

The rabbits, chinchillas and guinea pigs were cleared. But 11 of the hamsters, specifically the golden Syrian hamsters, tested positive for coronavirus, with a variant similar to the one that had infected the woman. The timelines matched: The hamsters had been flown in from the Netherlands on Jan. 7. The woman became ill on Jan. 11.

The young woman, and a second patient believed to have gotten sick directly from a hamster whose case was described in a preprint paper in the Lancet, did not get very ill. But Poon worried that one of the major changes to the virus related to how it attaches to receptors. He feared that if it was allowed to hop back and forth between humans and animals, it might ultimately change into something less benign.

Investigators identified 150 people, mostly customers who had visited the store, who were at risk of being infected and ordered them into quarantine, banned the importation of small mammals, and put to sleep the remaining 2,000 hamsters in city pet stores within days of the discovery of the link. Public health officials “strongly advised” pet owners to turn over any additional hamsters to be euthanized.

“We really have to highlight the concept of one health,” Poon reflected. “It’s not only about human health. We have to consider health in animals and the environment. And if you don’t look after these areas, we are the one that suffer at the end.”

Ontario, Canada

The first white-tailed deer were tested on a whim.

It was early 2020, and Andrew Bowman, an associate professor of veterinary medicine at Ohio State University, was tracking the animals near Columbus for other purposes and thought he might as well add in one more test. When the results came back positive, he was so taken aback that he rechecked and then triple-checked, and then called in the U.S. Department of Agriculture to verify before announcing the finding to the world.

“At that point, it was a surprise,” he recalled. “But when we step back, we really shouldn’t have been that surprised.”

As suburban communities expand into what was once forest, the population of white-tailed deer living in proximity to humans is increasing. While few people interact directly with deer, scientists are investigating whether the animals might be exposed to the coronavirus through discarded face masks and other trash, contaminated water or, perhaps, some intermediary species. Several scientific teams confirmed the breadth of the cases in deer and found that most of the animals appeared to be asymptomatic.

In August, the USDA announced that its own analysis found antibodies in about one-third of deer in Illinois, Michigan, New York and Pennsylvania. It issued a warning to the public to be cautious in their interactions, and to limit contact between wildlife and domestic animals.

In Canada, Brad Pickering, an animal pathogens expert with the country’s Food Inspection Agency, went further to learn more about the evolution of the virus in deer. Examining samples of 300 animals hunted in Ontario from Nov. 1 to Dec. 31, he was shocked to find 76 mutations in some deer strains.

“That’s a lot, more than omicron,” he said of the number of mutations. “It is showing there seems to be some adaptation to deer or wildlife, in general.”

According to a preprint paper he and a group of more than 30 scientists posted in February, the closest known branches to the deer strain were found in humans in Michigan a year earlier. Those, in turn, were related to mink samples from Michigan previously identified in September/October 2020. Given that the area where the deer samples were taken in southwestern Ontario is adjacent to Michigan, the researchers wondered whether the variant had jumped from humans to mink and then to deer.“Even if it’s left the human population. It doesn’t mean we’re done with it.” Brad Pickering, animal pathogens expert with the country’s Food Inspection Agency

Even more odd, the researchers identified a human sample from Michigan that was very similar to the highly mutated deer sample. It turned out that person had had close contact with deer, and while the information could not conclusively point to deer-to-human transmission, the evidence was strong.

Neither Bowman nor Pickering worries that the new deer strains of coronavirus pose any immediate danger to humans. Bowman said that the threat is “more of a long-game question.” If “it evolves in a different trajectory than humans, how long do we have before it’s diverse enough that the next pandemic strain spills back into humans?”

“Even if it’s left the human population,” Pickering said, “it doesn’t mean we’re done with it.”


Sarah Hamer’s work at Texas A&M University involves the broad ecology of animals and humans. The future of coronavirus variants, she explained, depends not just on how we interact with a single animal species but also on the web of associations among humans, domestic animals and wildlife.

For cats and dogs, scientists are somewhat confident the transmission has been mostly one-way, from humans to the animals spurred by people snuggling and playing with their pets. Hamer’s research aims to clarify details about the chain of transmission — who brought the infection into the household, who got it next and so forth.

Figuring out what is going on with deer has been much more challenging.

“We cannot explain it as spillover from humans, because not all of them have had a close contact,” Hamer explained. “That’s where it gets pretty interesting to think about.”

On the same weekend that Hamer and Auckland were taking samples from Oreo, Duke, Ellie and their human owners, another team from the same lab was in a nearby forest in eastern Texas, studying small and medium mammals.

After setting a couple of hundred traps that evening, they returned the next morning to find wild mice, wild rats and other mammals. While the primary purpose of the study was to look at vector-borne pathogens such as tick diseases, all the animals were also swabbed for coronavirus before being released back into the woods. The wildlife samples are being stored in a freezer, as Hamer awaits funding to test them.

Right now, such efforts are being conducted mostly piecemeal, often added to non-covid work that is already funded. She and other scientists say a more coordinated global surveillance approach that will target a range of species during different seasons and in different geographic areas is needed to stop a potential new generation of variants.

“There is so much more that should be done,” she said.

COVID cases rise in almost every state

Authors: Tina Reed Kavya Beheraj May 19, 2022 Axios

The COVID wave is accelerating across the U.S., with Maine being the only state to report a slight decline in the last two weeks.

Why it matters: A 53% jump in cases and a rise in hospitalizations reflects how case growth has moved beyond the Northeast, with metropolitan areas with high vaccination rates increasingly accounting for a higher share of disease spread.

That hasn’t appreciably changed public behavior, with one in three Americans now saying the pandemic is over, according to the latest installment of the Axios/Ipsos Coronavirus Index.

  • Concern among Americans ticked up slightly, the poll showed.
  • “But there’s absolutely no behavior change. If anything, behaviors are moving in the other direction,” said Ipsos pollster and senior vice president Chris Jackson, pointing to more time spent with friends and family and steady rates of dining out.

By the numbers: There were roughly 96,000 new daily cases over the last week, up more than 50% from about 62,500 two weeks ago.

  • The highest reported case rates continue to be in the northeast with Connecticut and Rhode Island both posting more than 76 new cases per 100,000 people. Massachusetts tallied 66 new cases per 100,000.
  • States with some of the biggest recent case jumps include Hawaii, Michigan, New Jersey and Delaware.
  • Eight states are still reporting new case rates in below 10 new cases per 100,000 people, including Idaho, Montana, South Dakota, Wyoming, Nebraska, Oklahoma, Arkansas, Alabama.

Zoom in: The U.S. averaged roughly 300 daily deaths, d0wn 11% from about 340 two weeks ago.

  • That returns the U.S. to a trend of declining COVID deaths after a 7% increase last week.

The big picture: The U.S. COVID death toll officially surpassed 1 million this week.

  • The Ipsos poll found respondents are more concerned about spreading COVID-19 to others or being inconvenienced by public health restrictions than getting sick or dying — regardless of vaccination status.
  • “There appears to be a relatively small amount of Americans who are feeling any personal sense of risk,” Jackson said.

Axios on facebookAxios on twitterAxios on linkedinAxios on email

Covid Failure

What we did didn’t work. Let’s learn from that.

Authors: PETER VAN BUREN MAY 16, 2022| The American Conservative

We were swindled, fooled, bamboozled, and lied to during the pandemic. The public-health establishment misled the American people about the value of masking, closures, and social distancing. No one has accepted blame. Understanding how badly we failed is not only an inevitable part of the “told you so” process, but, more importantly, a lesson for next time. Just ask the Swedes.

Sweden had zero excess deaths associated with Covid-19. The U.S. had the most excess deaths of all nations. New York had more than Florida. That’s the whole story right there in a handful of words.

Let’s unpack it.

The key element of misdirection in the American swindle was case counts, those running numbers on screens telling us how many Americans had tested positive for Covid. If you’re curious, it looks like some 60 percent of us have had Covid at some point, with most of us experiencing mild or no symptoms.

How high the case numbers went in your neck of the woods depended a lot on the amount of testing taking place. More testing meant more “cases.” For me, when I had a very mild set of symptoms all clearly in line with Covid, I never even bothered to test. Like most people, I just sat around the house for a few days until I got better. My spouse, who had no symptoms, never got tested, either. Neither of us were included in the ever-growing case counts that dominated the headlines for years.

Not that it matters. The case count tells us very little. Hospitalization totals are useful for managing caseload, but often are indicative of protocols like testing patients upon entry to the hospital. Many hospital treatments changed, too. Initially, many Covid-positive people were hospitalized and put on respirators. Before long, many doctors realized infections associated with long-term respirator use were killing people, too.

Eventually, hospitalization numbers went down. That stat, too, only told you so much. Since Covid proved fatal primarily to the elderly, many hospitalizations began with something else only to end with Covid. My own father suffered a blinding, massive stroke, went into hospital, and caught Covid there, to officially die of respiratory failure. I’m not sure if he counted as a Covid death or not.

Now the bad news: Modern medicine cannot cure death. Everybody dies. Most Americans who don’t die earlier in life in accidents typically die after the age of 77. In 2020, heart disease and cancer each killed about double the number of people that Covid did.

The only statistic that really matters then when talking about the roughly two years of the pandemic is “excess deaths,” deaths beyond the usual couple of million that occur every year.

Sweden had zero excess deaths. The U.S. had the most excess deaths of all nations. New York had more than Florida.

Sweden did very little in terms of halting work and school, or forcing masking and social distancing. The U.S. did quite a bit more. The U.S. states known for their Covid “efforts,” particularly New York, had excess deaths worse than or similar to do-little Florida. These states expended an awful lot of effort and angst, and suffered great collateral damage (addiction, suicide, unemployment, social unrest, failing grades), for very little benefit.

And we were lied to by the Covidians. In July 2020, the New York Times stated Sweden’s “decision to carry on in the face of the pandemic has yielded a surge of deaths without sparing its economy from damage. Sweden’s grim result—more death, and nearly equal economic damage—suggests that the supposed choice between lives and paychecks is a false one: failure to impose social distancing can cost lives and jobs at the same time.”

Tsktsk, said the media. And they’re still saying it. Despite Florida having 148 excess deaths per 100,000 to New York’s 248, Politico‘s May 1, 2022, headline read: “Florida lost 70,000 people to Covid. It’s still not prepared for the next wave.”

Much as Florida did, Sweden allowed restaurants, gyms, shops, and most schools to stay open. People went to work; some voluntarily masked, some not. Their decision stood in stark contrast to the U.S., where, by April 2020, the CDC recommended draconian lockdowns, throwing millions out of work and school.

The U.S. is the only major Western nation that still demands a negative Covid test for entry, including for its own citizens. The U.S. is the only nation where every Covid therapeutic, such as new anti-viral drugs that lessen the severity of a positive case, is filtered through the lens of partisan politics.

In addition to leaving our economy in shambles, America’s Covid strategy apparently did not consider the age disparity in excess deaths. Globally, most Covid deaths occured among persons age 77 and older. People exposed to Covid in their 70s have twice the mortality rate of those exposed in their 60s, and 3,000 times that of Covid-exposed children. But everyone was made to wear a mask as though everyone were at equal risk of Covid, and without solid evidence that mask mandates significantly lower viral spread in the community.

The data were clear in China from the early days of the pandemic. Death rates for elderly Chinese in the early days of the pandemic, who were not social distancing, and elderly Americans, who were social distancing, were very similar. Swedish intensive-care-admission rates showed sharp declines after early pandemic peaks despite a lack of state-imposed shutdowns.

Age-specific solutions were needed for a virus with age-specific effects. We ignored or overlooked the data. We are paying for that mistake now. Savings lives or saving the economy? Both, please. Ask the Swedes.

America’s pandemic response was wrong across the board. Its failure is attributable in part to red-blue politics and a pathetic desire for control by Democratic governors.

It was also exacerbated by Americans’ underlying health, which is worse than most other developed countries. Our underlying health woes are exacerbated by income inequality and high rates of poverty, and maddening levels of obesity, diabetes, and “deaths of despair,” especially among the underclass. Black Americans were hit harder by Covid than white Americans. The poor were hit harder than the well-to-do.

Whatever we did, whether we masked or locked down or stayed open and maskless, we still would have suffered because of these underlying issues. Fixing the next pandemic means fixing America first.

1 Million COVID Deaths: Here’s The Real Reason Why More People Died From COVID In The United States Than Every Other Country

Authors:  Alicia Powe May 14, 2022 Gateway Pundit

After two years of inundating the public with propaganda and fear surrounding COVID-19, the mainstream media and the federal government turned the page on the manufactured pandemic.

While coronavirus fades from headlines, the number of people purportedly dying from the virus continues to climb.

Over one million Americans have now purportedly died from COVID-19, according to figures published by Our World In Data.

More people died from COVID-19 in the United States than any other developed nation worldwide.

COVID-19 was the third leading cause of death in the U.S. in 2021, following heart disease and cancer, according to new data from the Centers for Disease Control and Prevention.

s the corporate press and the Biden administration raise alarm this week over the high COVID-19 death toll in America, they continue to ignore the elephant in the room.

Americans infected with COVID-19 are not actually dying from the virus, doctors warn.

The United States has the highest COVID death rate in the world because COVID patients are dying in hospitals under the CDC guidelines, Dr. Ben Marble MD., primary care doctor, told The Gateway Pundit in an exclusive interview.

“There’s no question without a doubt that the reason why the United States has the number one highest death toll from COVID is because of all these policies,” he said.

Health practitioners in hospitals “stopped administering all the drugs that work — they stopped the Ivermectin, they stopped the Hydroxychloroquine, and they start protocols that don’t work. They started bad drugs that don’t work like Remdesivir which causes kidney failure in at least 20 percent of people who get it. It’s a bad drug that should be pulled off the market. They didn’t want us doing early treatment — that didn’t make any sense. Early treatment is the cornerstone of all good medicine. Treat everything as early as possible.

In addition to providing every COVID-19 patient with deadly Remdesivir, doctors are required to intubate patients as their health depletes from the drug notorious for causing renal failure.

“They basically hold the patient hostage,” Marble explained. “They won’t let the patient have any visitors. If they complain incessantly, then they sedate the patient. Once you’re sedated, you get intubated. Intubated patients end up dying overnight.”

COVID-19 mandates may have waned, but people are still getting killed in the hospitals and no one is stopping it, Marble fumed.

“All the hospitals in America are still following these protocols that we know don’t work. That’s why the average person doesn’t want to have to go to the hospital right now. A lot of us call them ‘hellspitals’ because of this. They are doing all the wrong things for patients with these failed policies and we all know they’ve failed. The government bureaucracy is stuck on stupid.

After practicing medicine in the emergency for over 15 years, Dr. Marble refused to follow along with new lethal CDC protocols, resigned and founded MyFreeDoctor.Com, where his team provides free treatment for COVID patients.

“I realized Fauci, the CDC, FDA — and everything they were recommending was wrong, the masks, social distancing, the shutdowns — all those things did not work to stop COVID. All they did was make everything worse. It’s proven by the fact that America has the highest death toll in the world,” he said. “We know these are failed policies and we have to quit following them unless we want the highest death toll. Just ignore everything the federal government says and do the opposite.

As information showcasing the inefficacy of COVID vaccines began to permeate the mainstream and Americans became increasingly fed up with the lies, the Biden administration and its propaganda arm diverted the public’s attention to the war with Russia and Ukraine and COVID-19 mandates waned.

“Certainly, they are trying to change the narrative because a lot of the doctors like myself have blown the whistle on this scam and spoken out against Fauci and friends – who are against early treatment while favoring these vaccines,” Dr. Marble said. “The vaccines — normally when an FDA-approved drug harms over 50 people, the normal standard is to pull it off the market at 50 deaths. Yet, according to the Pfizer data that was just revealed, there were over 1,200 deaths in the Pfizer study — there were only like 39,000 people in it. 1200 of them died. The vaccine should have been pulled off the market over a year ago. We’ve blown the whistle on that so the average person doesn’t want to take the vaccine anymore, so they decided, ‘Hey let’s switch the narrative to Ukraine.’”

“Fauci is the architect of all of this.  He is the greatest mass murderer in all the world because he financed the gain of function research. He paid for the creation of COVID-19. That virus is a manmade virus, we know that for a fact. It’s a combination of Sars 1 with parts of HIV, parts of Respiratory Syncytial Virus and mixed the spike protein in there with it. They tried to make it as dangerous as they could to justify the need for fake vaccine gene-editing technology.  The sad thing is they’ve already vaccinated 5 billion people they are going to get to 6 billion by the end of this year. Go to my free, that’s where we will try to deliver treatment for free and help as many people as we can. That’s the best we can try to do to help people.”

Washington-based Physician Assistant Scott Miller is unable to practice medicine for the foreseeable future after saving the lives of more than 2,500 Covid patients who were refused adequate care by their doctors or the local hospitals.

It’s clear the CDC has been weaponized against the American public, and they, along with other federal and state government agencies, are complicit in the deaths of now over 1 million Americans and the disruption or destruction of the lives of tens and tens of millions of families across our United States, Miller told The Gateway Pundit in an exclusive interview.

Obviously, Remdesivir is deadly, it shuts down the kidneys. When they were doing the Ebola studies in 2015, Remdesivir was so toxic, so damaging to the organs, it had to be stopped. If they weren’t dying from Ebola, Remdesivir was killing them. Even the WHO deemed it toxic and ineffective and recommended against its use over 16 months ago. Meanwhile, the vaccines are shutting off people’s immune pathways ability to recognize foreign invaders. It’s silencing areas of our immune system that are critical to recognizing a threat. Hell, we could spend a couple hours just talking about antibody-dependent enhancement.”

 Like Miller, medical practitioners who expose the murderous protocols in the hospitals or conspire to save lives continue to be smeared and revoked of their medical licenses.

“The doctors don’t profit, but if they don’t comply with the new protocol, a formal internal investigation would be opened by the hospital as to why they chose to break rank or they would simply be fired. They put so much pressure on the doctors to conform and follow “evidence-based medicine,” Miller said.  “For someone like me, who wasn’t an employee and didn’t work in a clinic, the consequences are far more devastating. I owned my own Pediatrics practice. When the Washington Medical Commission started opening investigations on me, a sane person would have capitulated and stopped speaking out, stopped helping people, and refused to treat them. When they suspended my license, I didn’t just lose my job, I lost the entirety of everything that I worked and sacrificed to build over the last 15 years. I  had hundreds of reasons to not speak out, to not provide care to those who have been abandoned by the medical system, but I had several thousand better reasons to not only share the truth about what is really going on, but to also treat everyone that reached out to me in need.

“The witch hunts by these medical boards across the United States — threatening the livelihood of any provider that chooses to share actual facts, to share truth, to share information that can save people’s lives — has not been addressed nearly enough. If you are a medical provider in the United States, a provider that actually knows the science, your right to free speech hasn’t just been trampled, beaten, stabbed, gang-raped, and then shot, left in the middle of the road for everyone else to see, as a symbol of what happens if you dare speak out against our new normal,” he continued. “If I was going to share how I feel about my experiences over the last 2 years —  on an almost daily basis tasked with trying to figure out how to preserve the lives of people that have been ignored or actively harmed by our medical system — it would not be fit to print. “

Deaths from COVID begin to rise again

Authors: Tina Reed Kavya Beheraj May 12, 2022 Axios

Deaths from COVID-19 are on the rise again after several weeks of upward ticking case rates sparked by Omicron variants.

Driving the news: The U.S. averaged roughly 365 daily deaths, up 7% from about 342 two weeks ago. That’s still a fraction of where things stood several months ago when the daily average was in the thousands.

Yes, but: The increase in deaths comes after several weeks of declines. While increasingly transmissible Omicron variants have generally not appeared to cause more serious illness, some people are still dying.

  • Waning immunity and low booster uptake has also meant a growing share of the deaths are among the vaccinated, officials warn.

By the numbers: There were roughly 77,000 new daily cases over the last week, up 44% from about 53,000 two weeks ago.

  • Reported cases rates remained highest in the Northeast, with Rhode Island marking 67.3 new cases per 100,000 people, up from 38.4 per 100,000 two weeks ago.
  • Rhode Island, Massachusetts, Vermont and Maine were the four states with 50 or more new cases per 100,000 people over the last two weeks.
  • On the flip side, 15 states reported having 10 or fewer new cases per 100,00o people over the same time, including Alabama, Arizona, Arkansas, Georgia, Idaho, Louisiana Mississippi, Montana, Nebraska, Oklahoma, Pennsylvania, Texas, South Carolina, South Dakota and Wyoming.
  • Five states reported declines in COVID case rates, including Montana, which reported 5.2 new cases per 100,000 people, down from 5.5 per 100,000 two weeks ago. Alaska, Colorado, Pennsylvania and Washington also reported dips. D.C. also reported a drop, however, the CDC said Wednesday the District had a two-week lapse in reporting, Axios’ Chelsea Cirruzzo reports.

Reality check: As we’ve warned before, the data on new cases are getting less reliable as the public testing infrastructure continues to wind down and home test results are less likely to be reported to officials.

  • But it still offers a window into the broad trends of COVID spread in the states.

The bottom line: As variants spread, warm weather returns and more people let their guard down, cases are on the rise. While numbers appear far better than what they once were, officials warn the virus isn’t done with us yet.

COVID-19: Omicron variant did not wipe out Delta, it could return

While the Delta virus wiped out the variants that preceded it, Omicron has not eliminated Delta, according to a new study from Israel’s Ben-Gurion University of the Negev.

Authors:  JUDY SIEGEL-ITZKOVICH Published: MAY 2, 2022 

Don’t throw away your unused face masks yet. COVID-19’s Omicron variants may burn themselves out in the next couple of months, and the Delta variant might re-emerge, researchers at Beersheba’s Ben-Gurion University of the Negev (BGU) suggest in a new scientific paper.

Their findings were just published in the peer-reviewed journal Science of the Total Environment under the title “Managing an evolving pandemic: Cryptic circulation of the Delta variant during the Omicron rise.”

The first new coronavirus to appear at the end of 2019 was Alpha, followed by Beta (first detected in South Africa); Gamma (first detected in Brazil); Delta (that revealed itself in India); and the more-infectious but milder Omicron, which has developed a variety of sub-variants and spread all over the world.

While the Delta variant wiped out the variants that preceded it, Omicron has not eliminated Delta, according to Prof. Ariel Kushmaro and Dr. Karin Yaniv, who just received her doctorate in the field.

The lab team has developed sensitive arrays that can differentiate variants from each other in wastewater, which continues to give indications of where the coronavirus is active, even when PCR and rapid testing of people declines.

Kushmaro, who earned his advance degrees in molecular microbiology and biotechnology at Tel Aviv University, trained as postdoctoral fellow at the Hebrew University and at Harvard. He arrived at BGU 21 years ago and established a lab at the School of Sustainability and Climate Change and the Goldstein-Goren Department of Biotechnology Engineering.

The lab specializes in wastewater microbiology, marine microbial ecology and antimicrobial activity of varies microorganisms as well as biological treatment of industrial wastewater.

His team monitored Beersheba’s sewage from December 2021 to January 2022 and noticed this disturbing interaction between the Omicron and Delta variants.

They also built a model with Granek that predicts that Omicron is burning itself out while Delta is just waiting to pounce on the population again.

“SARS-CoV-2 continued circulation results in mutations and the emergence of various variants. Until now, whenever a new, dominant, variant appeared, it overpowered its predecessor after a short parallel period,” they wrote.

“Despite vaccination efforts in Israel, with a large portion of the population being vaccinated between the first to fourth dose of vaccine and despite high infection rates by previous variants, the Omicron variant had now rooted itself in Israel.”

The latest variant of concern, Omicron, is spreading swiftly around the world with record morbidity reports, wrote the authors. “Unlike the Delta variant, previously considered to be the main variant of concern in most countries, including Israel, the dynamics of the Omicron variant showed different characteristics.”

If their prediction comes to pass, its circulation may result in the reemergence of a Delta morbidity wave or in the possible generation of a new threatening variant, they wrote.

With the expected significant decline in morbidity from all the recovered Omicron cases, the Israeli government and the Health Ministry have eliminated most restrictions. “In the meantime, the Delta, which is still circulating in a population with waning immunity and under fewer restrictions, may re-emerge in larger numbers or even produce a new, different variant to generate infections in Israel.”

In any case, the team recommended wastewater-based epidemiology as a “convenient and representative tool for pandemic containment.

“Of course, there are a lot of factors involved, but our model indicates there could be another outbreak of Delta or another coronavirus variant this summer,” warned Kushmaro, who was assisted by Dr. Eden Ozer and Marilou Shagan at BGU and Dr. Yossi Paitan from Ilex Labs. 

Trends and associated factors for Covid-19 hospitalisation and fatality risk in 2.3 million adults in England

Authors: T. BeaneyA. L. NevesA. AlboksmatyH. AshrafianK. FlottA. FowlerJ. R. Benger

P. AylinS. ElkinA. Darzi & J. Clarke  Nature Communications volume 13, Article number: 2356 (2022) 


The Covid-19 mortality rate varies between countries and over time but the extent to which this is explained by the underlying risk in those infected is unclear. Using data on all adults in England with a positive Covid-19 test between 1st October 2020 and 30th April 2021 linked to clinical records, we examined trends and risk factors for hospital admission and mortality. Of 2,311,282 people included in the study, 164,046 (7.1%) were admitted and 53,156 (2.3%) died within 28 days of a positive Covid-19 test. We found significant variation in the case hospitalisation and mortality risk over time, which remained after accounting for the underlying risk of those infected. Older age groups, males, those resident in areas of greater socioeconomic deprivation, and those with obesity had higher odds of admission and death. People with severe mental illness and learning disability had the highest odds of admission and death. Our findings highlight both the role of external factors in Covid-19 admission and mortality risk and the need for more proactive care in the most vulnerable groups.


The Covid-19 case fatality ratio (CFR) varies widely between countries1 and definitions of mortality differ across the world, making comparisons challenging2. In England, the most widely reported measure is mortality within 28 days of a positive test3. Up to 21 September 2021, 539,921 hospital admissions and 118,846 deaths have occurred in England, out of a total of 6,398,633 cases, giving a crude case hospitalisation ratio (CHR) of 8.4% and a CFR of 1.9%4. Previous epidemiological studies have shown variation in the CFR over time1,5, but without individual level data, it is unclear the extent to which this variation is accounted for by differences in the risk of those infected.

Many risk factors for death from Covid-19 have been characterised, such as increased age, male gender, and obesity6. Several long-term conditions are strongly linked to a higher mortality risk; in England, this led to the early adoption of a ‘clinically extremely vulnerable’ (CEV) status for those deemed to be at highest risk, subsequently advised to isolate to reduce transmission7. Previous studies have focussed on the first wave of the pandemic in the first half of 2020, which may not be representative of subsequent pandemic waves, particularly given advances in the management of Covid-19 patients and the emergence of new variants8. Furthermore, to our knowledge, no study to date has used data with national coverage, including all laboratory-confirmed Covid-19 test results linked to electronic health record (EHR) data.

The main aim of this paper is to describe the changing trends in the Covid-19 case hospitalisation risk (CHR) and case fatality risk (CFR) in England, during the ‘second wave’ of the pandemic (i.e., from 1st October 2020 to 30th April 2021). The secondary aims are to identify patient characteristics associated with hospitalisation and mortality risk; and to evaluate whether residual unexplained variation in the CHR and CFR remains after accounting for differences in the underlying risk factors of those infected.


From 1st October 2020 to 30th April 2021, data were available for 2,433,768 individuals with a positive Covid-19 test result in England. Data for 34,317 (1.4%) participants with a positive test result could not be linked to either primary or secondary care records and were excluded. Care home residents accounted for 3.7% of the total (n = 88,169) and were excluded from further analyses, resulting in a total population of 2,311,282.

Characteristics of the study population are provided in Table 1. The mean (SD) age of participants was 44.3 (17.1) years, with 43.6% under 40 years. The majority were female (54.3%) and of White ethnicity (72.8%). There were relatively higher proportions from more deprived deciles of IMD, with 56.7% in the bottom five deciles. Similar proportions of subjects with a healthy weight (28.4%), overweight (28.1%) or obese (26.1%) were observed, and only 3.4% were underweight. 16.3% were current smokers and 8.3% were designated as CEV. Chronic respiratory disease (21.2%), hypertension (15.0%) and diabetes (8.6%) were the three most prevalent chronic conditions in the population.Table 1 Characteristics of the study population with hospital admissions and deaths within 28 days (N = 2,311,282).Full size table

Case hospitalisation and fatality risk over time

Of the study population, 164,046 people were admitted to hospital at least once within 28 days of a positive test, giving a crude CHR of 7.1% over the seven-month period. 53,156 deaths occurred within 28 days of a positive test, giving a crude CFR of 2.3%. Of these, 49,172 (92.5%) had Covid-19 as a cause of death on the death certificate. There were significant differences over time in both the CHR and CFR (Supplementary Fig. 1). The age distribution of people with a positive test varied over time, with the highest proportions of all infection in people aged 60 years and above infected in November 2020 and January 2021 (Supplementary Table 1). Within all age groups, a similar pattern of change in the CHR and CFR over time was seen, with risk peaking in December 2020–February 2021 (Supplementary Tables 2 and 3, respectively, and Supplementary Fig. 2).

Factors associated with 28-day mortality and hospitalisation risk

Multiple imputation was used to impute missing data for 381,283 people. Multivariable logistic regression models were constructed for each outcome adjusting for all patient level covariates (model 2). Calibration plots indicated adequate calibration (Supplementary Figs. 3 and 4). Results for hospital admissions and mortality are presented in Figs. 1 and 2 (also Supplementary Tables 4 and 5). Males had 41% higher adjusted odds of admission (95% CI: 1.39–1.42) and 62% higher adjusted odds of mortality (95% CI: 1.58–1.65) compared to females. People of all four non-White ethnicities had higher odds of admission, and those of Asian and Black ethnicities also had higher odds of mortality compared to those of White ethnicity. People living in less deprived areas had lower odds of both admission and mortality compared to those in the most deprived areas. Compared to people of a healthy weight, those underweight had 10% higher odds of admission (95% CI: 1.05–1.14) and 99% higher odds of death (95% CI: 1.87–2.11). People who were overweight had a 24% increase in odds of admission (95% CI: 1.22–1.26) but 20% lower odds of death (95% CI: 0.77-0.82); those who were obese had 93% higher odds of admission (1.90–1.97) and 4% increased odds of death (95% CI: 1.01–1.07). Current smokers had lower odds of admission compared to non-smokers but an increase in the odds of death after adjustment.

figure 1
Fig. 1: Adjusted odds ratios for emergency hospital admission within 28 days of positive Covid-19 test.
figure 2
Fig. 2: Adjusted odds ratios for death within 28 days of positive Covid-19 test.

All chronic conditions included were strongly associated with an increase in odds of admission and death, except for dementia, which was associated with 6% lower odds of admission. People identified as CEV had 85% higher odds of being admitted to hospital (95% CI: 1.83–1.88) but 12% lower odds of death (95% CI: 0.86–0.90) after full adjustment. In a sub-analysis adjusting CEV status for age, time (and their interaction), sex, ethnicity, and deprivation only, odds of admission were significantly higher (aOR 2.62, 95% CI: 2.58–2.65) as were odds of death (aOR 1.52, 95% CI: 1.49–1.55).

A sensitivity analysis of the 1,929,999 complete cases showed similar estimates to the fully adjusted model (Supplementary Tables 6 and 7).

CHR and CFR over time

A significant association remained with time for both CHR and CFR models after adjusting for all patient covariates (p < 0.0001 in each model from likelihood ratio tests). The predicted CHR and CFR from the fully adjusted models are plotted for the whole population (Supplementary Fig. 5) and by age category in Fig. 3, showing that a significant time-varying relationship remained after adjustment. The relative change in predicted CHR and CFR from the baseline predicted risk in the first full week of October is shown in Fig. 4 (and Supplementary Figs. 6 and 7). The CFR increased across all age groups, peaking between late December 2020 to early February 2021in different age groups before declining towards April. A smaller relative increase in hospitalisation risk was seen across age groups. In most age groups, CHR peaked in January, except in the 18–39 age group, which continued to increase throughout the study period. After adjustment, the trends in absolute mortality and hospitalisation risk in each age group were similar to those in the unadjusted analyses (Fig. 4 and Supplementary Fig. 2) indicating that the distributions of risk factors of those infected within age groups did not change significantly over time.

figure 3
Fig. 3: 28-day case hospitalisation risk and fatality risk over time in people with Covid-19.
figure 4
Fig. 4: Relative change in 28-day case hospitalisation risk and fatality risk over time in people with Covid-19.


In this retrospective cohort study including all adults in England with a positive Covid-19 test result, there was significant variation in the 28-day CHR and CFR by age group and over time, which remained after accounting for individual risk. Demographics and chronic conditions were strongly associated with hospitalisation and death.

Variation in CHR and CFR over time

Across the whole study population, CHR and CFR varied over time from 1st October 2020 to 30th April 2021. This was partially explained by the changing age distributions of those infected, but significant variation remained after adjustment. Within age groups, absolute differences in the CHR and CFR over time were greatest in older age groups, reflecting higher baseline risk, but the relative risk varied significantly across all groups. Historically, there is a strong seasonal component to mortality in England, with figures indicating 16.8% higher mortality in winter months compared to summer months9. An increased incidence of respiratory diseases, including influenza, are one of the main drivers of increased winter mortality, and the 28-day mortality metric used in this study includes deaths from non-Covid-19 causes. However, with influenza rates at lower levels than previous years, it is unlikely the variation in CFR over time can be explained by the incidence of other infectious diseases alone10.

Strain on the health system may also contribute to the patterns seen, with Covid-19 bed occupancy and critical care occupancy in England peaking in January 2021, associated with a lower proportion of patients seen in Accident & Emergency departments within 4 hours than in November 2020 and February 20214,11. Larger relative increases were seen in the CFR compared to the CHR, which may indicate a health system reaching full capacity and struggling to meet demand. A previous UK study of patients admitted to hospital with Covid-19 found a fall in mortality from March to July 2020, a time over which bed occupancy fell and evidence for new treatments, such as dexamethasone, became available, with similar findings from a US cohort between March and September 202012,13. Changes to care delivery at an organisational level may also have an impact, with triage models for Covid-19 patients on the national 111 urgent care service varying between services and over time14. The Alpha variant became the dominant Covid-19 strain in England in December 2020, and has been associated with a 64% increase in 28-day mortality compared to prior variants, which may explain part of the rise in the CHR and CFR15.

Declines in the CHR and CFR from January 2021 onwards are likely to be explained at least partially by the development of immunity, both through natural infection and by the vaccination programme, which was implemented from 8th December 2020 in England for the highest risk cohorts16. By February 2021, over 80% of over 80s had been vaccinated in most regions of the UK, with similar vaccine coverage in the 70–79 year age group by mid-February and in the 60–69 year age group by mid-March (Supplementary Figs. 810)17. However, our study population includes people with a positive Covid-19 test, who are more likely to be unvaccinated than the general population; population vaccine coverage is, therefore, unlikely to be representative of our study population and estimates could not be incorporated robustly into our modelling. Declines in CFR and CHR are most marked in older age groups, who were the first groups eligible for vaccination. However, declines in mortality are seen across all age groups, including the 18–39 year group, many of whom would not have been eligible for vaccination, suggesting vaccination does not fully account for the declines observed. Availability of new treatments may also explain the falls in mortality, with the RECOVERY trial demonstrating the benefit of tocilizumab published in February 2021, but is unlikely to explain the fall in admissions8,18.

Factors associated with hospitalisation and mortality

The findings of a higher risk of mortality in males, people of Asian and Black ethnic backgrounds, and those living in more deprived areas are consistent with a previous UK cohort and confirmed in our study, including an increased risk of admission6. People who were underweight were more likely to be admitted and had significantly higher risk of death, which might be partly accounted for by unmeasured associated conditions, such as frailty. People who were overweight and obese had higher risk of admission than those of a healthy weight, but mortality risk was lower in those overweight, which may indicate higher perceived risk amongst clinicians and a lower threshold for admission.

People identified as CEV were significantly more likely to be admitted but were found to have significantly lower mortality, after adjusting for other risk factors including co-morbidities. However, in partially adjusted models not including BMI, smoking, or clinical co-morbidities, those identified as CEV had significantly higher odds of death. Taken together, these findings indicate a lower threshold for clinical assessment and/or admission and escalation in CEV patients with a protective effect on mortality. All twelve included clinical co-morbidities were associated with significant increases in the odds of mortality and admission. Severe mental illness and learning disability had the strongest associations with mortality and admission, highlighting a need for more proactive care in these groups and more research into the reasons for mortality differences19. Those with dementia had significantly increased odds of mortality but were less likely to be admitted, suggesting they are more likely to receive care at home, although the cohort did not include those living in care homes and so will not represent the full population of those with dementia.

The emergence of the Delta and Omicron variants have shown the potential of Covid-19 to vary in both transmissibility and pathogenicity over time. In England, December 2021–January 2022 saw the highest case numbers but without the resulting number of hospitalisations and deaths associated with earlier variants and before widespread vaccination4. Despite the emergence of new variants, the findings of our study are relevant in highlighting that the risk of mortality was independent of an extensive panel of clinical and demographic factors in the winter of 2020/21, pointing to the role of wider strain on the health system as an important feature in outcomes in people with Covid-19. While the Omicron variant has contributed to an increase in hospitalisations and emergency department presentations in England and elsewhere, its impact on staff absence has been particularly marked. At the peak of the Omicron wave in early January 2022, almost 50,000 NHS staff were absent due to Covid-19, almost a five-fold increase from the end of November 202120,21,22. Ensuring health systems possess the resilience to weather the dual shocks of an increased demand for care and decreased capacity to provide it, without adversely affecting the quality and safety of healthcare, is an ongoing area of concern.

Strengths and limitations

A strength of this study is the inclusion of routine national laboratory data for positive Covid-19 test results in adults in England with only 1.5% unable to be linked to EHR data, and as a result, has lower risk of sampling bias23. To our knowledge, this is the largest such study including individual level data at a national level. Previous studies in England on predictors of mortality are reported on a smaller cohort of patients with 40% national coverage6. The use of multiple imputation assumes that data are missing at random, and we cannot rule out non-random missing patterns, particularly for data on ethnicity and deprivation, where more marginalised groups are less likely to be registered in the primary care record. However, sensitivity analyses showed inferences were similar between the complete case analysis and imputed results, suggesting limited impact of the missing data on model estimates. Associations with risk factors may also be confounded by differential uptake of vaccinations among risk groups; for example, if those with co-morbidities or defined as CEV were more likely to be vaccinated, the odds ratios for hospitalisation and death may be under-estimated.

Data represented here include only those who died within 28 days of a positive test result, in line with estimates reported by PHE. Deaths mentioning Covid-19 on a death certificate are an alternative metric used widely in many countries as recommended by the World Health Organisation24 and have tended to give a larger estimate of deaths in England, due to those attributable to Covid-19 after 28 days4. Over 90% of deaths within 28 days in our study also had Covid-19 as a cause of death on the death certificate, but we did not have corresponding data for those cases recorded on a death certificate without a positive Covid-19 test. The associations found in our study might be different if using deaths recorded on death certificates, rather than deaths within 28 days of a positive Covid-19 test, particularly if there were changes to death certification practices over time.

Through use of linked EHR data, we were able to incorporate detailed medical factors for the study cohort. However, we were unable to explore the relationship with external factors such as Covid-19 variants. Geographical and time-varying system factors, such as proximity to a hospital and hospital capacity are likely to impact on a person’s health-seeking behaviour. Our study included people living in the community and given patients in England may attend any hospital, and the size of hospital markets vary considerably across the country, we could not reliably model the impact of nearby hospital bed availability at an individual level. However, our modelling showed only minimal residual variation accounted for by CCG level clustering (intraclass correlation coefficient <1%), suggesting these additional factors would have minimal impact on the findings. Access to testing may also impact the probability of having a positive test. Positivity rates in England peaked on 31st December 2020 at 18.3% and fell to 1.7% by 1st April 20214, but the extent to which this reflects increased incidence or a lack of test availability is uncertain. It is possible that if testing were limited during the peak in cases in December 2020–January 2021, those with more symptomatic disease may have been more likely to receive a test, compared to those who were asymptomatic or with mild symptoms. This may in turn lead to an apparent increase in risk of mortality due to changes in the severity of illness of those testing positive, rather than the severity of disease within the community as a whole. Furthermore, access to testing may be driven by sociodemographic factors, and the finding of lower hospitalisation and mortality risk in less deprived areas could reflect better availability of testing. Exploring mortality risk in patients admitted to hospital or to intensive care units and whether this changed over time was outside the scope of the current study but is an area for further research.

The risk of hospitalisation and death from Covid-19 varied significantly over time from October 2020 to April 2021 in all age groups, independent of the underlying risk in those infected. Time-varying risks should be considered by researchers and policymakers in assessing the risks of hospitalisation and mortality from Covid-19. People with severe mental illness and learning disability were amongst those with the highest odds of both admission and mortality, indicating the need for proactive care in these groups.


The work was conducted as part of a wider service evaluation, approved by Imperial College Healthcare Trust on December 3rd 2020. Data access was approved by the Independent Group Advising on the Release of Data (IGARD; DARS-NIC-421524-R0Y3P) on April 15th 2021.

Study design and population

We conducted a retrospective cohort study including all adults (≥18 years) resident in England with a positive Covid-19 test result (polymerase chain reaction or lateral flow tests) from 1st October 2020 to 30th April 2021, excluding people resident in care homes. Study participants were followed-up for 28 days from the date of a first positive test. The two primary outcomes were (i) one or more emergency hospital admissions and (ii) death from any cause, each within 28 days from the date of the positive test.

Data sources and data processing

Several datasets were linked for this study and provided by NHS Digital as part of an evaluation of the NHS England Covid Oximetry @home programme25. Covid-19 testing data was sourced from the Public Health England (PHE) Second Generation Surveillance System26, the national laboratory reporting system for positive Covid-19 tests, covering the period from 1st October 2020 to 30th April 2021. Primary care data came from the General Practice Extraction Service (GPES) Data for Pandemic Planning and Research (GDPPR)27. CEV status was linked to GDPPR from the Shielded Patient List (see Supplementary Methods)28. Data on hospital admissions came from Hospital Episode Statistics (HES) data set up to 31st May 2021, linked to Office for National Statistics (ONS) data on death registrations up to 5th July 2021. Datasets were linked using a de-identified NHS patient ID. Participants who could not be linked from testing data to at least one of GDPPR or HES were excluded.

Patient demographics were derived from GDPPR, or  where missing, from HES. Lower layer super output area (LSOA) of residence was linked to indices of relative deprivation using deciles of Index of Multiple Deprivation (IMD) 201929. Residence in a care home, CEV status, body mass index (BMI), and smoking status were derived from GDPPR only. BMI was categorised as underweight (<18.5 kg/m2), healthy weight (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2) and obese (≥30.0 kg/m2). Chronic conditions were extracted from GDPPR based on Systematised Nomenclature of Medicine Clinical Terms (SNOMED-CT) codes pertaining to relevant diagnosis code clusters. Only codes recorded prior to the date of a positive Covid-19 test were included, to exclude any diagnoses following Covid-19 infection. Where the latest code indicated resolution of a condition, the diagnosis was excluded for that individual. Further details on data curation are given in the Supplementary Methods.

Statistical analysis

Patients were followed from date of first positive Covid-19 test to emergency hospital admission or death within 28 days. Mixed effects logistic regression was conducted for each outcome, with a two-level hierarchical model incorporating Clinical Commissioning Group (CCG, of which there are 106 in England) of residence as a random intercept. Time, represented by the week of Covid-19 test, was modelled as a restricted cubic spline with five knots placed at equally spaced percentiles30. Two models were run for each outcome:

  1. 1.Model 1: incorporating age category and time splines along with their interaction.
  2. 2.Model 2: incorporating age category and time splines along with their interaction and including all additional patient level covariates: sex, ethnicity, IMD decile, BMI category, CEV status, smoking status, and presence of chronic conditions.

For model 2, multiple imputation using chained equations was used to impute missing values of covariates, under the assumption that values were missing at random. All variables included in the analysis model were included in the imputation model31. Fifteen imputations were created, with a burn-in of 10 iterations which gave adequate precision and convergence, respectively (Supplementary Methods). A sensitivity analysis was performed using complete cases only. Calibration was assessed using plots of predicted against observed probabilities for each decile of predicted probability.

For each outcome, the predicted probability of the outcome was computed within each age group and study week stratum to calculate age- and time-specific case hospitalisation risk (CHR) and case fatality risk (CFR). These were calculated using the fixed portion of the model (assuming zero random effects). The relative changes in the CHR and CFR over time were calculated as the predicted probability in each week relative to the week of 5th–11th October 2020 in each age group. In adjusted models (model 2), other model covariates were set to the population mean (or proportion for categorical variables) within each age group. For CEV status, an additional sub-analysis was conducted adjusting only for the age category and time splines (and their interaction), sex, ethnicity, and IMD decile. Further details of the statistical methods are given in Supplementary Methods.

Analyses were conducted in the Big Data and Analytics Unit Secure Environment, Imperial College, using Python version 3.9.5, Pandas version 1.2.3, and Stata version 17.0 (StataCorp).

Data availability

The patient level data used in this study are not publicly available but are available to applicants meeting certain criteria through application of a Data Access Request Service (DARS) and approval from the Independent Group Advising on the Release of Data. Further information is given below:

Code availability

The SNOMED terms used in defining chronic conditions are available in our GitHub repository: Further analysis codes are available on request to the corresponding author.


  1. Sorci, G., Faivre, B. & Morand, S. Explaining among-country variation in COVID-19 case fatality rate. Sci. Rep. 10, 18909 (2020).ADS Article Google Scholar 
  2. Beaney, T. et al. Excess mortality: The gold standard in measuring the impact of COVID-19 worldwide? J. R. Soc. Med. 113, 329–334 (2020).Article Google Scholar 
  3. Public Health England. Technical summary: Public Health England data series on deaths in people with COVID-19. 1–15 (2020).
  4. UK Government. Coronavirus (COVID-19) in the UK. (2021).
  5. Hasan, M. N. et al. The global case-fatality rate of COVID-19 has been declining since May 2020. Am. J. Trop. Med. Hyg. 104, 2176–2184 (2021).ADS Article Google Scholar 
  6. Williamson, E. J. et al. OpenSAFELY: Factors associated with COVID-19 death in 17 million patients. Nature (2020).
  7. NHS Digital. Shielded patient list risk criteria. (2021).
  8. Siemieniuk, R. A. C. et al. Drug treatments for covid-19: Living systematic review and network meta-analysis. BMJ 370, m2980 (2020).
  9. Office for National Statistics. Excess winter mortality in England and Wales: 2019 to 2020 (provisional) and 2018 to 2019 (final). (2020).
  10. Public Health England. Surveillance of influenza and other seasonal respiratory viruses in the UK Winter 2020 to 2021. (2021).
  11. NHS England. Urgent and Emergency Care Daily Situation Reports 2021–22 (2022).
  12. Bottle, A., Faitna, P. & Aylin, P. P. Patient-level and hospital-level variation and related time trends in COVID-19 case fatality rates during the first pandemic wave in England: Multilevel modelling analysis of routine data. BMJ Qual. Saf (2021).
  13. Cai, M., Bowe, B., Xie, Y. & Al-Aly, Z. Temporal trends of COVID-19 mortality and hospitalisation rates: An observational cohort study from the US Department of Veterans Affairs. BMJ Open 11, e047369 (2021).
  14. Snooks, H. et al. Call volume, triage outcomes, and protocols during the first wave of the COVID-19 pandemic in the United Kingdom: Results of a national survey. J. Am. Coll. Emerg. Physicians Open 2, e12492 (2021).PubMed PubMed Central Google Scholar 
  15. Challen, R. et al. Risk of mortality in patients infected with SARS-CoV-2 variant of concern 202012/1: Matched cohort study. BMJ 372, n579 (2021).
  16. Egan, C., Thorpe, M., Knight, S., Shaw, C. & Mclean, K. Hospital Admission for COVID-19 and impact of vaccination: Analysis of linked data from the National Immunisation Management Service (NIMS) and the Coronavirus Clinical Information Network (CO-CIN). (2021).
  17. NHS England. COVID-19 vaccinations archive. (2022).
  18. Abani, O. et al. Tocilizumab in patients admitted to hospital with COVID-19 (RECOVERY): A randomised, controlled, open-label, platform trial. Lancet 397, 1637–1645 (2021).Article Google Scholar 
  19. Perera, B. et al. COVID-19 deaths in people with intellectual disability in the UK and Ireland: Descriptive study. BJPsych Open 6, e123 (2020).Article Google Scholar 
  20. McCay, L. Omicron: Urgent action needed on NHS staffing crisis. BMJ 376, o18 (2022).
  21. NHS England. Urgent and Emergency Care Daily Situation Reports 2020–21. (2021).
  22. NHS England. Record number of NHS ambulance call outs for life-threatening conditions in December, despite jump in Omicron absences. (2022).
  23. Griffith, G. J. et al. Collider bias undermines our understanding of COVID-19 disease risk and severity. Nat. Commun. 11, 5749 (2020).ADS CAS Article Google Scholar 
  24. World Health Organisation. International guidelines for certification and classification (coding) of COVID-19 as cause of death based on ICD International Statistical Classification of Diseases. (2020).
  25. NHS England. COVID Oximetry @home. (2021).
  26. Public Health England. Laboratory reporting to Public Health England: A guide for diagnostic laboratories. (2020).
  27. NHS Digital. General Practice Extraction Service (GPES) Data for pandemic planning and research: A guide for analysts and users of the data. (2021).
  28. NHS Digital. Shielded patient list. (2021).
  29. Ministry of Housing & Communities & Local Government. English indices of deprivation 2019. (2019).
  30. Harrell, F. E. Regression Modeling Strategies, With Applications to Linear Models, Logistic Regression, and Survival Analysis (Springer, 2001).
  31. White, I. R., Royston, P. & Wood, A. M. Multiple imputation using chained equations: Issues and guidance for practice. Stat. Med. 30, 377–399 (2011).MathSciNet Article Google Scholar 

Change in reported COVID-19 cases per 100k people in the last two weeks

Authors: Kavya Beheraj  N.Y. Times; Cartogram: /AxiosApril 13-26, 2022

COVID cases are on the rise in all but six states and Washington, D.C., as the Omicron subvariant continues to spread across the U.S.

The big picture: Case rates and hospitalizations are still well below pandemic highs, prompting NIAID director Anthony Fauci to say this week that the nation is out of a “full-blown explosive pandemic phase.”

By the numbers: There were roughly 51,000 new daily cases over the last week, up 51% from two weeks ago.

  • The highest reported case rates were in Vermont, where there were 45.5 new cases per 100,000 people. Rhode Island and New York had 35.4 and 33.3 new cases per 100,000 people, respectively. Cases were increasing in all three states.
  • The lowest reported case rates were in Mississippi where there were 3.0 new cases of COVID per 100,000 people. Georgia and Alabama reported 3.4 new cases and 3.6 new cases per 100,000 people, respectively. Mississippi and Georgia had declining case rates while Alabama saw a slight uptick.

Between the lines: COVID deaths are still falling. There were 362 deaths a day on average, down 23% from an average of about 470 deaths a day two weeks ago. But the decline in deaths slowed a bit.

  • The U.S. is approaching 1 million deaths from COVID since the start of the pandemic.

Reality check: As we mentioned last week, the data regarding new cases are getting less reliable as the public testing infrastructure continues to wind down and home test results are less likely to be reported to officials.

  • The World Health Organization warned the world is “increasingly blind” to COVID transmission, the Daily Mail reported.

COVID-19 cases are rising sharply again — should we worry?

Authors: John Woolfolk, Bay Area News Group  Apr 24, 2022

COVID-19 cases and hospitalizations are rising once again across the country, driven by more contagious subvariants of the virus and leaving health experts unsure whether vaccination and immunity from prior infection will be protective enough to prevent yet another deadly wave of infections.

Nearly a third of the country is now registering substantial or high levels of COVID-19 transmission in the last seven days, including most Bay Area counties at the high level, according to the Centers for Disease Control and Prevention.

But now, more than two years into the pandemic, what exactly does that mean in terms of serious illness?

Hospitalizations from COVID-19 are up over 30% in the last two weeks in New York state, said Dr. Céline Gounder, an infectious disease specialist and epidemiologist and Editor-at-Large for Public Health at Kaiser Health News. “The situation in the Northeast may foreshadow what’s to come in the Bay Area.”

Death rates mostly have yet to increase, but in earlier waves, they tended to follow the trend in hospitalizations.

The rising numbers follow a relatively short reprieve from this winter’s nationwide surge propelled by the highly contagious omicron variant. Omicron began spreading in December, with infections peaking a month later and then dropping sharply through February and March.

Nationally, average daily cases are up more than 70% since the end of March, though they remain far below the omicron and delta peaks, and the decline in hospitalization rates appears to be reversing. In California, average daily cases have gone up by more than 50% since the end of March.

The rebound comes just as U.S. officials dropped the mask mandate for public transportation after a judge in Florida said it exceeded their authority — with people from all over now crowding unmasked into planes, buses, subways and rail cars. Though the federal government is appealing the judge’s ruling, airlines and many transit operators indicated the mask mandate isn’t likely to return soon.

New CDC data Thursday showed that the highly transmissible omicron variant that dominated in January has given way to its more contagious cousin, BA.2, which now accounts for 3 out of 4 cases across the country. And BA.2 is yielding to an even faster-spreading sister, BA.2.12.1, now 1 in 5 cases.

COVID cases rise again in half the states

Change in reported COVID-19 cases per 100k people in the last two weeks

March 23 to April 5, 2022

Half of the states are seeing COVID case numbers rise again while nationwide totals continue to fall.

The big picture: The Omicron subvariant known as BA.2 is the dominant strain circulating around the U.S., accounting for almost three out of every four cases.

By the numbers: Overall, cases dropped 5% across the U.S. to an average of about 28,700 cases from an average of more than 30,000 cases two weeks ago.

  • Three states — Alaska, Vermont and Rhode Island — had more than 20 new cases per 100,000 people.
  • Nine states — Utah, Montana, South Dakota, Kansas, Louisiana, Iowa, Arkansas, Indiana and Tennessee — had three or fewer new cases per 100,000 people.

Between the lines: Deaths fell to an average of 600 a day, down 34% from just over 900 a day two weeks ago.

What we’re watching: While U.S. officials have said they aren’t expecting a significant rise in hospitalizations or deaths, there have been signs of hospitalizations rising among older individuals in the U.K., the Guardian reported.

  • Since those numbers lag behind new cases, we won’t have a clear view of that impact in the U.S. for a few weeks.
  • The highly contagious subvariant surged through parts of Europe but probably will spare many Americans, thanks in part to this winter’s Omicron surge.

Axios on facebookAxios on twitterAxios on linkedinAxios on email