South Carolina Tops Two Million COVID-19 Vaccinations Given – SCDHEC

FOR IMMEDIATE RELEASE:
April 1, 2021

COLUMBIA, S.C. – South Carolina marked two historical milestones this week: more than two million doses of COVID-19 vaccine have been given in the state, and everyone 16 and older is now eligible to get their shot.

As of today, a total of 2,034,077 doses have been given statewide, with 1,289,672 South Carolina residents having received at least one dose of vaccine.

“Having the COVID-19 vaccine reach the two million mark is a giant step toward ending this pandemic in South Carolina,” said Dr. Edward Simmer, DHEC Director. “We’re grateful to our local leaders, providers, and community partners for their efforts to get shots into arms as quickly as possible. They’ve not only helped our state achieve this two-million-dose milestone, but they’ve also allowed us to open up vaccines to all South Carolinians 16 and older much faster that we initially anticipated. Now we need to continue this great effort until every South Carolinian has had an opportunity to receive the vaccine.”

While vaccine doses remain somewhat limited, South Carolina and other states are seeing an uptick in the number of doses they’re receiving from the federal government. All three vaccines — Pfizer, Moderna and Janssen — are available to those aged 18 and older, and, currently, Pfizer is the only vaccine available to those aged 16-18.

“Our new message, loud and clear, is ‘don’t wait – vaccinate,’” said Dr. Brannon Traxler, DHEC Interim Public Health Director. “While you may not be able to get your shot right away, we urge everyone to continue searching for an appointment. Providers are working to open as many appointments as quickly as possible based on their inventory, and appointment availability will vary each week. For everyone 16 and older: it’s your turn to help us put a stop to COVID-19.”

As of today, 31.4 percent of South Carolinians have received at least one shot, and 17.1 percent are considered fully vaccinated. Herd immunity can be achieved and DHEC advises that certain public health recommendations like masks and physical distancing can begin to be relaxed once 70 to 80 percent of the population is vaccinated. Until then, it’s important for everyone to continue to wear masks and physically distance. According to the Centers for Disease Control and Prevention (CDC), here’s what you can do once you’ve been fully vaccinated.

“The Pfizer, Moderna, and Janssen vaccines are all safe and very effective at preventing severe illness and death, and I encourage everyone 16 and older to begin making their appointments and to get whichever brand of the shot they can first,” said Dr. Linda Bell, State Epidemiologist. “These vaccines truly are the light at the end of the tunnel, and we are indebted to the scientists and doctors across the world who have dedicated themselves to the safe and thoroughly researched development of these vaccines. These vaccines are saving lives and helping the world return to normal.” 

To find a COVID-19 vaccine appointment, visit DHEC’s vaccine locator map or call your provider directly to ask about appointment availability. For the latest COVID-19 vaccine information, visit scdhec.gov/vaxfacts. 

COVID-19 Confirmed As Third Leading Cause Of Death In US Last Year : Coronavirus Updates – NPR

Congressional leaders held a candlelight vigil outside the U.S. Capitol in Washington, D.C. on February 23, 2021 to mark the more than 500,000 U.S. deaths due to the COVID-19 pandemic. COVID-19 was the third leading underlying cause of death in 2020, according to a study published by the Centers for Disease Control and Prevention on Wednesday. Al Drago/Getty Images hide caption

toggle caption

Al Drago/Getty Images

Congressional leaders held a candlelight vigil outside the U.S. Capitol in Washington, D.C. on February 23, 2021 to mark the more than 500,000 U.S. deaths due to the COVID-19 pandemic. COVID-19 was the third leading underlying cause of death in 2020, according to a study published by the Centers for Disease Control and Prevention on Wednesday.

Al Drago/Getty Images

COVID-19 was the third underlying cause of death in 2020 after heart disease and cancer, the Centers for Disease Control and Prevention confirmed on Wednesday.

A pair of reports published in the CDC’s Morbidity and Mortality Weekly Report sheds new light on the approximately 375,000 U.S. deaths attributed to COVID-19 last year, and highlights the pandemic’s disproportionate impact on communities of color — a point CDC Director Rochelle Walensky emphasized at a White House COVID-19 Response Team briefing on Wednesday.

She said deaths related to COVID-19 were higher among American Indian and Alaskan Native persons, Hispanics, Blacks and Native Hawaiian and Pacific Islander persons than whites. She added that “among nearly all of these ethnic and racial minority groups, the COVID-19 related deaths were more than double the death rate of non-Hispanic white persons.”

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“The data should serve again as a catalyst for each of us [to] continue to do our part to drive down cases and reduce the spread of COVID-19, and get people vaccinated as soon as possible,” she said.

The reports examine data from U.S. death certificates and the National Vital Statistics System to draw conclusions about the accuracy of the country’s mortality surveillance and shifts in mortality trends.

One found that the age-adjusted death rate rose by 15.9% in 2020, its first increase in three years.

Overall death rates were highest among Black and American Indian/Alaska Native people, and higher for elderly people than younger people, according to the report. Age-adjusted death rates were higher among males than females.

COVID-19 was reported as either the underlying cause of death or a contributing cause of death for some 11.3% of U.S. fatalities, and replaced suicide as one of the top 10 leading causes of death.

Similarly, COVID-19 death rates were highest among individuals ages 85 and older, with the age-adjusted death rate higher among males than females. The COVID-19 death rate was highest among Hispanic and American Indian/ Alaska Native people.

Researchers emphasized that these death estimates are provisional, as the final annual mortality data for a given year are typically released 11 months after the year ends. Still, they said early estimates can give researchers and policymakers an early indication of changing trends and other “actionable information.”

“These data can guide public health policies and interventions aimed at reducing numbers of deaths that are directly or indirectly associated with the COVID-19 pandemic and among persons most affected, including those who are older, male, or from disproportionately affected racial/ethnic minority groups,” they added.

The other study examined 378,048 death certificates from 2020 that listed COVID-19 as a cause of death. Researchers said their findings “support the accuracy of COVID-19 mortality surveillance” using official death certificates, noting the importance of high-quality documentation and countering concerns about deaths being improperly attributed to the pandemic.

Among the death certificates reviewed, just 5.5% listed COVID-19 and no other conditions. Among those that included at least one other condition, 97% had either a co-occurring diagnosis of a “plausible chain-of-event” condition such as pneumonia or respiratory failure, a “significant contributing” condition such as hypertension or diabetes, or both.

“Continued messaging and training for professionals who complete death certificates remains important as the pandemic progresses,” researchers said. “Accurate mortality surveillance is critical for understanding the impact of variants of SARS-CoV-2, the virus that causes COVID-19, and of COVID-19 vaccination and for guiding public health action.”

Officials at the Wednesday briefing continued to call on Americans to practice mitigation measures and do their part to keep themselves and others safe, noting that COVID-19 cases continue to rise even as the country’s vaccine rollout accelerates.

The 7-day average of new cases is just under 62,000 cases per day, Walensky said, marking a nearly 12% increase from the previous 7-day period. Hospitalizations are also up at about 4,900 admissions per day, she added, with the 7-day average of deaths remaining slightly above 900 per day.

Dr. Celine Gounder, an infectious disease specialist at New York University who served as a COVID-19 adviser on the Biden transition team, told NPR’s Morning Edition on Wednesday that she remains concerned about the rate of new infections, even as the country has made considerable progress with its vaccination rollout.

She compared vaccines to a raincoat and an umbrella, noting they provide protection during a rainstorm but not in a hurricane

“And we’re really still in a COVID hurricane,” Gounder said. “Transmission rates are extremely high. And so even if you’ve been vaccinated, you really do need to continue to be careful, avoid crowds and wear masks in public.”

COVID-19 Daily Update 3-30-2021 – West Virginia Department of Health and Human Resources

The West Virginia Department of Health and Human Resources (DHHR) reports as of March 30, 2021, there have been 2,438,840 total confirmatory laboratory results received for COVID-19, with 141,332 total cases and 2,640 total deaths.

DHHR has confirmed the deaths of a 74-year old female from Raleigh County and an 84-year old female from Raleigh County.

“These are holes in our hearts and communities,” said Bill J. Crouch, DHHR Cabinet Secretary. “Our lives have been forever changed by the pandemic.”

CASES PER COUNTY: Barbour (1,310), Berkeley (10,632), Boone (1,740), Braxton (837), Brooke (2,061), Cabell (8,385), Calhoun (243), Clay (389), Doddridge (514), Fayette (2,981), Gilmer (730), Grant (1,167), Greenbrier (2,498), Hampshire (1,609), Hancock (2,630), Hardy (1,391), Harrison (5,144), Jackson (1,775), Jefferson (4,009), Kanawha (13,045), Lewis (1,099), Lincoln (1,355), Logan (2,935), Marion (3,924), Marshall (3,171), Mason (1,868), McDowell (1,411), Mercer (4,385), Mineral (2,640), Mingo (2,301), Monongalia (8,633), Monroe (1,015), Morgan (1,007), Nicholas (1,378), Ohio (3,833), Pendleton (666), Pleasants (817), Pocahontas (616), Preston (2,714), Putnam (4,542), Raleigh (5,494), Randolph (2,462), Ritchie (639), Roane (523), Summers (725), Taylor (1,149), Tucker (516), Tyler (657), Upshur (1,780), Wayne (2,741), Webster (442), Wetzel (1,165), Wirt (368), Wood (7,438), Wyoming (1,823).

Delays may be experienced with the reporting of information from the local health department to DHHR. As case surveillance continues at the local health department level, it may reveal that those tested in a certain county may not be a resident of that county, or even the state as an individual in question may have crossed the state border to be tested. Such is the case of Barbour, Pleasants and Tucker counties in this report.

West Virginians may pre-register for their COVID-19 vaccination at vaccinate.wv.gov. The COVID-19 dashboard located at www.coronavirus.wv.gov shows the total number of vaccines administered. Please see the vaccine summary tab for more detailed information. 

Free COVID-19 testing is available today in Barbour, Berkeley, Boone, Brooke, Clay, Fayette, Grant, Hardy, Jefferson, Lincoln, Logan, Mason, Mineral, Mingo, Morgan, Nicholas, Putnam, Raleigh and Wyoming counties.

March 30

Barbour County

9:00 AM – 11:00 AM, Barbour County Health Department, 109 Wabash Avenue, Philippi, WV 

3:00 PM – 7:00 PM, Junior Volunteer Fire Department, 331 Row Avenue, Junior, WV 

Berkeley County 

1:00 PM – 5:00 PM, Shenandoah Community Health, 99 Tavern Road, Martinsburg, WV

4:30 PM – 8:00 PM, Dorothy McCormack Building, 2000 Foundation Way, Martinsburg, WV 

10:00 AM – 6:00 PM, 891 Auto Parts Place, Martinsburg, WV

10:00 AM – 6:00 PM, Ambrose Park, 25404 Mall Drive, Martinsburg, WV

Boone County

Brooke County

Clay County

1:00 PM – 3:00 PM, Clay County Health Department, 452 Main Street, Clay, WV

Fayette County

9:00 AM – 11:00 AM, Fayette County Health Department, 202 Church Street, Fayetteville, WV

10:00 AM – 2:00 PM, Ruby Welcome Center, 55 Hazel Lane, Mount Hope, WV

Grant County

Hardy County

Jefferson County

10:00 AM – 6:00 PM, Hollywood Casino, 750 Hollywood Drive, Charles Town, WV 

10:00 AM – 6:00 PM, Shepherd University Wellness Center Parking Lot, 164 University Drive, Shepherdstown, WV

Lincoln County

Logan County

Mason County

Mineral County

10:00 AM – 6:00 PM, Mineral County Health Department, 541 Harley O Staggers Drive, Keyser, WV

Mingo County

3:00 PM – 7:00 PM, Kermit Fire Department, 49 Main Street, Kermit, WV

Morgan County

10:00 AM – 6:00 PM, Valley Health War Memorial Hospital, 1 Health Way, Berkeley Spring, WV

Nicholas County

Putnam County

9:00 AM – 1:00 PM, Liberty Square, 613 Putnam Village, Hurricane, WV

Raleigh County

Wyoming County

11:00 AM – 3:00 PM, Wyoming County Fire Department, 12 Park Street, Pineville, WV

Monoclonal Antibody Treatments Continue to Help Reduce COVID-19 Hospitalizations – SCDHEC

FOR IMMEDIATE RELEASE:
March 29, 2021

COLUMBIA, S.C. — In South Carolina, hospitalizations due to COVID-19 have begun to decline, in part because of the purposeful prioritization of high-risk individuals in the state’s vaccination plan, as well as the success of the state’s monoclonal antibody program.

While monoclonal antibody treatments are currently only approved for emergency use, data shows they help reduce hospitalizations and emergency room visits due to COVID-19. State health officials estimate that well over 1,000 hospital admissions have been avoided and more than 100 COVID-19-related deaths have been prevented in South Carolina due to monoclonal antibody treatments. 

Monoclonal antibodies are a type of treatment doctors have been using for COVID-19 patients since November 2020, when the U.S. Food and Drug Administration issued Emergency Use Authorizations for two types of monoclonal antibody treatments: bamlanivimab and casirivimab plus imdevimab. These antibodies work by directly blocking the effect of the COVID-19 virus in patients that are already infected. More than 9,500 South Carolinians have received monoclonal antibody treatments.

“Monoclonal antibodies are laboratory-designed antibodies that can detect the SARS-CoV-2 virus, which is the virus that causes COVID-19, and can help your immune system get rid of it,” said Dr. Jonathan Knoche, DHEC’s immunization medical consultant. “Health care providers typically use these treatments for patients with mild to moderate COVID-19 symptoms but who are at high risk for developing severe complications from the virus.”

While the federal government recently announced it was suspending the distribution of bamlanivimab because COVID-19 variants were resistant to bamlanivimab alone, other monoclonal antibody treatments are showing effectiveness against variant strains of the virus. Public health officials in this country around the world are continuing to learn more about recently identified COVID-19 variants. 

“Even as more and more people are receiving their COVID-19 vaccines, monoclonal antibody treatments are still an important treatment option, especially as variants become more prevalent,” said Dr. Rick Foster, DHEC Public Health Consultant. “We are continuing to actively support existing monoclonal antibody treatment sites and we’re working to add more sites across the state that offer this outpatient infusion therapy. This treatment has been very effective in reducing risk for more severe illness and hospitalization in high risk patients with symptomatic COVID-19.” 

Doctors determine whether a monoclonal antibody treatment is appropriate for a patient after the patient is first diagnosed with COVID-19. The sooner a high-risk individual who has tested positive begins receiving the treatment, the more successful it is in reducing the patient’s symptoms from COVID-19. 

Recently, a private pediatric practice in Charleston County treated two high-risk adolescent COVID-19 patients with monoclonal antibodies. Because current COVID-19 vaccines aren’t recommended for anyone under the age of 16, monoclonal antibody treatment is an essential resource in treating high-risk children and teenagers who can’t protect themselves by way of vaccination.

The treatment is a single-dose IV infusion, meaning the patient receives their full dose of the antibody treatment in one sitting.

DHEC is working with partners to expand the number of monoclonal antibody infusion locations in the state, and the agency is also reaching out to providers that offer home infusions to expand this treatment, at a doctor’s recommendation, to homebound individuals. DHEC maintains a map of locations currently offering this treatment, and has developed a web-based application to assist in the screening and timely referral of  patients that are eligible for this treatment. However, it’s important to remember that monoclonal antibody treatment must be recommended by a healthcare professional; an individual can’t simply show up at one of these locations without a referral.

A health care location interested in becoming a monoclonal antibody infusion site should contact covid19drug@dhec.sc.gov.

For the latest COVID-19 information, visit scdhec.gov/COVID19.

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COVID-19 Daily Update 3-28-2021 – West Virginia Department of Health and Human Resources

The West
Virginia Department of Health and Human Resources
(DHHR) reports
as of March 28, 2021, there have been 2,422,755
total confirmatory laboratory results received
for COVID-19, with 140,613 total cases and 2,634 total deaths.

 

DHHR has confirmed the deaths of a 75-year old male from Kanawha
County, a 74-year old female from Putnam County, and a 71-year old female from
Logan County.

“As
many of us are ready for COVID-19 to go away, we must realize it is still spreading
in our communities,” said Bill J. Crouch, DHHR Cabinet Secretary. “Please
continue prevention efforts to combat this horrible virus and join with me in
extending our condolences to these families.”

 

CASES PER
COUNTY
: Barbour (1,313), Berkeley
(10,568), Boone (1,734), Braxton (833), Brooke (2,060), Cabell (8,359), Calhoun
(242), Clay (388), Doddridge (512), Fayette (2,953), Gilmer (728), Grant
(1,163), Greenbrier (2,485), Hampshire (1,604), Hancock (2,627), Hardy (1,381),
Harrison (5,115), Jackson (1,764), Jefferson (3,963), Kanawha (12,944), Lewis
(1,093), Lincoln (1,351), Logan (2,930), Marion (3,896), Marshall (3,167),
Mason (1,859), McDowell (1,407), Mercer (4,372), Mineral (2,632), Mingo
(2,292), Monongalia (8,612), Monroe (1,011), Morgan (1,004), Nicholas (1,369),
Ohio (3,813), Pendleton (659), Pleasants (819), Pocahontas (615), Preston
(2,701), Putnam (4,506), Raleigh (5,411), Randolph (2,449), Ritchie (639),
Roane (520), Summers (723), Taylor (1,143), Tucker (516), Tyler (657), Upshur
(1,770), Wayne (2,737), Webster (441), Wetzel (1,158), Wirt (368), Wood
(7,422), Wyoming (1,814).

Delays may
be experienced with the reporting of information from the local health
department to DHHR. As case surveillance continues at the local health
department level, it may reveal that those tested in a certain county may not
be a resident of that county, or even the state as an individual in question
may have crossed the state border to be tested
.

 

West Virginians may pre-register for their COVID-19
vaccination at
vaccinate.wv.gov. The COVID-19 dashboard located at www.coronavirus.wv.gov shows the total number of vaccines administered.
Please see the vaccine summary tab for more detailed information.

 

Free COVID-19 testing is available today
in Doddridge, Nicholas, and Webster counties:

Doddridge
County


10:00 AM –
6:00 PM, Doddridge County Park, The Barn, 1252 Snowbird Road S., West Union, WV

Nicholas County

12:00 PM – 3:00 PM, Richwood City Hall, 6
White Avenue, Richwood, WV
(pre-registration: https://wv.getmycovidresult.com/)

Webster
County
12:00 PM – 4:00 PM, Hacker Valley Elementary School, 60
School Loop Road, Hacker Valley, WV
(pre-registration:
https://wv.getmycovidresult.com/)

 

Monday, March 29 testing events:

Berkeley County
10:00 AM – 6:00 PM, 891 Auto Parts Place, Martinsburg, WV


10:00 AM – 6:00 PM, Ambrose Park, 25404 Mall Drive, Martinsburg, WV

Barbour County

9:00 AM – 11:00 AM, Barbour County Health Department, 109
Wabash Avenue, Philippi, WV

1:00 PM – 5:00 PM, Junior Volunteer Fire Department, 331
Row Avenue, Junior, WV

Boone County
12:00 PM – 6:00 PM, Boone County
Health Department, 213 Kenmore Dr., Danville, WV

Jefferson County
10:00 AM – 6:00 PM, Hollywood
Casino, 750 Hollywood Drive, Charles Town, WV

10:00 AM – 6:00 PM, Shepherd University Wellness Center
Parking Lot, 164 University Drive, Shepherdstown, WV

Lincoln County

9:00 AM – 3:00 PM, Lincoln County Health Department, 8008 Court
Avenue, Hamlin, WV (pre-registration:
https://wv.getmycovidresult.com/)

Marshall County

11:00 AM – 3:00 PM,
Marshall County Health Department, 513 6
th Street, Moundsville, WV (pre-registration:
https://wv.getmycovidresult.com/)

Mineral County

10:00 AM – 6:00 PM, Mineral County Health Department, 541
Harley O Staggers Drive, Keyser, WV

Monongalia County

9:00 AM – 11:00 AM, WVU Recreation Center, Lower Level,
2001 Rec Center Drive, Morgantown, WV

Nicholas County

3:00 PM – 7:00 PM, St. Luke’s United Methodist Church,
18001 West Webster Road, Craigsville, WV
(pre-registration:
https://wv.getmycovidresult.com/)

Ohio County

11:00 AM – 4:00 PM, Wheeling Island Fire Station #5, 11
North Wabash Street, Wheeling, WV

Pendleton County

11:00 AM – 5:00 PM, Pendleton County Health Department,
273 Mill Road, Franklin, WV
(pre-registration:
https://wv.getmycovidresult.com/)

Preston County

4:00 PM – 5:30 PM, Terra Alta EMS, 1124 East State
Avenue, Terra Alta, WV
(pre-registration:
https://wv.getmycovidresult.com/)

Raleigh County

10:00 AM – 2:00 PM, Beckley-Raleigh County Health Department, 1602
Harper Road, Beckley, WV (pre-registration:
https://wv.getmycovidresult.com/)

Wayne County

10:00 AM – 2:00 PM, Wayne Community Center, 11580 Route 152, Wayne, WV

 

For more testing opportunities, including
pharmacy testing, visit
https://dhhr.wv.gov/COVID-19/pages/testing.aspx.

Comparing the COVID-19 vaccines – USA TODAY

The U.S. Food and Drug Administration has granted emergency use authorizations for three COVID-19 vaccines so far. We compare the different available shots, and several still in the approval process for use in the Untied States.

Which vaccines are approved already?

The Pfizer-BioNTech and Moderna vaccines have been available to Americans since December.

A vaccine developed by Janssen Pharmaceuticals, a Johnson & Johnson company, was authorized in late February.

AstraZeneca, working in collaboration with Oxford University, is expected to request authorization from the FDA for their vaccine within a few weeks, and Novavax is expected to follow with its application later in April or May.

What technology does each vaccine use?

Many vaccines use weakened or inactivated versions or components of the disease-causing pathogen to stimulate the body’s immune response. However, the vaccines developed by PfizerBioNTech and Moderna take advantage of messenger RNA (mRNA), which instructs cells to produce a protein on the surface of the virus. The immune system recognizes those vaccine-triggered spike proteins as invaders and creates antibodies to block future attacks of the virus that causes COVID-19. 

More: How mRNA vaccines work

The Johnson & Johnson vaccine takes the more traditional approach. Adenoviruses are common viruses that typically cause colds or flulike symptoms. The J&J team used a modified adenovirus that can enter cells but can’t replicate inside them or cause illness. The vaccine contains this modified virus that delivers a spike protein, activating the immune system.

The AstraZeneca-Oxford vaccine also uses an adenovirus, in this case a monkey virus that the human immune system doesn’t recognize. It delivers the gene that encodes the spike protein to human cells rather than the protein itself.

Novavax’s vaccine works by delivering lab-grown spike proteins along with a compound that attracts immune cells to the site of the injection.

Where is each vaccine developed?

Moderna, Pfizer, Johnson & Johnson and Novavax are all US-based companies. The J&J vaccine was developed by researchers at Harvard University and in Leiden, Netherlands. Moderna, based in Cambridge, Massachusetts, developed its vaccine in collaboration with U.S. government scientists. The AstraZeneca vaccine was developed by the University of Oxford and its spin-off company, Vaccitech. BioNTech, a biotechnology company based in Mainz, Germany, partnered with Pfizer to test and produce its vaccine. 

How many shots do I need to get?

All the vaccines except the one from Johnson & Johnson require two shots to train the immune system well enough to fight the coronavirus.

After one dose of the Pfizer vaccine, for example, its effectiveness against illness reached up to 52% after 12 days, and up to 95% a week after the second dose, according to a study. That’s why it’s important for people who have received only one dose to keep practicing the usual precautions, like wearing a mask and maintaining social distance. The Centers for Disease Control and Prevention suggests getting the second shot as close to the recommended dose schedule as possible, but not earlier. 

The J&J vaccine is a one-dose vaccine. However, the company is also testing a two-dose regimen, with the two shots given eight weeks apart.

How much do vaccines cost per dose?

Moderna and PfizerBioNTech so far have been selling the majority of their doses to high-income countries, including the U.S., Canada and the European Union. These are also the most expensive vaccines. 

AstraZeneca-Oxford has the cheapest of the five vaccines. The company has committed not to profit from it while the pandemic lasts. 

There have been reports of different prices being paid by governments in different parts of the world.

How much has the U.S. spent on each?

Overall, the U.S. government has made vaccine deals totaling more than $9 billion with multiple private companies, but the deals vary in size.  

The pharmaceutical giant Merck will also receive $105 million from the government to help produce the J&J vaccine.

How many doses of each will the U.S. get?

President Joe Biden said in March that the U.S. will have enough vaccines for every adult in the U.S. by the end of May. The main challenge will now be to distribute the doses and to convince hesitant people to take them.

Do we have enough vaccine to go around?

The U.S. has purchased over 1 billion doses of vaccine for a population of 330 million.

According to a ONE’s Policy team study, the world’s richest countries have collectively  bought 1 billion more doses than their citizens need. The rest of the world has been able to secure only 2.5 billion doses – not enough to vaccinate their populations.

The excess doses purchased by rich countries alone would be sufficient to vaccinate the entire adult population of Africa. In many developing countries, widespread vaccination coverage might not be achieved before 2023, unless countries like the U.S. share their surplus.

Do temperature and storage requirements differ between vaccines?

The mRNA vaccines from PfizerBioNTech and Moderna require a complicated cold chain to safely distribute them. During each part of the process, the vaccine boxes must be kept at exactly the right temperature. The Pfizer-BioNTech vaccine must be used within five days after the transfer to refrigerator. Moderna vaccine can last in the refrigerator for a month and the J&J vaccine can be stored in the refrigerator for at least 3 months.

More: How coronavirus vaccines will be shipped and distributed using ‘cold chain’ technologies

The Novavax and AstraZeneca-Oxford vaccines can be stored in normal refrigerators for up to six months.

What about safety and side effects?

Serious side effects, allergic reactions or adverse incidents stemming from the vaccines are rare, though in clinical trials, mild to moderate side effects were common. The most common complaints were pain at the injection site, fatigue and aching muscles and joints. People with a history of anaphylaxis or severe allergic reactions should inform health professionals before they are vaccinated, and anyone with an allergy to one of the vaccine’s ingredients should consult a healthcare provider before getting vaccinated.

Do some vaccines work better than others?

Public health experts emphasize that all the vaccines are effective, particularly at preventing serious disease.

Because trials were conducted differently at different times, effectiveness figures cannot be directly compared.

In its large-scale trial, the Pfizer-BioNTech vaccine was shown to prevent 95% of symptomatic COVID-19, just 1 percentage point more than Moderna’s. Both vaccines appear to be equally protective across age groups and racial and ethnic groups. 

The J&J vaccine was shown to be 72% effective in moderate to severe disease and 85% effective in preventing the most severe disease. 

AstraZeneca-Oxford vaccine was 76% effective at preventing symptomatic COVID-19 two weeks after the second dose and was 100% effective in stopping severe disease and hospitalization in a U.S.-based clinical trial, according to the company.

In a trial in the U.K., two doses of the Novavax vaccine were shown to be nearly 90% effective against symptomatic COVID-19, though just 50% effective in a smaller South African trial.

What do we still need to know?

It remains unclear how long any of these vaccines will continue to protect people. While the small number of early trial participants have maintained levels of protective antibodies for nearly a year, companies will continue to follow volunteers for two years to see if their immunity begins to wane. Periodic booster shots to extend immunity and/or protect against new variants of the virus might be needed.

And although a growing body of research suggests that vaccines will also protect people from passing on the virus – even if they don’t have symptoms – that remains to be confirmed.

Published

Updated

Coronavirus (COVID-19) Update: March 26, 2021 – FDA.gov

For Immediate Release:

The U.S. Food and Drug Administration today announced the following actions taken in its ongoing response effort to the COVID-19 pandemic:

  • On March 23, 2021, the FDA issued an emergency use authorization (EUA) to the Twist Bioscience Corporation for their SARS-CoV-2 NGS Assay. The SARS-CoV-2 NGS Assay is a next-generation sequencing (NGS) based test for the identification of SARS-CoV-2 RNA from respiratory samples, such as nose or throat swabs and washes, from people who are suspected of having COVID-19.  This is the second whole genome sequencing diagnostic test for the qualitative detection of SARS-CoV-2 RNA authorized by the FDA. The test can be performed in laboratories certified under the Clinical Laboratory Improvement Amendments of 1988 (CLIA) that meet the requirements to perform high-complexity testing. 
  • In a March 24 Consumer Update, the FDA answers common questions about COVID-19 vaccines. The FDA is publicly sharing information about the evidence behind emergency use authorizations for COVID-19 vaccines so everyone can see it for themselves. Read the full article: Learn More About COVID-19 Vaccines From the FDA.
  • Testing updates:
    • As of today, 348 tests and sample collection devices are authorized by the FDA under emergency use authorizations (EUAs). These include 258 molecular tests and sample collection devices, 74 antibody and other immune response tests, and 16 antigen tests. There are 42 molecular authorizations that can be used with home-collected samples. There is one molecular prescription at-home test, two antigen prescription at-home tests, one over-the-counter (OTC) at-home antigen test, and one OTC molecular test.

The FDA, an agency within the U.S. Department of Health and Human Services, protects the public health by assuring the safety, effectiveness, and security of human and veterinary drugs, vaccines and other biological products for human use, and medical devices. The agency also is responsible for the safety and security of our nation’s food supply, cosmetics, dietary supplements, products that give off electronic radiation, and for regulating tobacco products.

###


Inquiries

Consumer:
888-INFO-FDA


Related Information

Age groups that sustain resurging COVID-19 epidemics in the United States – Science

Age-specific contact

How can the resurgent epidemics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during 2020 be explained? Are they a result of students going back to school? To address this question, Monod et al. created a contact matrix for infection based on data collected in Europe and China and extended it to the United States. Early in the pandemic, before interventions were widely implemented, contacts concentrated among individuals of similar age were the highest among school-aged children, between children and their parents, and between middle-aged adults and the elderly. However, with the advent of nonpharmaceutical interventions, these contact patterns changed substantially. By mid-August 2020, although schools reopening facilitated transmission, the resurgence in the United States was largely driven by adults 20 to 49 years of age. Thus, working adults who need to support themselves and their families have fueled the resurging epidemics in the United States.

Science, this issue p. eabe8372

Structured Abstract

INTRODUCTION

After initial declines, in mid-2020, a sustained resurgence in the transmission of novel coronavirus disease (COVID-19) occurred in the United States. Throughout the US epidemic, considerable heterogeneity existed among states, both in terms of overall mortality and infection, but also in the types and stringency of nonpharmaceutical interventions. Despite these stark differences among states, little is known about the relationship between interventions, contact patterns, and infections, or how this varies by age and demographics. A useful tool for studying these dynamics is individual, age-specific mobility data. In this study, we use detailed mobile-phone data from more than 10 million individuals and establish a mechanistic relationship between individual contact patterns and COVID-19 mortality data.

RATIONALE

As the pandemic progresses, disease control responses are becoming increasingly nuanced and targeted. Understanding fine-scale patterns of how individuals interact with each other is essential to mounting an efficient public health control program. For example, the choice of closing workplaces, closing schools, limiting hospitality sectors, or prioritizing vaccination to certain population groups should be informed by the demographics currently driving and sustaining transmission. To develop the tools to answer such questions, we introduce a new framework that links mobility to mortality through age-specific contact patterns and then use this rich relationship to reconstruct accurate transmission dynamics (see figure panel A).

RESULTS

We find that as of 29 October 2020, adults aged 20 to 34 and 35 to 49 are the only age groups that have sustained SARS-CoV-2 transmission with reproduction numbers (transmission rates) consistently above one. The high reproduction numbers from adults are linked both to rebounding mobility over the summer and elevated transmission risks per venue visit among adults aged 20 to 49. Before school reopening, we estimate that 75 of 100 COVID-19 infections originated from adults aged 20 to 49, and the share of young adults aged 20 to 34 among COVID-19 infections was highly variable geographically. After school reopening, we reconstruct relatively modest shifts in the age-specific sources of resurgent COVID-19 toward younger individuals, with less than 5% of SARS-CoV-2 transmissions attributable to children aged 0 to 9 and less than 10% attributable to early adolescents and teenagers aged 10 to 19. Thus, adults aged 20 to 49 continue to be the only age groups that contribute disproportionately to COVID-19 spread relative to their size in the population (see figure panel B). However, because children and teenagers seed infections among adults who are more transmission efficient, we estimate that overall, school opening is indirectly associated with a 26% increase in SARS-CoV-2 transmission.

CONCLUSION

We show that considering transmission through the lens of contact patterns is fundamental to understanding which population groups are driving disease transmission. Over time, the share of age groups among reported deaths has been markedly constant, and the data provide no evidence that transmission shifted to younger age groups before school reopening, and no evidence that young adults aged 20 to 34 were the primary source of resurgent epidemics since the summer of 2020. Our key conclusion is that in locations where novel, highly transmissible SARS-CoV-2 lineages have not yet become established, additional interventions among adults aged 20 to 49, such as mass vaccination with transmission-blocking vaccines, could bring resurgent COVID-19 epidemics under control and avert deaths.

Model developed to estimate the contribution of age groups to resurgent COVID-19 epidemics in the United States.

(A) Model overview. (B) Estimated contribution of age groups to SARS-CoV-2 transmission in October.

Abstract

After initial declines, in mid-2020 a resurgence in transmission of novel coronavirus disease (COVID-19) occurred in the United States and Europe. As efforts to control COVID-19 disease are reintensified, understanding the age demographics driving transmission and how these affect the loosening of interventions is crucial. We analyze aggregated, age-specific mobility trends from more than 10 million individuals in the United States and link these mechanistically to age-specific COVID-19 mortality data. We estimate that as of October 2020, individuals aged 20 to 49 are the only age groups sustaining resurgent SARS-CoV-2 transmission with reproduction numbers well above one and that at least 65 of 100 COVID-19 infections originate from individuals aged 20 to 49 in the United States. Targeting interventions—including transmission-blocking vaccines—to adults aged 20 to 49 is an important consideration in halting resurgent epidemics and preventing COVID-19–attributable deaths.

Following worldwide spread of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the implementation of large-scale nonpharmaceutical interventions has led to sustained declines in the number of reported SARS-CoV-2 infections and deaths from COVID-19 (1, 2). However, since mid-June 2020, the daily number of reported COVID-19 cases has been resurging in Europe and North America and in the United States (US) alone surpassed 40,000 daily reported cases on 26 June and 100,000 on 4 November 2020 (3). Demographic analyses have shown that the share of individuals aged 20 to 29 among reported cases increased most, suggesting that young adults may be driving resurging epidemics (4). However, reported COVID-19 case data may not be a reliable indicator of disease spread owing to the large proportion of asymptomatic COVID-19, increased testing, and changing testing behavior (5). Here, we use detailed, longitudinal, and age-specific population mobility and COVID-19 mortality data to estimate how nonpharmaceutical interventions, changing contact intensities, age, and other factors have interacted and led to resurgent disease spread. We test previous claims that resurgent COVID-19 is a result of increased spread from young adults, identify the population age groups driving SARS-CoV-2 spread across the US through 29 October 2020, and quantify changes in transmission dynamics since schools reopened.

Similar to many other respiratory diseases, the spread of SARS-CoV-2 occurs primarily through close human contact, which, at a population level, is highly structured (6). Prior to the implementation of COVID-19 interventions, contacts concentrated among individuals of similar age, were highest among school-aged children and teens, and were also common between children and teens and their parents and between middle-aged adults and the elderly (6). Since the beginning of the pandemic, these contact patterns have changed substantially (79). In the US, the Berkeley Interpersonal Contact Study indicates that in late March 2020, after stay-at-home orders were issued, the average number of daily contacts made by a single individual, also known as contact intensity, dropped to four or fewer contacts per day (9). Data from China show that infants and school-aged children and teens had almost no contact to similarly aged children and teens in the first weeks after stay-at-home orders and reduced contact intensities with older individuals (7). However, detailed human contact and mobility data have remained scarce, especially longitudinally, although such data are essential to better understand the engines of COVID-19 transmission (10).

Cell-phone data suggest similar rebounds in mobility across age groups

We compiled a national-level, aggregate mobility data set using cell phone data from >10 million individuals with Foursquare’s location technology, Pilgrim (11), which leverages a wide variety of mobile device signals to pinpoint the time, duration, and location of user visits to locations such as shops, parks, or universities. Unlike the population-level mobility trends published by Google from cell phone geolocation data (12), the data are disaggregated by age. User venue visits were aggregated and projected to estimate, for each state, and two metropolitan areas, daily percentage changes in venue visits for individuals aged 18 to 24, 25 to 34, 35 to 44, 45 to 54, 55 to 64, and 65+ years relative to the baseline period 3 to 9 February 2020 (figs. S1 and S2 and supplementary materials).

Across the US as a whole, the mobility trends indicate substantial initial declines in venue visits, followed by a subsequent rebound for all age groups (Fig. 1A and fig. S1). During the initial phase of epidemic spread, trends declined most strongly among individuals aged 18 to 24 years across almost all states and metropolitan areas and subsequently tended to increase most strongly among individuals aged 18 to 24 in the majority of states and metropolitan areas (fig. S3), consistent with reopening policies for restaurants, night clubs, and other venues (10, 13, 14). Yet, considering both the initial decline and subsequent rebound until 28 October 2020, our data indicate that mobility levels among individuals aged <35 years have not increased above those observed among older individuals (Fig. 1B and fig. S3).

Fig. 1 Mobility trends and estimated time evolution of contact intensities in the United States.

(A) National, longitudinal mobility trends for individuals aged 18 to 24, 25 to 34, 35 to 44, 45 to 54, 55 to 64, and 65+, relative to the baseline period 3 February to 9 February 2020. Projected per capita visits standardized daily visit volumes by the population size in each location and age group. The vertical dashed lines show the dip and rebound dates since mobility trends began to decrease and increase, which were estimated from the time-series data. (B) One-week average of age-specific mobility trends between 22 October 2020 and 28 October 2020 across the United States. (C) Inferred time evolution of contact intensities in California, calculated with Eq. 4.

Mobile-phone signals are challenging to analyze, owing e.g., to daily fluctuations in the user panel providing location data, imprecise geolocation measurements, and changing user behavior (15). We cross-validated the inferred mobility trends against age-specific mobility data from a second mobile phone intelligence provider, Emodo. This second data set quantified the daily proportions of age-stratified users who spent time outside their home location and also showed no evidence for faster mobility rebounds among young adults aged <35 years as compared to older age groups (see supplementary materials). Although other age-specific behavioral differences in, for example, consistent social distancing, mask use, duration of visits, or types of venues visited could also explain age-specific differences in transmission risk (10, 13, 14, 16, 17), these observations nonetheless led us to hypothesize that the resurgent epidemics in the US may not be driven by increased transmission from young adults aged 20 to 34.

Reconstructing human contact patterns and SARS-CoV-2 transmission

To test this hypothesis and disentangle the various factors, we incorporated the mobility data into a Bayesian contact-and-infection model that describes time-changing contact and transmission dynamics at state- and metropolitan area–level across the US. For the time period prior to changes in mobility trends, we used data from pre-COVID-19 contact surveys (6) and each location’s age composition and population density to predict contact intensities between individuals grouped in 5-year age bands (figs. S4 to S6), similar to (18). On weekends, contact intensities between school-aged children and teens are lower than on weekdays, whereas intergenerational contact intensities are higher. In the model, the observed age-specific mobility trends of Fig. 1 are then used to estimate in each location (state or metropolitan area) daily changes in age-specific contact intensities for individuals aged 20 and above. For younger individuals, for whom mobility trends are not recorded, contact intensities during school closure periods were set to estimates from two contact surveys conducted after COVID-19 emergence (7, 8). After school reopening in August 2020, relative changes in disease-relevant contacts from and to children and teens aged 0 to 19 were estimated through the model. Contact intensities between children and teens were modeled and estimated separately, to account for potentially lower or higher disease-relevant contacts between children and teens in the context of existing nonpharmaceutical interventions within and outside schools (see materials and methods). As in (19), the model further incorporates random effects in space, in time, and by age to allow for unobserved, potential age-specific factors that could modulate disease-relevant contact patterns. These random effects enabled us to identify signatures of age-specific, behavioral drivers of SARS-CoV-2 transmission beyond the mobility data in Fig. 1 that may underlie the highly heterogeneous epidemic trajectories across the US. Finally, the reconstructed contact intensities are used in the model to estimate the rate of SARS-CoV-2 transmission, and subsequently infections and deaths. The summary figure provides a model overview, and full details are in the supplementary materials.

Estimated disease dynamics closely reproduce age-specific COVID-19–attributable death counts

The contact-and-infection model was fitted to the Foursquare mobility trends and to age-specific, COVID-19–attributed mortality time-series data, which we recorded daily from publicly available sources in 42 US states, the District of Columbia, and New York City since 15 March 2020 (fig. S7 and supplementary materials). Our overall rationale was that, reflecting the highly structured nature of human contacts, transmissions from age groups are received by specific other age groups, and mortality accrues in the age groups receiving infections. Thus, working back from the time evolution of reliably documented, age-specific COVID-19–attributable deaths, it is possible to reconstruct age-specific drivers of transmission during particular time periods. Inference was performed in a Bayesian framework and restricted to 38 US states, the District of Columbia, and New York City with at least 300 COVID-19–attributed deaths, giving a total of 8676 observation days. The estimated disease dynamics closely reproduced the age-specific COVID-19 death counts (fig. S8).

Figure 2 illustrates the model fits for New York City, Florida, California, and Arizona, showing that the inferred epidemic dynamics differed markedly across locations. For example, in New York City, the epidemic accelerated for at least 4 weeks since the 10th cumulative death and until age-specific reproduction numbers started to decline, resulting in an epidemic of large magnitude, as shown through the estimated number of infectious individuals (Fig. 2, middle column). Subsequently, we find that reproduction numbers from all age groups were controlled to well below one, except for individuals aged 20 to 49 (Fig. 2, rightmost column), resulting in a steady decline of infectious individuals. In the model, children and teens returned to their pre-lockdown contact intensities on 24 August 2020 or later, depending on when state administrations no longer mandated statewide school closures, and relative decreases or increases in their disease-relevant contact intensities after school reopening were estimated. Concomitantly, reproduction numbers from children aged 0 to 9 and teens aged 10 to 19 increased, but as of the last observation week in October 2020, we find no strong evidence that their reproduction numbers have exceeded one at the population level in most states and metropolitan areas considered. Detailed situation analyses for all locations are presented in the supplementary materials.

Fig. 2 Model fits and key generated quantities for New York City, California, Florida, and Arizona.

(Left) Observed cumulative COVID-19 mortality data (dots) versus posterior median estimate (line) and 95% credible intervals (ribbon). The vertical line indicates the collection start date of age-specific death counts. (Middle) Estimated number of infectious individuals by age (posterior median). (Right) Estimated age-specific effective reproduction number, posterior median estimate (line) and 95% credible intervals (ribbon).

SARS-CoV-2 transmission is sustained primarily from age groups 20 to 49

Figure 3 summarizes the epidemic situation for all states and metropolitan areas evaluated and the age groups that sustain COVID-19 spread. In the last observation week in October 2020, the estimated reproduction number across all locations evaluated was highest from individuals aged 35 to 49 [1.39 (1.34–1.44)] and 20 to 34 [1.29 (1.24–1.36)], and around one for age groups 10 to 19 and 50 to 64 (tables S1 and S2). These trends across age groups were largely consistent over time. The primary mechanisms underlying the high reproduction numbers from 20- to 49-year-olds are that at the population level, adults aged 20 to 49 naturally have most contacts with other adults aged 20 and above, who are more susceptible to COVID-19 than younger individuals, paired with increasing mobility trends for these age groups since April 2020 (Fig. 1 and fig. S6). In addition, from the death time-series data, the model inferred characteristic random effect signatures in time and by age across locations (fig. S9), which indicate elevated transmission risk per venue visit for individuals aged 20 to 49 relative to other age groups. Figure S10 visualizes the combined, estimated effects of mobility and behavior on transmission risk and reveals, together with Fig. 3, considerable heterogeneity in age-specific transmission dynamics across locations. Although the model consistently estimates effective reproduction numbers close to or above one across all locations from adults aged 35 to 49, disease dynamics are more variable from young adults aged 20 to 34, with some states (Arizona, Florida, Texas) showing sustained transmission from young adults in May and June, and other states (e.g., Colorado, Illinois, Wisconsin) showing sustained transmission from young adults since August. This suggests that additional interventions among adults aged 20 to 49, including rapid mass vaccination if vaccines prove to block transmission, could bring resurgent COVID-19 epidemics under control.

Fig. 3 Time evolution of estimated age-specific SARS-CoV-2 reproduction numbers across the US.

Each panel shows, for the corresponding location (state or metropolitan area), the estimated posterior probability that the daily effective reproduction number from individuals stratified in seven age groups was below one. Darker colors indicate a low probability that reproduction numbers were below one.

The majority of COVID-19 infections originate from age groups 20 to 49

To quantify how age groups contribute to resurgent COVID-19, it is not enough to estimate reproduction numbers, because reproduction numbers estimate the number of secondary infections per infectious individual, and the number of infectious individuals varies by age as a result of age-specific susceptibility gradients and age-specific contact exposures. We therefore considered the reconstructed transmission flows and calculated from the fitted model the contribution of each age group to new infections in each US location over time. Across all locations evaluated, we estimate that until mid-August 2020, before schools were considered to reopen in the first locations in the model, the percentage contribution to onward spread was 41.1% (40.7 to 41.4%) from individuals aged 35 to 49, compared with 2.1% (1.6 to 2.8%) from individuals aged 0 to 9, 4.0% (3.5 to 4.6%) from individuals aged 10 to 19, 34.7% (33.9 to 35.5%) from individuals aged 20 to 34, 15.3% (14.8 to 15.8%) from individuals aged 50 to 64, 2.5% (2.2 to 2.9%) from individuals aged 65 to 79, and 0.3% (0.3 to 0.3%) from individuals aged 80+ (table S4). Spatially, the contribution of adults aged 35 to 49 was estimated to be markedly homogeneous across states, whereas the estimated contributions of young adults aged 20 to 34 to COVID-19 spread tended to be higher in southern, southwestern, and western regions of the US (Fig. 4), in line with previous observations (4).

Fig. 4 Estimated spatial variation in the share of young adults aged 20 to 34 and adults aged 35 to 49 to COVID-19 spread until mid-August 2020.

Posterior median estimates of the contribution to cumulated SARS-CoV-2 infections until 17 August 2020, prior to school reopening. State-level COVID-19 epidemics not considered in this study are in gray.

No substantial shifts in age-specific disease dynamics over time

Over time, we found that the shares of age groups among the observed COVID-19–attributable deaths were markedly constant (Fig. 5A and fig. S11), which stands in contrast to the large fluctuations in the share of age groups among reported cases (4). To test for shifts in the share of age groups among COVID-19 infections, we next back-calculated the number of expected, age-specific infections per calendar month of aggregated COVID-19–attributable deaths using meta-analysis estimates of the age-specific COVID-19 infection fatality ratio (20). This empirical analysis suggested no statistically significant trends in the share of age groups among COVID19 infections (Fig. 5B and fig. S12), which is further supported by model estimates (Fig. 5C and fig. S13). On the basis of the combined mobility and death data, we find that the reconstructed fluctuations in age-specific reproduction numbers had a relatively modest impact on the contribution of age groups to onward spread over time, and no evidence that young adults aged 20 to 34 were the primary source of resurgent COVID-19 in the US over the summer of 2020. These results underscore that, when testing rates are heterogeneous and not population representative, it is challenging to determine the age-specific pattern of transmission based only on reported case data.

Fig. 5 Share of age groups among COVID-19–attributable deaths and infections in the United States.

(Top) Proportion of monthly observed deaths attributed to COVID-19 by age group. Age-specific COVID-19–attributable deaths were recorded from state or city Departments of Health. Departments of Health used their own age stratification, and the observed data were re-estimated into common age groups across states with a Dirichlet-Multinomial model (see supplementary materials). An asterisk (*) next to a location’s name indicates that there was a statistically significant shift in the share of individuals aged 80+ among deaths in the corresponding location. (Middle) Proportion of monthly reported cases among 20- to 49-year olds. Monthly cases were back-calculated using the meta-analysis infection fatality rate estimates of (20). The figure shows the estimated share of individuals aged 20 to 49 among monthly cases (posterior median: line, 95% credible interval: ribbon). (Bottom) New daily estimated infections by age group in New York City, Florida, California, and Arizona (posterior median).

School reopening has not resulted in substantial increases in COVID-19–attributable deaths

Between August and October 2020, school closure mandates have been lifted in 39 out of 40 of the US locations evaluated in this study and provided 2570 observation days to estimate the impact of school reopening on COVID-19 spread. The following analyses are therefore based on fewer data points than those mentioned previously and rely on mortality figures accrued until the end of October 2020, as well as reported school case data from Florida and Texas, which were used to define lower and upper bounds on cumulative attack rates among children and teens aged 5 to 18 (see materials and methods). Reflecting stuttering transmission chains in school settings, reproduction numbers from children aged 0 to 9 and teens aged 10 to 19 were estimated at below one [respectively, 0.52 (0.42 to 0.60) and 0.73 (0.57 to 0.88)] after schools were considered to have reopened in the model (Fig. 3 and table S2). Reproduction numbers from children were lower than those from teens because at a population level, preschoolers have fewer contacts than school-aged children (fig. S6).

Since school closure mandates were lifted, the higher reproduction numbers from children and teens resulted in age shifts in the sources of SARS-CoV-2 infections. In October 2020, an estimated 2.7% (1.8 to 3.7%) of infections originated from children aged 0 to 9, 7.1% (4.5 to 10.3%) from teens aged 10 to 19, 34.0% (31.9 to 36.4%) from adults aged 20 to 34, 38.2% (36.7 to 39.4%) from adults aged 35 to 49, 15.1% (14.1 to 16.1%) from adults aged 50 to 64, 2.5% (2.2 to 2.9%) from individuals aged 65 to 79, and 0.3% (0.2 to 0.3%) from individuals aged 80+ across all locations evaluated (compare table S5 and table S4). The reconstructed shifts in the age of COVID-19 sources after school reopening are relatively modest compared to the typical age profile of infection sources of pandemic influenza (21) and reflect a lower age-specific susceptibility to SARS-CoV-2 transmission among children and teens but also substantially fewer, inferred disease-relevant contacts from children and teens than would be expected from their corresponding prepandemic contact intensities. The mechanisms behind these beneficial effects remain unclear, but the model suggests that they are substantial. In retrospective counterfactual scenarios, we explored what COVID-19 case and death trajectories would have been expected if schools had remained closed and find a large overlap between the counterfactual and actual case and death trajectories (Fig. 6 and fig. S15). However, because children and teens seed infections in older age groups that are more transmission efficient, as of October 2020, school opening is associated with an estimated 25.7% (14.5 to 40.5%) increase in COVID-19 infections and a 5.9% (3.4 to 9.3%) increase in COVID-19–attributable deaths (table S7). Larger proportions of COVID-19 infections and deaths are attributed to school reopening if the actual number of cases among school-aged children is more than six times as large as the number in school situation reports (table S7). These findings indicate that adults aged 20 to 34 and 35 to 49 continue to be the only age groups that contribute disproportionately to COVID-19 spread relative to their size in the population (fig. S14) and that the impact of school reopening on resurgent COVID-19 is mitigated most effectively by strengthening disease control among adults aged 20 to 49.

Fig. 6 Retrospective counterfactual modeling scenarios exploring the impact of school reopening on COVID-19–attributable cases.

Shown in blue and red are estimated, daily COVID-19 cases (purple line, posterior median estimate; purple ribbon, 95% credible intervals) until 29 October 2020. The model was fitted to data including reported cases among school aged children in this period, assuming that reported cases could be an underestimate of actual cases among school aged children by a factor of 6 or less. In counterfactual modeling scenarios, the retrospective impact of continued school closures was explored until 29 October 2020, and the predicted case trajectories are shown (black line, posterior median estimate; black ribbon, 95% credible interval).

Caveats

The findings of this study need to be considered in the context of the following limitations. Rossen and colleagues (22) observed that US excess deaths between the beginning of the pandemic and October 2020 were 38% higher than the reported COVID-19–attributable deaths, suggesting that the death data on which this analysis rests are subject to underreporting. The scale of the US epidemics may be larger than we infer, and our age-specific analyses may be biased if underreporting of deaths depends on age. However, owing to the high proportion of asymptomatic COVID-19 cases (5), underreporting is a substantially larger caveat for reported case data, and in particular the observed shifts in the share of age groups among reported cases (4, 23), which are absent from the share of age groups among reported deaths (fig. S11). This suggests that age-specific death data provide a more reliable picture into resurgent COVID-19 epidemics than reported cases. We further rely on limited data from two contact surveys performed in the United Kingdom and China to characterize contact patterns from and to younger individuals during school-closure periods (7, 8), and this could have biased our findings that children and teens have contributed negligibly to SARS-CoV-2 spread until school reopening. To address this limitation, we explored the impact of higher intergenerational contact intensities involving children during school closure periods, and in these analyses the estimated contribution of children aged 0 to 9 to onward spread until August 2020 remained below 5% and the contribution of teens aged 10 to 19 remained below 12.5% (see supplementary materials). Epidemiologic models are sensitive to assumptions about the infection fatality ratio (IFR) that enables the estimation of actual cases from observed deaths by age. Our analyses are based on a meta-analysis that consolidates estimates from 27 studies and 34 geographic locations (20). To test the assumed IFR, we compared the scale of the estimated resurgent epidemics against data from seroprevalence surveys conducted by the Centers for Disease Control and Prevention (CDC) (24) and found good congruence (table S6 and supplementary materials). The COVID-19 epidemic is more granular than considered in our spatial modeling approach. Substantial heterogeneity in disease transmission exists at the county level (25), and our situation analyses by state and metropolitan areas should be interpreted as averages. Without exception, the model underlying our analyses also relies on simplifying mathematical assumptions on population-level disease spread, which may be shown unsuitable as further evidence on SARS-CoV-2 transmission accumulates (26). For instance, the model assumes that children and teens transmit SARS-CoV-2 as readily as do adults, which has been challenging to quantify to date (27), and falls short of accounting for population structure other than age, such as household settings, where attack rates have been estimated to be substantially higher than in non-household settings (28). It is possible that the model underestimates the impact of school reopening on SARS-CoV-2 transmission.

Data from countries that have reopened schools have provided little evidence for substantial transmission in schools, nor for significantly increased community-level infection rates after school reopening until the emergence of more transmissible SARS-CoV-2 variants (29, 30), but this might reflect frequent subclinical infection among school-aged children. More-transmissible SARS-CoV-2 variants could increase reproduction numbers to above one from all age groups, which implies substantial spread from all age groups, and require generally stricter control measures across all ages to prevent COVID-19–attributable deaths (31).

Conclusions

This study provides evidence that the resurgent COVID-19 epidemics in the US in 2020 have been driven by adults aged 20 to 49 and, in particular, adults aged 35 to 49, before and after school reopening. Unlike pandemic influenza, these adults accounted, after school reopening in October 2020, for an estimated 72.2% (68.6 to 75.9%) of SARS-CoV-2 infections in the US locations considered, whereas less than 5% originated from children aged 0 to 9 and less than 10% from teens aged 10 to 19. The population mobility data, and the death data provided by state and city Departments of Health, reveal heterogeneous disease spread in the US, with higher transmission risk per venue visit attributed to individuals aged 20 to 49 over distinct time periods and younger epidemics with a greater share of individuals aged 20 to 34 among cumulative infections in the southern, southwestern, and western regions of the US. Over time, the share of age groups among reported deaths has been markedly constant, suggesting that young adults are unlikely to have been the primary source of resurgent epidemics since summer 2020 and that, instead, changes in mobility and behavior among the broader group of adults aged 20 to 49 underlie resurgent COVID-19 in the US in 2020. This study indicates that in locations where novel, highly transmissible SARS-CoV-2 lineages have not yet become established, additional interventions among adults aged 20 to 49, such as mass vaccination with transmission-blocking vaccines, could bring resurgent COVID-19 epidemics under control and avert deaths.

Materials and methods

To characterize the role of age groups in driving resurgent COVID-19, we have taken a systematic approach that involved data collection, mathematical modeling, likelihood-based inference, and validation against external data. The following sections summarize our materials and methods, and full technical details are in the Data Availability Statement and the supplementary Materials.

Data and data processing

The analyses presented in this study are based on age-specific COVID-19 attributable mortality counts that were collected daily from US state and city Departments of Health (DoH), all-age COVID-19 death counts, all-age COVID-19 case counts, COVID-19 case counts in school settings K1-K15, human contact data before and during the pandemic, and human mobility data during the pandemic.

Briefly, age-specific COVID-19 cumulative death counts were retrieved for 42 US states, the District of Columbia and New York City from city or state DoH websites, data repositories, or via data requests to DoH (table S8). Data were checked for consistency and adjusted when necessary. Age-specific COVID-19 death time series were reconstructed from cumulative counts, and the time series were used for model fitting (32).

All-age daily COVID-19 case and death counts from 1 February 2020 until 30 October 2020 regardless of age were obtained from Johns Hopkins University (JHU) for all US states and the District of Columbia (3), except New York City. For New York City, daily COVID-19 deaths counts were obtained from the GitHub Repository (33). The all-age death counts were used for model fitting prior to when age-specific death counts were reported for each location, and all-age case counts were used for model fitting for the entire study period.

COVID-19 case counts in school settings K1-K15 were retrieved for Florida and Texas and matched with student enrolment numbers in each school from the Common Core of Data Americas Public Schools database (34). Cumulative attack rates were obtained by dividing cumulative reported cases among students by student numbers, and used for model fitting.

Human contact data before the pandemic were obtained from the Polymod study (6), and used to predict baseline contact matrices during the early part of the pandemic for each location, similar as in (18). Given the variation in contact patterns seen across survey settings, baseline contact matrices for each study location in the US were predicted based on each location’s population density and age composition with a log linear regression model. Age-specific population counts were obtained from (35). Area measurements were obtained for every US state and for New York City, respectively, from (36) and (37). Contact matrices were predicted by 5-year age bands for weekdays and weekends, and used in the model. Human contact data during the pandemic were retrieved from two surveys (7, 8), and used in the model to specify contact patterns from and to individuals aged 0 to 19 during periods of school closure.

Age-specific human mobility trends were derived from the Foursquare Labs Inc. US first-party panel that includes >10 million of opt-in, always-on active users. From operated and partner apps, Foursquare collects a variety of device signals against opted-in users including intermittent device GPS coordinate pings, WiFi signals, cell signal strength, device model, and operating system version. A smaller set of labeled explicit check-ins is captured from a portion of the user panel. Check-ins are explicit confirmations that a user was at a given venue at a given point of time, and serve as training labels for a nonlinear model that is used to predict visits among users with unlabeled visits in terms of probabilities as to which venue users ultimately visited (11). Visit probabilities among panellists were processed and aggregated by day, age, and study location, and standardized to daily per capita visits using latest US Census data. Percent changes in daily venue visits by age and study location were obtained relative to the baseline period 3 February to 9 February 2020 and used for analysis and model fitting. For validation purposes, a second mobility data set was obtained from Emodo. The Emodo data set quantifies the proportion of individuals with at least one observed ping outside the user’s home location, out of a panel of individuals whose GPS enabled devices emitted at least one ping on the corresponding day. Primary data were similarly aggregated by day, age, and study location, standardized to daily per capita visits using latest US Census data, and mobility trends were calculated relative to the baseline period 19 February to 3 March 2020.

Statistical analysis of human mobility data and COVID-19–attributable death data

The age-specific human mobility data showed marked time trends, which were characterized in terms of three phases defined by the dip date after which the 15-day moving average fell below 10% compared to the average value in the two prior weeks, and the rebound date that corresponded to the date at which the 15-day moving average was lowest. Differences in the mobility trends relative to the February baseline period, before and after rebound dates, and relative to individuals aged 35 to 44 were assessed using Gamma regression models using log link and location by age interaction covariates.

To characterize the time evolution of deaths across locations and validate model fits, age-specific COVID-19–attributable deaths among the same age strata across locations were predicted by month with a Dirichlet-Multinomial regression model. Trends in the share of age groups among monthly deaths were assessed by testing for differences in the proportions in the first month relative to subsequent months.

To test for potential differences in age-specific transmission dynamics based on the collected death data and without epidemic models, meta-analysis estimates of age-specific infection fatality ratios (20) were used to predict the share of age groups among infections from monthly age-specific deaths. Trends in the share of age groups among monthly infections were assessed by testing for differences in the proportions in the first month relative to subsequent months.

Contact-and-infection model

To quantify age-specific aspects of COVID-19 spread in heterogeneous populations, we formulated an age-specific, discrete-time renewal model in which disease transmission occurs via contact intensities between population groups stratified by 5-year age bands. The model has four key features described below. First, contact intensities vary in time and are inferred from signatures in the age-specific mortality and mobility data. This feature aims to reflect the substantial changes in human contact patterns during the pandemic (79). Second, the challenge and value of the model to produce generalizable knowledge is to explain disease spread across multiple locations with distinct demographics simultaneously. To this end, the renewal equations were embedded into a hierarchical model in which information on disease spread is borrowed across locations (1, 38). Third, the model describes disease spread during the initial and later phase of the pandemic, as mobility patterns become less correlated with transmission risk and schools reopen (39, 40). This feature allowed us to test for changes in disease dynamics over time. Fourth, the model is fitted in a Bayesian framework to the all-age and age-specific death data, all-age case data, case data from schools, and age-specific human mobility trends (41). This feature forced us to focus on a model whose parameters are inferable from the data across all locations. The model is described in detail in the supplementary materials.

Briefly, we consider populations stratified by the 5-year age bands A, such thata ∈ A = {[0-4], [5-9], …, [75-79], [80-84], [85+]}(1)and denote the number of new infections, c, on day t, in age group a, and location m as cm,t,a. In the renewal equation, past infections are weighted by their relative infectiousness on day t, and the sum of these individuals has contacts with individuals in other age groups. Contacts are described by the expected number of disease relevant human contacts one person in age group a has with other individuals in age group a’ on day t in location m, cm,t,a,a. Upon contact, a proportion sm,t,aof individuals in age group a’ on day t in location m remains susceptible to SARS-CoV-2 infection, and transmission occurs with probability ρa. Thus, the age-specific renewal equation with time-changing contact intensities is cm,t,a=sm,t,aρaaCm,t,a,a(s=1t1cm,s,a g(ts))(2)where g quantifies the relative infectiousness of individuals s days after infection. An important feature of SARS-CoV-2 transmission is that similarly to other coronaviruses but unlike pandemic influenza (42), susceptibility to SARS-CoV-2 infection increases with age (7, 21, 43). Here, we used contact tracing data from Hunan province, China (7) to specify lower susceptibility to SARS-CoV-2 infection among children aged 0 to 9, and higher susceptibility among individuals aged 60+, when compared to the 10 to 59 age group as part of the transmission probabilities ρa. Previously infected individuals are assumed to be immune to re-infection within the analysis period, consistent with mounting evidence for sustained antibody responses to SARS-CoV-2 antigens (44, 45), so thatsm,t,a=1s=1t1cm,s,aNm,a,(3)where Nm,a denotes the population count in age group a’ and location m.

For adults aged 20+, the time changing contact intensities were described in terms of the prepandemic baseline contact intensities in location m, which we denote by Cm,t,a,a, and expected reductions in disease relevant contacts from contacting individuals of age a on day t in location m, which we denote by ηm,t,a, and contacted individuals of age a’ on day t in location m, ηm,t,aCm,t,a,a=ηm,t,aCm,a,aηm,t,a,(4)where a, a’ ∈ {[20−24], …, [85+]}. Expected reductions in disease relevant contacts were specified as a random effects model that included the observed age-specific mobility trends as covariates. In the model, each age-specific mobility trend was decoupled into three separate covariates that reflect the initial pre-pandemic, dip, and rebound phases in human mobility trends, so that previously observed decreases in correlation between mobility trends and transmission risk could be captured (40, 41, 46). As the same number of venue visits in, e.g., Wyoming may translate to different transmission risk than in e.g., New York City, spatial random effects allowed for scaling of mobility trends during the dip and rebound phase in each location. As venue visits do not capture all aspects of transmission risk, the model further incorporates independently for each location autocorrelated biweekly random effects to capture information on elevated, disease relevant contact intensities and transmission risk that is present in the death time series data. To test for age-specific signatures of elevated transmission risk, the model further included for each location age-specific random effects for individuals aged 20 to 49.

For children and teens aged 0 to 20, mobility data are not available, and during periods of school closure the contact intensities from and to children and teens were set to the average contact intensities reported in (7). This implied that relative to pre-pandemic contact patterns, peer-based contacts were substantially reduced, whereas contacts from an adult to children and teens increased slightly. In the model, schools were set to reopen on or after 24 August 2020 when state administrations no longer mandated statewide school closures by that date (47, 48). Thereafter, Eq. 4 was extended to include children and teens, and expected mobility reductions were estimated from the case and death data. In the absence of further data, a common average effect could be estimated across locations and children and teen age groups for the last two observation months, ηm,t,a=ηchildren for a ∈ [0 − 20]. A further compound effect γ was added to modulate the number of disease relevant child/teen child/teen contacts, which we interpreted as reduced infectiousness from children and teens and/or a positive impact of non-pharmaceutical interventions among school aged children and teens.

Bayesian inference

Past age-specific disease dynamics across all locations were inferred from age-specific death data available across locations, and age-specific mobility data. To do this, in the model, a proportion πm,a of new infections in location m of age a die, and the day of death is determined by the infection-to-death distribution, which was assumed to be constant across age groups. The proportions πm,a were associated with a strongly informative prior derived from the meta-analysis of (20), but were allowed to deviate from the baseline infection fatality ratio through location-specific random effects. The expected number of deaths in location m on day t in age group a, dm,t,a, were aggregated to the reporting strata in each location, and fitted to the observed data using a Negative Binomial likelihood model. When age-specific death data were not available, the model was fitted to all-age death data with a Negative Binomial likelihood model. All-age case data were smoothed, and used to specify a lower bound on the overall number of infections cm,t=acm,t,a through a student-t cumulative density likelihood model. Case data from schools were used to calculate empirical attack rates in school settings during specified observation windows. In turn, the empirical attack rates were used to describe a lower bound on the actual attack rate among 5 to 18-year-old children and teens in the same observation periods in the model, using a normal cumulative density likelihood model. An upper bound on the actual attack rates was also specified by assuming that actual cases in school settings were underreported at most 10-fold, using a normal complementary cumulative density likelihood model. The contact-and-infection model was fitted with CmdStan release 2.23.0 (22 April 2020), using an adaptive Hamiltonian Monte Carlo (HMC) sampler (41). 8 HMC chains were run in parallel for 1,000 iterations, of which the first 400 iterations were specified as warm-up. There were no divergent transitions.

Generated quantities

Results were reported in the age bands d ∈ D = {[0−9], [10−19], [20−34], [35−49], [50 to 64], [65 to 79], [80+]}. The primary model outputs were aggregated correspondingly, e.g. the number of new infections in location m on day t in reporting age group d was cm,t,d=adcm,t,a. The effective number of infectious individuals c in location m and age group d on day t was calculated based on the renewal model (2), cm,t,d*=s=1t1cm,s,dg(ts), and is shown in Fig. 2. Following (2), the time-varying reproduction number on day t from one infectious person in age group a in location m is Rm,t,a=asm,t,aρaCm,t,a,a, and the reproduction numbers were aggregated to the reporting strata based on the identity Rm,t,d=ad(cm,t,a*)/(kdcm,t,k*)Rm,t,a, and are shown in Fig. 2 and tables S1 and S2. The transmission flows from age group a to age group a’ at time t in location m are given by Fm,t,a,a=sm,t,aρaCm,t,a,a(s=1t1cm,s,ag(ts)), and are aggregated using Fm,t,d,d=ad,adFm,t,a,a. In turn, the contributions of age groups to COVID-19 spread are Sm,t,d=(dFm,t,d,d)/(ddFm,t,d,d), and are reported in tables S4. Cumulated COVID-19 attack rates were calculated through Am,t,d=(s=1tcm,s,d)/(Nm,d), where Nm,d is the number of individuals in location m and age group d, and are reported in table S6.

Validation and sensitivity analyses

Reconstructed past transmission dynamics were assessed against external data on the scale of the epidemic from seroprevalence surveys conducted across the US by the CDC (24). Validation results are reported in the supplementary materials, suggesting larger discrepancies between model fit and seroprevalence data for Connecticut and New York City, with larger epidemics reconstructed in the model than the data suggest. The contact-and-infection model does not account for sustained spatial importation of SARS-CoV-2 infections such as from New York City to Connecticut, and may have over-estimated the magnitude of self-sustaining epidemic in locations receiving sustained SARS-Cov-2 importations. However, we also note that the Connecticut seroprevalence estimates predict an infection to observed case ratio that is substantially below those of the other CDC seroprevalence studies. The inferred contact patterns were assessed against external data from the BICS study that quantified human contact patterns during the pandemic (9). Validation results are reported in the supplementary materials, suggesting similarly strong reductions in human contact intensities as in the survey data. Disaggregated by age, the model reproduces highest contact intensities among 35- to 44-year-old individuals, comparatively lower contact intensities from individuals aged 45+, and largest reductions in contact intensities from individuals aged 25 to 34. The survey data suggest that contact intensities from individuals aged 18 to 24 could be higher than reconstructed through the contact-and-infection model, but we also note large confidence intervals around the survey estimates.

Sensitivity analyses were conducted to assess central modeling assumptions on the infection fatality ratio, contact intensities among children and teens during periods of school closure, relative susceptibility of children and teens to SARS-CoV-2 infection, and are reported in the supplementary materials. Our findings on the age groups that drive SARS-CoV-2 transmission were found to be robust to these assumptions.

Supplementary Materials

References and Notes

  1. Feehan, D. M., Mahmud, A., Quantifying population contact patterns in the United States during the COVID-19 pandemic. medrXiv 2020.04.13.20064014 [Preprint]. 29 August 2020). doi:10.1101/2020.04.13.20064014

  2. A. Gelman et al., Bayesian Data Analysis (CRC Press, 2013).

  3. T. Wutzler, lognorm: Functions for the Lognormal Distribution. R package version 0.1.6 (2019).

  4. Ferguson, N et al., Report 9: Impact of non-pharmaceutical interventions (NPIs) to re-duce COVID19 mortality and healthcare demand. Imperial College London COVID-19 Reports (2020); .doi:10.25561/77482

  5. C. G. McAloon et al., The incubation period of COVID-19: A rapid systematic re-view and meta-analysis of observational research. medRxiv 2020.04.24.20073957 [Preprint]. 28 April 2020. .doi:10.1101/2020.04.24.20073957

  6. J. Friedman, P. Liu, E. Gakidou, COVID, I., Team, M. C., Predictive performance of international COVID-19 mortality forecasting models. medRxiv 2020.07.13.20151233 [Preprint] (19 November 2020). doi:10.1101/2020.07.13.20151233

Acknowledgments: We thank the Imperial College COVID-19 Response Team for their insightful comments: K. E. C. Ainslie, A. Boonyasiri, O. Boyd, L. Cattarino, L. V. Cooper, Z. Cucunubá, G. Cuomo-Dannenburg, B. Djaafara, I. Dorigatti, R. FitzJohn, K. A. M. Gaythorpe, L. Geidelberg, W. D. Green, A. Hamlet, W. Hinsley, B. Jeffrey, E. Knock, D. Laydon, G. Nedjati-Gilani, P. Nouvellet, K. V. Parag, I. Siveroni, H. A. Thompson, R. Verity, C. E. Walters, H. Wang, Y. Wang, O. J. Watson, P. Winskill, C. Whittaker, P. G. T. Walker, C. A. Donnelly, L. Okell, B. Sangeeta, N. F. Brazeau, O. D. Eales, D. Haw, N. Imai, E. Jauneikaite, J. Lees, A. Mousa, D. Olivera, J. Skarp, and L. Whittles. Funding: This work was supported by the NIHR HPRU in Modelling and Health Economics, a partnership between PHE, Imperial College London and LSHTM (grant code NIHR200908), and the Imperial College Research Computing Service. We acknowledge funding from the Imperial College COVID-19 Response Fund; the Bill & Melinda Gates Foundation; the EPSRC, through the EPSRC Centre for Doctoral Training in Modern Statistics and Statistical Machine Learning at Imperial and Oxford; and the MRC Centre for Global Infectious Disease Analysis (reference MR/R015600/1), jointly funded by the UK Medical Research Council (MRC) and the UK Foreign, Commonwealth, and Development Office (FCDO), under the MRC-FCDO Concordat agreement and that is also part of the EDCTP2 program supported by the European Union. We thank Microsoft and Amazon for providing cloud computing services. The views expressed are those of the authors and not necessarily those of the United Kingdom (UK) Department of Health and Social Care, the National Health Service, the National Institute for Health Research (NIHR), or Public Health England (PHE) Author contributions: O.R. conceived the study. A.G., S.M., S.F., S.B., N.F., and O.R. oversaw the study. M.M., D.H., S.Be., S.T., Y.C., M.McM., M.H., H.Z., A.Be., and O.R. oversaw and performed data collection. M.M., A.Bl., X.X., and O.R. led the analysis. V.C.B., H.C., S.F., J.I.H., T.M., A.G., H.J.T.U., M.V., S.W., and S.M. contributed to the analysis. All authors discussed the results and contributed to the revision of the final manuscript. Competing interests: S.B. acknowledges the National Institute for Health Research (NIHR) BRC Imperial College NHS Trust Infection and COVID themes, the Academy of Medical Sciences Springboard award and the Bill and Melinda Gates Foundation. OR reports grants from the Bill & Melinda Gates Foundation during the conduct of the study. Data and materials availability: The Foursquare population mobility data are available on Github, https://github.com/ImperialCollegeLondon/covid19model, under the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International Public License. The Emodo population mobility data are available on Github, https://github.com/ImperialCollegeLondon/covid19model, and Zenodo (49) (for updates see (32)), under the Creative Commons Attribution-NonCommercial 4.0 International Public License. Code are available on Github, https://github.com/ImperialCollegeLondon/covid19model, under the MIT License. This work is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/. This license does not apply to figures/photos/artwork or other content included in the article that is credited to a third party; obtain authorization from the rights holder before using such material.

Covid-19 and the Investigator Pipeline | NEJM – nejm.org

On December 14, 2020, the first person in the United States to be vaccinated against Covid-19 outside of a clinical trial received her first dose of vaccine. Later that week, a trial demonstrated reductions in viral load among patients receiving monoclonal antibodies against SARS-CoV-2.1 By January 1, 2021, less than 10 months after Covid-19 was declared a pandemic, more than 4000 studies related to Covid-19 were registered on ClinicalTrials.gov, including nearly 1500 involving a drug or vaccine. The speed, depth, and breadth of the response to Covid-19 by the biomedical research community has been unprecedented. At the same time as these advances are on track to save millions of lives, however, the Covid-19 pandemic is dismantling the pipeline of investigators who are essential to the future of biomedical science.

When Covid-19 hit the United States in the spring of 2020, universities and hospitals in affected areas quickly shut down basic science laboratories, in keeping with directions from public health authorities. Experiments were upended, and valuable laboratory animals were sacrificed. With the exception of Covid-related studies, clinical research slowed dramatically, as conducting study visits became nearly impossible and resources were redirected. Investigators saw their staff deployed to support patient care during surges. In many health systems and universities, hiring of new staff and access to institutional funds were restricted or frozen because of projections of sharp reductions in revenue.

Other consequences of Covid-19 created additional challenges for specific groups. Many investigators with young families had to manage remote schooling with children home all day. Investigators in clinical disciplines that were central to pandemic response were pulled to staff clinical services — particularly in critical care, infectious diseases, and hospital medicine. International travel restrictions impeded global health research, with projects sidelined because of the inability to hire and train study staff in other countries. Financial stress was most acute when an investigator’s partner lost income, an institution cut salaries or benefits, or the costs of pandemic-related needs, such as in-home child care, drove up monthly expenses. Some investigators faced illnesses or deaths of family members or other loved ones. The deaths of Breonna Taylor, George Floyd, and many others at the hands of police highlighted the violence and oppression experienced by people of color in the United States. Together with the growing anger over racist federal and state policies, these events led to national anguish, particularly among people from racial and ethnic communities that are underrepresented in the sciences.

By late spring 2020, the implications of Covid-19 for the investigator community were widely recognized, and mitigation efforts began. National Institutes of Health (NIH) program officers reached out to grantees to ask about the pandemic’s effects on their research. Eligibility for some NIH career-development awards was extended so that trainees with more than the usual amount of postdoctoral research experience could apply. Carryover rules for grant funds and grant-application deadlines became more flexible. Many funders created new programs to support Covid-19 research, although most programs relied on supplements to existing grants, which disadvantaged junior investigators.

Academic institutions throughout the United States also responded to the challenges facing investigators. Reopening research laboratories was prioritized, and scientists were able to return to their labs for at least a few hours each day. When possible, telehealth platforms were leveraged to support clinical research efforts, which enabled study visits to restart even while hospital or practice visits were limited. Bridge and pilot funding programs were developed or expanded, often targeting junior investigators who were most at risk for having their careers disrupted because of upheaval in the research and training process. Many universities extended timelines for tenure decisions by a year or more. Some created virtual mentoring programs in which senior faculty reached out to junior investigators to offer grant-writing support and other advice.2

In the Massachusetts General Hospital Department of Medicine, we launched several additional initiatives, which early evidence suggests have had some limited success. Samples from patients with Covid-19 were collected using a centralized biobanking protocol that prioritized access for junior investigators. Junior faculty leveraged their access to build new collaborations, which led to new grants. We developed a Covid-19 clinical trials review process and infrastructure to enable junior faculty without research staff to propose and lead studies.3 Senior investigators who had grants that were eligible for Covid-related supplements included junior investigators in proposals when possible. At the same time, clinical leaders minimized the clinical demands on junior investigators (in part by offering extra payment to people who were interested in additional clinical time and by ensuring that the extent of additional clinical requests was proportional to baseline clinical responsibilities) and maximized flexibility in clinical scheduling to help faculty balance family-related and professional demands. We established a program that provided research-assistant support to junior faculty and internships to college students from disadvantaged backgrounds. With a recent philanthropic gift, we initiated an effort to distribute supplemental funding to investigators with career-development awards and additional salary support to mentored junior faculty who have had to delay their entry into the job market.

Although we were fortunate to be able to leverage Covid-related donations that are unavailable to many institutions, it’s become increasingly clear that even these steps are insufficient. Every week, I hear from senior faculty concerned about losing junior scientists and from junior scientists who are anxious and exhausted. A recent job posting for an investigator position yielded nearly 100 applications, many more than we would have received a year ago. The past year has made it clear how critical the investigator pipeline is for global well-being. More must be done to strengthen it.

Academic institutions and funding agencies have the greatest responsibility for the scientific-investigator pipeline and should support programs and policy changes that could mitigate the pandemic’s effects. At the same time, the Covid-19 vaccine effort highlights the power of the federal government to leverage public–private partnerships during a national crisis. The companies developing and producing Covid-19 vaccines are performing an immense public service. They are also expected to make many billions of dollars selling these vaccines — products that could not have been developed without decades of formative basic and clinical research, including fundamental studies of gene structure and editing and animal and human experiments that used engineered RNA and DNA to induce immune responses. It’s hard to imagine a better rationale for prioritizing public–private investment in the scientific-investigator pipeline.

Building on earlier recommendations, I believe such investment should address financial and other disincentives faced by junior scientists and should help diversify career options for scientists completing their training.4,5 There is no silver bullet, and successfully shoring up the investigator pipeline will require multiple initiatives. At the top of the list should be increasing the flexibility and amount of support associated with training and career-development grants for awardees and mentors (particularly for K awards, which now lead to 25% of new R01 grants and have provided essentially the same amount of funding for decades), establishing programs that create incentives for institutions and principal investigators to hire staff scientists, extending current grants and providing additional funding to help scientists redo experiments that were abandoned or cut short, expanding accountable loan-repayment programs, and fostering partnerships between academic institutions, health systems, government agencies, and private companies such as pharmaceutical companies to promote investment in critical scientific and support infrastructure (including child care) for junior scientists in particular. These efforts can take lessons from pipeline programs in other countries, including the Canada Research Chairs program, which committed $335 million to support Canadian scientists in 2020. Finally, marked disparities in Covid-19 infections and deaths by race and ethnic group emphasize the importance of designing investments in the investigator pipeline to prioritize health equity throughout the biomedical research process.

Emerging infectious diseases and other health threats will continue to pose a danger to the United States and the global community. It’s time to leverage vaccine-development efforts to take on another important Covid-related challenge: stabilizing the investigator pipeline. As recent months have shown, the scientific pipeline is both in jeopardy and central to our ability to address emerging health threats.

Coronavirus (COVID-19) Update: March 23, 2021 – FDA.gov

For Immediate Release:

The U.S. Food and Drug Administration today announced the following actions taken in its ongoing response effort to the COVID-19 pandemic:

  • In a March 22 Consumer Update, the FDA provided an update on simple steps to help slow the spread of coronavirus disease to protect ourselves, our families and our communities. Read more: Help Stop the Spread of Coronavirus and Protect Your Family.
  • The FDA and the NIH CURE ID app has received the 2021 Golan Christie Taglia Patient Impact Philanthropy Award from Cures Within Reach. CURE ID is an internet-based repository that lets the clinical community share novel uses of existing drugs for difficult-to-treat infectious diseases. The FDA and the NIH have made critical updates to CURE ID to be a more effective tool during COVID-19.
  • As part of the FDA’s effort to protect consumers, the agency issued a warning letter jointly with the Federal Trade Commission to PYRLess Group, LLC dba Dr. Fitt for selling unapproved products with fraudulent COVID-19 claims. The FDA requested that the company take immediate action to cease the sale of any unapproved and misbranded products for the treatment or prevention of COVID-19. Consumers concerned about COVID-19 should consult with their health care providers.
  • Testing updates:
    • As of today, 343 tests and sample collection devices are authorized by the FDA under emergency use authorizations (EUAs). These include 255 molecular tests and sample collection devices, 73 antibody and other immune response tests, and 15 antigen tests. There are 41 molecular authorizations that can be used with home-collected samples. There is one molecular prescription at-home test, two antigen prescription at-home tests, one over-the-counter (OTC) at-home antigen test, and one OTC molecular test.

The FDA, an agency within the U.S. Department of Health and Human Services, protects the public health by assuring the safety, effectiveness, and security of human and veterinary drugs, vaccines and other biological products for human use, and medical devices. The agency also is responsible for the safety and security of our nation’s food supply, cosmetics, dietary supplements, products that give off electronic radiation, and for regulating tobacco products.

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