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How COVID-19 Took Hold in North America and Europe

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SARS-CoV-2 Tracking
Caption: SARS-CoV-2 introductions to U.S. and Europe. Credit: Modified from Worobey M, Science, 2020.

It was nearly 10 months ago on January 15 that a traveler returned home to the Seattle area after visiting family in Wuhan, China. A few days later, he started feeling poorly and became the first laboratory-confirmed case of coronavirus disease 2019 (COVID-19) in the United States. The rest is history.

However, new evidence published in the journal Science suggests that this first COVID-19 case on the West Coast didn’t snowball into the current epidemic. Instead, while public health officials in Washington state worked tirelessly and ultimately succeeded in containing its sustained transmission, the novel coronavirus slipped in via another individual about two weeks later, around the beginning of February.

COVID-19 is caused by the novel coronavirus SARS-CoV-2. Last winter, researchers sequenced the genetic material from the SARS-CoV-2 that was isolated from the returned Seattle traveler. While contact tracing didn’t identify any spread of this particular virus, dubbed WA1, questions arose when a genetically similar virus known as WA2 turned up in Washington state. Not long after, WA2-like viruses then appeared in California; British Columbia, Canada; and eventually 3,000 miles away in Connecticut. By mid-March, this WA2 cluster accounted for the vast majority—85 percent—of the cases in Washington state.

But was it possible that the WA2 cluster is a direct descendent of WA1? Did WA1 cause an unnoticed chain of transmission over several weeks, making the Seattle the epicenter of the outbreak in North America?

To answer those questions and others from around the globe, Michael Worobey, University of Arizona, Tucson, and his colleagues drew on multiple sources of information. These included data peretaining to viral genomes, airline passenger flow, and disease incidence in China’s Hubei Province and other places that likely would have influenced the probability that infected travelers were moving the virus around the globe. Based on all the evidence, the researchers simulated the outbreak more than 1,000 times on a computer over a two-month period, beginning on January 15 and assuming the epidemic started with WA1. And, not once did any of their simulated outbreaks match up to the actual genome data.

Those findings suggest to the researchers that the idea WA1 is responsible for all that came later is exceedingly unlikely. The evidence and simulations also appear to rule out the notion that the earliest cases in Washington state entered the United States by way of Canada. A deep dive into the data suggests a more likely scenario is that the outbreak was set off by one or more introductions of genetically similar viruses from China to the West Coast. Though we still don’t know exactly where, the Seattle area is the most likely site given the large number of WA2-like viruses sampled there.

Worobey’s team conducted a second analysis of the outbreak in Europe, and those simulations paint a similar picture to the one in the United States. The researchers conclude that the first known case of COVID-19 in Europe, arriving in Germany on January 20, led to a relatively small number of cases before being stamped out by aggressive testing and contact tracing efforts. That small, early outbreak probably didn’t spark the later one in Northern Italy, which eventually spread to the United States.

Their findings also show that the chain of transmission from China to Italy to New York City sparked outbreaks on the East Coast slightly later in February than those that spread from China directly to Washington state. It confirms that the Seattle outbreak was indeed the first, predating others on the East Coast and in California.

The findings in this report are yet another reminder of the value of integrating genome surveillance together with other sources of data when it comes to understanding, tracking, and containing the spread of COVID-19. They also show that swift and decisive public health measures to contain the virus worked when SARS-CoV-2 first entered the United States and Europe, and can now serve as models of containment.

Since the suffering and death from this pandemic continues in the United States, this historical reconstruction from early in 2020 is one more reminder that all of us have the opportunity and the responsibility to try to limit further spread. Wear your mask when you are outside the home; maintain physical distancing; wash your hands frequently; and don’t congregate indoors, where the risks are greatest. These lessons will enable us to better anticipate, prevent, and respond to additional outbreaks of COVID-19 or any other novel viruses that may arise in the future.

Reference:

[1] The emergence of SARS-CoV-2 in Europe and North America. Worobey M, Pekar J, Larsen BB, Nelson MI, Hill V, Joy JB, Rambaut A, Suchard MA, Wertheim JO, Lemey P. Science. 2020 Sep 10:eabc8169 [Epub ahead of print]

Links:

Coronavirus (COVID-19) (NIH)

Michael Worobey (University of Arizona, Tucson)

NIH Support: National Institute of Allergy and Infectious Diseases; Fogarty International Center; National Library of Medicine


Months After Recovery, COVID-19 Survivors Often Have Persistent Lung Trouble

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Lung function test
Caption: Testing breathing capacity with a spirometer. Credit: iStock/Koldunov

The pandemic has already claimed far too many lives in the United States and around the world. Fortunately, as doctors have gained more experience in treating coronavirus disease 2019 (COVID-19), more people who’ve been hospitalized eventually will recover. This raises an important question: what does recovery look like for them?

Because COVID-19 is still a new condition, there aren’t a lot of data out there yet to answer that question. But a recent study of 55 people recovering from COVID-19 in China offers some early insight into the recovery of lung function [1]. The results make clear that—even in those with a mild-to-moderate infection—the effects of COVID-19 can persist in the lungs for months. In fact, three months after leaving the hospital about 70 percent of those in the study continued to have abnormal lung scans, an indication that the lungs are still damaged and trying to heal.

The findings in EClinicalMedicine come from a team in Henan Province, China, led by Aiguo Xu, The First Affiliated Hospital of Zhengzhou University; Yanfeng Gao, Zhengzhou University; and Hong Luo, Guangshan People’s Hospital. They’d heard about reports of lung abnormalities in patients discharged from the hospital. But it wasn’t clear how long those problems stuck around.

To find out, the researchers enrolled 55 men and women who’d been admitted to the hospital with COVID-19 three months earlier. Some of the participants, whose average age was 48, had other health conditions, such as diabetes or heart disease. But none had any pre-existing lung problems.

Most of the patients had mild or moderate respiratory illness while hospitalized. Only four of the 55 had been classified as severely ill. Fourteen patients required supplemental oxygen while in the hospital, but none needed mechanical ventilation.

Three months after discharge from the hospital, all of the patients were able to return to work. But they continued to have lingering symptoms of COVID-19, including shortness of breath, cough, gastrointestinal problems, headache, or fatigue.

Evidence of this continued trouble also showed up in their lungs. Thirty-nine of the study’s participants had an abnormal result in their computed tomography (CT) lung scan, which creates cross-sectional images of the lungs. Fourteen individuals (1 in 4) also showed reduced lung function in breathing tests.

Interestingly, the researchers found that those who went on to have more lasting lung problems also had elevated levels of D-dimer, a protein fragment that arises when a blood clot dissolves. They suggest that a D-dimer test might help to identify those with COVID-19 who would benefit from pulmonary rehabilitation to rebuild their lung function, even in the absence of severe respiratory symptoms.

This finding also points to the way in which the SARS-CoV-2 virus seems to enhance a tendency toward blood clotting—a problem addressed in our Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) public-private partnership. The partnership recently initiated a trial of blood thinners. That trial will start out by focusing on newly diagnosed outpatients and hospitalized patients, but will go on to include a component related to convalescence.

Moving forward, it will be important to conduct larger and longer-term studies of COVID-19 recovery in people of diverse backgrounds to continue to learn more about what it means to survive COVID-19. The new findings certainly indicate that for many people who’ve been hospitalized with COVID-19, regaining normal lung function may take a while. As we learn even more about the underlying causes and long-term consequences of this new infectious disease, let’s hope it will soon lead to insights that will help many more COVID-19 long-haulers and their concerned loved ones breathe easier.

Reference:

[1] Follow-up study of the pulmonary function and related physiological characteristics of COVID-19 survivors three months after recovery. Zhao YM, Shang YM, Song WB, Li QQ, Xie H, Xu QF, Jia JL, Li LM, Mao HL, Zhou XM, Luo H, Gao YF, Xu AG. EClinicalMedicine.2020 Aug 25:100463

Links:

Coronavirus (COVID-19) (NIH)

How the Lungs Work (National Heart, Lung, and Blood Institute/NIH)

Computed Tomography (CT) (National Institute of Biomedical Imaging and Bioengineering/NIH)

Zhengzhou University (Zhengzhou City, Henan Province, China)

Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) (NIH)


Study in Primates Finds Acquired Immunity Prevents COVID-19 Reinfections

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SARS-CoV-2 and Antibodies

There have been rare reports of people recovering from infection with SARS-CoV-2, the novel coronavirus that causes COVID-19, only to test positive a second time. Such results might be explained by reports that the virus can linger in our systems. Yet some important questions remain: Is it possible that people could beat this virus only to get reinfected a short time later? How long does immunity last after infection? And what can we expect about the duration of protection from a vaccine?

A recent study of rhesus macaques, which are among our close primate relatives, offers relevant insights into the first question. In a paper published in the journal Science, researchers found that after macaques recover from mild SARS-CoV-2 infection, they are protected from reinfection—at least for a while.

In work conducted in the lab of Chuan Qin, Peking Union Medical College, Beijing, China, six macaques were exposed to SARS-CoV-2. Following infection, the animals developed mild-to-moderate illness, including pneumonia and evidence of active infection in their respiratory and gastrointestinal tracts. Twenty-eight days later, when the macaques had cleared the infection and started recovering, four animals were re-exposed to the same strain of SARS-CoV-2. The other two served as controls, with researchers monitoring their continued recovery.

Qin’s team noted that after the second SARS-CoV-2 exposure, the four macaques developed a transient fever that had not been seen after their first infection. No other signs of reinfection were observed or detected in chest X-rays, and the animals tested negative for active virus over a two-week period.

While more study is needed to understand details of the immune responses, researchers did detect a reassuring appearance of antibodies specific to the SARS-CoV-2 spike protein in the macaques over the course of the first infection. The spike protein is what the virus uses to attach to macaque and human cells before infecting them.

Of interest, levels of those neutralizing antibodies were even higher two weeks after the second viral challenge than they were two weeks after the initial exposure. However, researchers note that it remains unclear which factors specifically were responsible for the observed protection against reinfection, and apparently the first exposure was sufficient.

Since the second viral challenge took place just 28 days after the first infection, this study provides a rather limited window into broad landscape of SARS-CoV-2 infection and recovery. Consequently, it will be important to determine to what extent a first infection might afford protection over the course of months and even years. Also, because the macaques in this study developed only mild-to-moderate COVID-19, more research is needed to investigate what happens after recovery from more severe COVID-19.

Of course, macaques are not humans. Nevertheless, the findings lend hope that COVID-19 patients who develop acquired immunity may be at low risk for reinfection, at least in the short term. Additional studies are underway to track people who came down with COVID-19 in New York during March and April to see if any experience reinfection. By the end of this year, we should have better answers.

Reference:

[1] Primary exposure to SARS-CoV-2 protects against reinfection in rhesus macaques. Deng W, Bao L, Liu J, et al. Science. 2020 Jul 2. [Published online ahead of print].

Links:

Coronavirus (COVID-19) (NIH)

Qin Lab (Peking Union Medical College, Beijing, China)


Public Health Policies Have Prevented Hundreds of Millions of Coronavirus Infections

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Touchless carryout
Credit: Stock photo/Juanmonino

The alarming spread of coronavirus disease 2019 (COVID-19) last winter presented a profound threat to nations around the world. Many government leaders responded by shutting down all non-essential activities, implementing policies that public health officials were hopeful could slow the highly infectious SARS-CoV-2, the novel coronavirus that causes COVID-19.

But the shutdown has come at a heavy cost for the U.S. and global economies. It’s also taken a heavy personal toll on many of us, disrupting our daily routines—getting children off to school, commuting to the office or lab, getting together with friends and family, meeting face to face to plan projects, eating out, going to the gym—and causing lots of uncertainty and frustration.

As difficult as the shutdowns have been, new research shows that without these public health measures, things would have been much, much worse. According to a study published recently in Nature [1], the implementation of containment and mitigation strategies across the globe prevented or delayed about 530 million coronavirus infections across six countries—China, South Korea, Iran, Italy, France, and the United States. Take a moment to absorb that number—530 million. Right now, there are 8.8 million cases documented across the globe.

Estimates of the benefits of anti-contagion policies have drawn from epidemiological models that simulate the spread of COVID-19 in various ways, depending on assumptions built into each model. But models are sophisticated ways of guessing. Back when decisions about staying at home had to be made, no one knew for sure if, or how well, such approaches to limit physical contact would work. What’s more, the only real historical precedent was the 1918 Spanish flu pandemic in a very different, much-less interconnected world.

That made it essential to evaluate the pros and cons of these public health strategies within a society. As many people have rightfully asked: are the health benefits really worth the pain?

Recognizing a pressing need to answer this question, an international team of scientists dropped everything that they were doing to find out. Led by Solomon Hsiang, director of the University of California, Berkeley’s Global Policy Laboratory and Chancellor’s Professor at the Goldman School of Public Policy, a research group of 15 researchers from China, France, South Korea, New Zealand, Singapore, and the United States evaluated 1,717 policies implemented in all six countries between January 2020, when the virus began its global rise, and April 6, 2020.

The team relied on econometric methods that use statistics and math to uncover meaningful patterns hiding in mountains of data. As the name implies, these techniques are used routinely by economists to understand, in a before-and-after way, how certain events affect economic growth.

In this look-back study, scientists compare observations before and after an event they couldn’t control, such as a natural disaster or disease outbreak. In the case of COVID-19, these researchers compared public health datasets in multiple localities (e.g., states or cities) within each of the six countries before and several weeks after lockdowns. For each data sample from a given locality, the time period right before a policy deployment was the experimental “control” for the same locality several weeks after it received one or more shutdown policy “treatments.”

Hsiang and his colleagues measured the effects of all the different policies put into place at local, regional, and national levels. These included travel restrictions, business and school closures, shelter-in-place orders, and other actions that didn’t involve any type of medical treatment for COVID-19.

Because SARS-CoV-2 is a new virus, the researchers knew that early in the pandemic, everyone was susceptible, and the outbreak would grow exponentially. The scientists could then use a statistical method designed to estimate how the daily growth rate of infections changed over time within a location after different combinations of large-scale policies were put into place.

The result? Early in the pandemic, coronavirus infection rates grew 38 percent each day, on average, across the six countries: translating to a two-day doubling time. Applying all policies at once slowed the daily COVID-19 infection rate by 31 percentage points! Policies having the clearest benefit were business closures and lockdowns, whereas travel restrictions and bans on social gatherings had mixed results. Without more data, the analysis can’t specify why, but the way different countries enacted those policies might be one reason.

As we continue to try to understand and thwart this new virus and its damage to so many aspects of our personal and professional lives, these new findings add context, comfort, and guidance about the present circumstances. They tell us that individual sacrifices from staying home and canceled events contributed collectively to a huge, positive impact on the world.

Now, as various communities start cautiously to open up, we should continue to practice social distancing, mask wearing, and handwashing. This is not the time to say that the risk has passed. We are all tired of the virus and its consequences for our personal lives, but the virus doesn’t care. It’s still out there. Stay safe, everyone!

Reference:

[1] The effect of large-scale anti-contagion policies on the COVID-19 pandemic. Hsiang S, Allen D, Annan-Phan S, et al. Nature. 2020 June 8 [published online ahead of print].

Links:

Coronavirus (NIH)

Global Policy Lab: Effect of Anti-Contagion Policies (University of California, Berkeley)

Video: How much have policies to slow COVID-19 worked? (UC Berkeley)

Hsiang Lab (UC Berkeley)

Global Policy Lab Rallies for COVID-19 Research,” COVID-19 News, Goldman School of Public Policy, June 5, 2020.


First Molecular Profiles of Severe COVID-19 Infections

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COVID-19 Severity Test
Credit: NIH

To ensure that people with coronavirus disease 2019 (COVID-19) get the care they need, it would help if a simple blood test could predict early on which patients are most likely to progress to severe and life-threatening illness—and which are more likely to recover without much need for medical intervention. Now, researchers have provided some of the first evidence that such a test might be possible.

This tantalizing possibility comes from a study reported recently in the journal Cell. In this study, researchers took blood samples from people with mild to severe COVID-19 and analyzed them for nearly 2,000 proteins and metabolites [1]. Their detailed analyses turned up hundreds of molecular changes in blood that differentiated milder COVID-19 symptoms from more severe illness. What’s more, they found that they could train a computer to use the most informative of the proteins and predict the disease severity with a high degree of accuracy.

The findings come from the lab of Tiannan Guo, Westlake University, Zhejiang Province, China. His team recognized that, while we’ve learned a lot about the clinical symptoms of COVID-19 and the spread of the illness around the world, much less is known about the condition’s underlying molecular features. It also remains mysterious what distinguishes the 80 percent of symptomatic infected people who recover with little to no need for medical care from the other 20 percent, who suffer from much more serious illness, including respiratory distress requiring oxygen or even more significant medical interventions.

In search of clues, Guo and colleagues analyzed hundreds of molecular changes in blood samples collected from 53 healthy people and 46 people with COVID-19, including 21 with severe disease involving respiratory distress and decreased blood-oxygen levels. Their studies turned up more than 470 proteins and metabolites that differed in people with COVID-19 compared to healthy people. Of those, levels of about 300 were associated with disease severity.

Further analysis revealed that the majority of proteins and metabolites on the list are associated with the suppression or dysregulation of one of three biological processes. Two processes are related to the immune system, including early immune responses and the function of particular scavenging immune cells called macrophages. The third relates to the function of platelets, which are sticky, disc-shaped cell fragments that play an essential role in blood clotting. Such biological insights might help pave the way for potentially effective new ways to treat COVID-19 down the road.

Next, the researchers turned to “machine learning” to explore the possibility that such molecular changes also might be used to predict mild versus severe COVID-19. Machine learning involves the use of computers to discern patterns, or molecular signatures, in large data sets that a human being couldn’t readily pick out. In this case, the question was whether the computer could “learn” to tell the difference between mild and severe COVID-19 based on molecular data alone.

Their analyses showed that a computer, once trained, could differentiate mild and severe COVID-19 based on just 22 proteins and 7 metabolites. Their model correctly classified all but one person in the original training set, for an accuracy of about 94 percent. And importantly, in further prospective validation tests, they confirmed that this model accurately identified mild versus severe COVID-19 in most cases.

While these findings are certainly encouraging, there’s much more work to do. It will be important to explore these molecular signatures in many more people. It also will be critical to find out how early in the course of the disease such telltale signatures arise. While we await those answers, I find encouragement in all that we’re learning—and will continue to learn—about COVID-19 each day.

Reference:

[1] Proteomic and metabolomic characterization of COVID-19 patient sera. Shen B et al. Cell. 28 May 2020. [Epub ahead of publication]

Links:

Coronavirus (COVID-19) (NIH)

Blood Tests (National Heart, Lung, and Blood Institute/NIH)

Tiannan Guo Lab (Westlake University, Zhejiang Province, China)


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