CDC
COVID-19 Infected Many More Americans in 2020 than Official Tallies Show
Posted on by Dr. Francis Collins
At the end of last year, you may recall hearing news reports that the number of COVID-19 cases in the United States had topped 20 million. While that number came as truly sobering news, it also likely was an underestimate. Many cases went undetected due to limited testing early in the year and a large number of infections that produced mild or no symptoms.
Now, a recent article published in Nature offers a more-comprehensive estimate that puts the true number of infections by the end of 2020 at more than 100 million [1]. That’s equal to just under a third of the U.S. population of 328 million. This revised number shows just how rapidly this novel coronavirus spread through the country last year. It also brings home just how timely the vaccines have been—and continue to be in 2021—to protect our nation’s health in this time of pandemic.
The work comes from NIH grantee Jeffrey Shaman, Sen Pei, and colleagues, Columbia University, New York. As shown above in the map, the researchers estimated the percentage of people who had been infected with SARS-CoV-2, the novel coronavirus that causes COVID-19, in communities across the country through December 2020.
To generate this map, they started with existing national data on the number of coronavirus cases (both detected and undetected) in 3,142 U.S. counties and major metropolitan areas. They then factored in data from the Centers for Disease Control and Prevention (CDC) on the number of people who tested positive for antibodies against SARS-CoV-2. These CDC data are useful for picking up on past infections, including those that went undetected.
From these data, the researchers calculated that only about 11 percent of all COVID-19 cases were confirmed by a positive test result in March 2020. By the end of the year, with testing improvements and heightened public awareness of COVID-19, the ascertainment rate (the number of infections that were known versus unknown) rose to about 25 percent on average. This measure also varied a lot across the country. For instance, the ascertainment rates in Miami and Phoenix were higher than the national average, while rates in New York City, Los Angeles, and Chicago were lower than average.
How many people were potentially walking around with a contagious SARS-CoV-2 infection? The model helps to answer this, too. On December 31, 2020, the researchers estimate that 0.77 percent of the U.S. population had a contagious infection. That’s about 1 in every 130 people on average. In some places, it was much higher. In Los Angeles, for example, nearly 1 in 40 (or 2.42 percent) had a SARS-CoV-2 infection as they rang in the New Year.
Over the course of the year, the fatality rate associated with COVID-19 dropped, at least in part due to earlier diagnosis and advances in treatment. The fatality rate went from 0.77 percent in April to 0.31 percent in December. While this is great news, it still shows that COVID-19 remains much more dangerous than seasonal influenza (which has a fatality rate of 0.08 percent).
Today, the landscape has changed considerably. Vaccines are now widely available, giving many more people immune protection without ever having to get infected. And yet, the rise of the Delta and other variants means that breakthrough infections and reinfections—which the researchers didn’t account for in their model—have become a much bigger concern.
Looking ahead to the end of 2021, Americans must continue to do everything they can to protect their communities from the spread of this terrible virus. That means getting vaccinated if you haven’t already, staying home and getting tested if you’ve got symptoms or know of an exposure, and taking other measures to keep yourself and your loved ones safe and well. These measures we take now will influence the infection rates and susceptibility to SARS-CoV-2 in our communities going forward. That will determine what the map of SARS-CoV-2 infections will look like in 2021 and beyond and, ultimately, how soon we can finally put this pandemic behind us.
Reference:
[1] Burden and characteristics of COVID-19 in the United States during 2020. Pei S, Yamana TK, Kandula S, Galanti M, Shaman J. Nature. 2021 Aug 26.
Links:
COVID-19 Research (NIH)
Sen Pei (Columbia University, New York)
Jeffrey Shaman (Columbia University, New York)
Following COVID-19 Vaccines Across the United States
Posted on by Dr. Francis Collins
Recently, there is a new and very hopeful COVID-19 number for everyone to track: the total number of vaccine doses that have been administered in the United States. If 80 percent of Americans roll up their sleeves in the coming months and accept COVID-19 vaccinations, we can greatly slow the spread of the novel coronavirus in our communities and bring this horrible pandemic to an end in 2021.
So far, more than 20 million people in our country have received one or two doses of either the Pfizer or Moderna vaccine. While this number is lower than initially projected for a variety of logistical reasons, we’re already seeing improvements in the distribution system that has made it possible to get close to 1 million doses administered per day.
If you want to keep track of the vaccine progress in your state over the coming weeks, it’s now pretty easy to do online. A fine resource is the vaccine information on the Centers for Disease Control and Prevention (CDC) COVID Data Tracker. It offers an interactive state-by-state map, as well as data on vaccinations in long-term care facilities. Keep in mind that there’s a delay of three to five days in reporting actual vaccinations from the states.
There’s also a lot of useful information on the Johns Hopkins Coronavirus Resource Center’s Vaccine Tracker. Posting the daily updates is a team, led by William Moss, that draws on the expertise of data scientists, analysts, programmers, and researchers. The Hopkins team gathers its vaccination data from each state’s official dashboard, webpages, press releases, or wherever cumulative numbers are reported. Not all states publish the same vaccine information, and that’s what can make the Vaccine Tracker so challenging to compile.
The Hopkins team now presents on its homepage the top 10 U. S. states and territories to vaccinate fully the highest percentage of their residents. With another click, there’s also a full rundown of vaccine administration by state and territory, plus the District of Columbia. The site also links to lots of other information about COVID-19—including cases, testing, contact tracing, and an interactive tool about vaccine development.
In uncertain times, knowledge can be a source of comfort. That’s what makes these interactive COVID-19 resources so helpful and empowering. They show that, with time, safe and effective COVID-19 vaccines will indeed coming to everyone. I hope that you will accept your vaccine, like I did when given the opportunity. However, until we get to the point where most Americans are immunized, we must stay vigilant and keep up our tried-and-true public health measures such as wearing masks, limiting physical interactions (especially indoors), and washing our hands.
Links:
COVID-19 Research (NIH)
CDC COVID Data Tracker (Centers for Disease Control and Prevention, Atlanta)
Coronavirus Resource Center (Johns Hopkins University School of Medicine)
William Moss (Johns Hopkins University, Baltimore)
International Vaccine Access Center (Johns Hopkins Bloomberg School of Public Health, Baltimore)
Genome Data Help to Track COVID-19 Superspreading Event
Posted on by Dr. Francis Collins
Credit: iStock/Chaay_Tee
When it comes to COVID-19, anyone, even without symptoms, can be a “superspreader” capable of unknowingly infecting a large number of people and causing a community outbreak. That’s why it is so important right now to wear masks when out in public and avoid large gatherings, especially those held indoors, where a superspreader can readily infect others with SARS-CoV-2, the virus responsible for COVID-19.
Driving home this point is a new NIH-funded study on the effects of just one superspreader event in the Boston area: an international biotech conference held in February, before the public health risks of COVID-19 had been fully realized [1]. Almost a hundred people were infected. But it didn’t end there.
In the study, the researchers sequenced close to 800 viral genomes, including cases from across the first wave of the epidemic in the Boston area. Using the fact that the viral genome changes in very subtle ways over time, they found that SARS-CoV-2 was actually introduced independently to the region more than 80 times, primarily from Europe and other parts of the United States. But the data also suggest that a single superspreading event at the biotech conference led to the infection of almost 20,000 people in the area, not to mention additional COVID-19 cases in other states and around the world.
The findings, posted on medRxiv as a pre-print, come from Bronwyn MacInnis and Pardis Sabeti at the Broad Institute of MIT and Harvard in Cambridge, MA, and their many close colleagues at Massachusetts General Hospital, the Massachusetts Department of Public Health, and the Boston Health Care for the Homeless Program. The initial focus of MacInnis, Sabeti, and their Broad colleagues has been on developing genome data and tools for surveillance of viruses and other infectious diseases in and viral outbreaks in West Africa, including Lassa fever and Ebola virus disease.
Closer to home, they’d expected to focus their attention on West Nile virus and tick-borne diseases. But, when the COVID-19 outbreak erupted, they were ready to pivot quickly to assist several Centers for Disease Control and Prevention (CDC) and state labs in the northeastern United States to use genomic tools to investigate local outbreaks.
It’s been clear from the beginning of the pandemic that COVID-19 cases often arise in clusters, linked to gatherings in places such as cruise ships, nursing homes, and homeless shelters. But the Broad Institute team and their colleagues realized, it’s difficult to see how extensively a virus spreads from such places into the wider community based on case counts alone.
Contact tracing certainly helps to track community spread of the virus. This surveillance strategy depends on quick, efficient identification of an infected individual. It follows up with the identification of all who’ve recently been in close contact with that person, allowing the contacts to self-quarantine and break the chain of transmission.
But contact tracing has its limitations. It’s not always possible to identify all the people that an infected person has been in recent contact with. Genome data, however, is particularly useful after the fact for connecting those dots to get a big picture view of viral transmission.
Here’s how it works: as SARS-CoV-2 spreads, the virus sometimes picks up a new mutation. Those tiny spelling changes in the viral genome usually have no effect on how the virus causes disease, but they do serve as distinct genomic fingerprints. Using those fingerprints to guide the way, researchers can trace the path the virus took through a community and beyond, identifying connections among cases that would be untrackable otherwise.
With this in mind, MacInnis and Sabeti’s team set out to help Boston’s public health officials understand just how the epidemic escalated so quickly in the Boston area, and just how much the February conference had contributed to community transmission of the virus. They also investigated other case clusters in the area, including within a skilled nursing facility, homeless shelters, and at Massachusetts General Hospital itself, to understand the spread of COVID-19 in these settings.
Based on contact tracing, officials had already connected approximately 90 cases of COVID-19 to the biotech conference, 28 of which were included in the original 772 viral genomes in this dataset. Based on the distinct genomic fingerprint carried by the 28 genomes, the researchers went on to discover that more than one-third of Boston area cases without any known link to the conference could indeed be traced back to the event.
When the researchers considered this proportion to the number of cases recorded in the region during the study, they extrapolated that the superspreader event led to nearly 20,000 cases in the Boston area. In contrast, the genome data show cases linked to another superspreader event that took place within a skilled nursing facility, while devastating to the residents, had much less of an impact on the surrounding community.
The analysis also uncovered some unexpected connections. The dataset showed that SARS-CoV-2 was brought to clients and staff at the Boston Health Care for the Homeless Program at least seven times. Remarkably, two of those introductions also traced back to the biotech conference. Researchers also found infections in Chelsea, Revere, and Everett, which were some of the hardest hit communities in the Boston area, that were connected to the original superspreading event.
There was some reassuring news about how precautions in hospitals are working. The researchers examined cases that were diagnosed among patients at Massachusetts General Hospital, raising concerns that the virus might have spread from one patient to another within the hospital. But the genome data show that those cases, in fact, weren’t part of the same transmission chain. They may have contracted the virus before they were hospitalized. Or it’s possible that staff may have inadvertently brought the virus into the hospital. But there was no patient-to-patient transmission.
Massachusetts is one of the states in which the COVID-19 pandemic had a particularly severe early impact. As such, these results present broadly applicable lessons for other states and urban areas about how the virus spreads. The findings highlight the value of genomic surveillance, along with standard contact tracing, for better understanding of viral transmission in our communities and improved prevention of future outbreaks.
Reference:
[1] Phylogenetic analysis of SARS-CoV-2 in the Boston area highlights the role of recurrent importation and superspreading events. Lemieux J. et al. medRxiv. August 25, 2020.
Links:
Coronavirus (COVID-19) (NIH)
Bronwyn MacInnis (Broad Institute of Harvard and MIT, Cambridge, MA)
Sabeti Lab (Broad Institute of Harvard and MIT)
NIH Support: National Institute of Allergy and Infectious Diseases; National Human Genome Research Institute; National Institute of General Medical Sciences
Citizen Scientists Take on the Challenge of Long-Haul COVID-19
Posted on by Dr. Francis Collins
Coronaviruses are a frequent cause of the common cold. Most of us bounce back from colds without any lasting health effects. So, you might think that individuals who survive other infectious diseases caused by coronaviruses—including COVID-19—would also return to normal relatively quickly. While that can be the case for some people, others who’ve survived even relatively mild COVID-19 are experiencing health challenges that may last for weeks or even months. In fact, the situation is so common, that some of these folks have banded together and given their condition a name: the COVID “long-haulers.”
Among the many longer-term health problems that have been associated with COVID-19 are shortness of breath, fatigue, cognitive issues, erratic heartbeat, gastrointestinal issues, low-grade fever, intolerance to physical or mental activity, and muscle and joint pains. COVID-19 survivors report that these symptoms flair up unpredictably, often in different combinations, and can be debilitating for days and weeks at a time. Because COVID-19 is such a new disease, little is known about what causes the persistence of symptoms, what is impeding full recovery, or how to help the long-haulers.
More information is now emerging from the first detailed patient survey of post-COVID syndrome, also known as Long COVID [1]. What’s unique about the survey is that it has been issued by a group of individuals who are struggling with the syndrome themselves. These citizen scientists, who belong to the online Body Politic COVID-19 Support Group, decided to take matters into their own hands. They already had a pretty good grip on what sort of questions to ask, as well as online access to hundreds of long-haulers to whom they could pose the questions.
The citizen scientists’ group, known as the Patient-led Research for COVID-19, brought a lot of talent and creativity to the table. Members reside in the United States, Canada, and England, and none have ever met face to face. But, between their day jobs, managing time differences, and health challenges, each team member spends about 20 hours per week working on their patient-led research, and are now putting the final touches on a follow-up survey that will get underway in the next few weeks.
For their first survey, the group members faced the difficult decision of whom to contact. First, they needed to define long hauler. For that, they decided to target people whose symptoms persisted for more than 2 weeks after their initial recovery from COVID-19. The 640 individuals who responded to the survey were predominately white females between the ages of 30 to 49 who lived in the United States. The members said that the gender bias may stem from women being more likely to join support groups and complete surveys, though there may be a gender component to Long COVID as well. About 10 percent of respondents reported that they had ultimately recovered from this post-COVID syndrome.
Another important issue revolved around COVID-19 testing. Most long-haulers in the online group had gotten sick in March and April, but weren’t so sick that they needed to be hospitalized. Because COVID-19 testing during those months was often limited to people hospitalized with severe respiratory problems, many long-haulers with mild or moderate COVID-like symptoms weren’t tested. Others were tested relatively late in the course of their illness, which can increase the likelihood of false negatives.
The team opted to cast a wide investigative net, concluding that limiting its data to only people who tested positive for COVID-19 might lead to the loss of essential information on long-haulers. It turns out that half of the respondents hadn’t been tested for SARS-CoV-2, the virus that causes COVID-19. The other half was divided almost equally between those who tested positive and those who tested negative. Here are some highlights of the survey’s findings:
Top 10 Symptoms: Respondents were asked to rank their most common symptoms and their relative severity. From highest to lowest, they were: mild shortness of breath, mild tightness of chest, moderate fatigue, mild fatigue, chills or sweats, mild body aches, dry cough, elevated temperature (98.8-100), mild headache, and brain fog/concentration challenges. Highlighting the value of patient-led research, the team was able to assemble an initial list of 62 symptoms that long-haulers often discuss in support groups. The survey revealed common symptoms that have been greatly underreported in the media, such as neurological symptoms. These include brain fog, concentration challenges, and dizziness.
Making a Recovery: Of the 60 respondents who had recovered, the average time to recovery was 27 days. The respondents who had not recovered had managed their symptoms for 40 days on average, with most dealing with health problems for 5 to 7 weeks. The report shows that the chance of full recovery by day 50 is less than 20 percent.
Exercise Capacity: About 65 percent of respondents now consider themselves mostly sedentary. Most had been highly physically active before developing COVID-19. Many long-haulers expressed concern that overexertion causes relapses
Testing. Respondents who reported testing positive for SARS-CoV-2 were tested on average earlier in their illness (by day 10) than those who reported testing negative (by day 16). The team noted that their findings parallel those in a recent published scientific study, which found false-negative rates for current PCR-based assays rose as the time between SARS-CoV-2 infection and testing increased [2]. In that published study, by day 21, the false-negative rate reached 66 percent. Only two symptoms (loss of smell and loss of taste) occurred more frequently in respondents who tested positive; the other 60 symptoms were statistically the same between groups. The citizen scientists speculate that testing is not capturing a subset of COVID patients, and more investigation is required.
Since issuing their survey results on May 11, the team has met with staff from the Centers for Disease Control and Prevention and the World Health Organization. Their work also been mentioned in magazine articles and even cited in some papers published in scientific journals.
In their next survey, these citizen scientists hope to fill in gaps in their first report, including examining antibody testing results, neurological symptoms, and the role of mental health. To increase geographic and demographic diversity, they will also translate the survey into 10 languages. If you’re a COVID-19 long-hauler and would like to find out how to get involved, there’s still time to take part in the next survey.
References:
[1] “What Does COVID-19 Recovery Actually Look Like?” Patient-led Research for COVID-19. May 11, 2020.
[2] Variation in False-Negative Rate of Reverse Transcriptase Polymerase Chain Reaction-Based SARS-CoV-2 Tests by Time Since Exposure. Kucirka LM, Lauer SA, Laeyendecker O, Boon D, Lessler J. Ann Intern Med. 2020 Aug 18;173(4):262-267.
Links:
Coronavirus (COVID-19) (NIH)
Testifying on COVID-19 Vaccine Development
Posted on by Dr. Francis Collins
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