Posted on by Dr. Francis Collins
As our nation looks with hope toward controlling the coronavirus 2019 disease (COVID-19) pandemic, researchers are forging ahead with efforts to develop and implement strategies to prevent future outbreaks. It sounds straightforward. However, several new studies indicate that containing SARS-CoV-2—the novel coronavirus that causes COVID-19—will involve many complex challenges, not the least of which is figuring out ways to use testing technologies to our best advantage in the battle against this stealthy foe.
The first thing that testing may help us do is to identify those SARS-CoV-2-infected individuals who have no symptoms, but who are still capable of transmitting the virus. These individuals, along with their close contacts, will need to be quarantined rapidly to protect others. These kinds of tests detect viral material and generally analyze cells collected via nasal or throat swabs.
The second way we can use testing is to identify individuals who’ve already been infected with SARS-CoV-2, but who didn’t get seriously ill and can no longer transmit the virus to others. These individuals may now be protected against future infections, and, consequently, may be in a good position to care for people with COVID-19 or who are vulnerable to the infection. Such tests use blood samples to detect antibodies, which are blood proteins that our immune systems produce to attack viruses and other foreign invaders.
A new study, published in Nature Medicine , models what testing of asymptomatic individuals with active SARS-CoV-2 infections may mean for future containment efforts. To develop their model, researchers at China’s Guangzhou Medical University and the University of Hong Kong School of Public Health analyzed throat swabs collected from 94 people who were moderately ill and hospitalized with COVID-19. Frequent in-hospital swabbing provided an objective, chronological record—in some cases, for more than a month after a diagnosis—of each patient’s viral loads and infectiousness.
The model, which also factored in patients’ subjective recollections of when they felt poorly, indicates:
• On average, patients became infectious 2.3 days before onset of symptoms.
• Their highest level of potential viral spreading likely peaked hours before their symptoms appeared.
• Patients became rapidly less infectious within a week, although the virus likely remains in the body for some time.
The researchers then turned to data from a separate, previously published study , which documented the timing of 77 person-to-person transmissions of SARS-CoV-2. Comparing the two data sets, the researchers estimated that 44 percent of SARS-CoV-2 transmissions occur before people get sick.
Based on this two-part model, the researchers warned that traditional containment strategies (testing only of people with symptoms, contact tracing, quarantine) will face a stiff challenge keeping up with COVID-19. Indeed, they estimated that if more than 30 percent of new infections come from people who are asymptomatic, and they aren’t tested and found positive until 2 or 3 days later, public health officials will need to track down more than 90 percent of their close contacts and get them quarantined quickly to contain the virus.
The researchers also suggested alternate strategies for curbing SARS-CoV-2 transmission fueled by people who are initially asymptomatic. One possibility is digital tracing. It involves creating large networks of people who’ve agreed to install a special tracing app on their smart phones. If a phone user tests positive for COVID-19, everyone with the app who happened to have come in close contact with that person would be alerted anonymously and advised to shelter at home.
The NIH has a team that’s exploring various ways to carry out digital tracing while still protecting personal privacy. The private sector also has been exploring technological solutions, with Apple and Google recently announcing a partnership to develop application programming interfaces (APIs) to allow voluntary digital tracing for COVID-19 , The rollout of their first API is expected in May.
Of course, all these approaches depend upon widespread access to point-of-care testing that can give rapid results. The NIH is developing an ambitious program to accelerate the development of such testing technologies; stay tuned for more information about this in a forthcoming blog.
The second crucial piece of the containment puzzle is identifying those individuals who’ve already been infected by SARS-CoV-2, many unknowingly, but who are no longer infectious. Early results from an ongoing study on residents in Los Angeles County indicated that approximately 4.1 percent tested positive for antibodies against SARS-CoV-2 . That figure is much higher than expected based on the county’s number of known COVID-19 cases, but jibes with preliminary findings from a different research group that conducted antibody testing on residents of Santa Clara County, CA .
Still, it’s important to keep in mind that SARS-CoV-2 antibody tests are just in the development stage. It’s possible some of these results might represent false positives—perhaps caused by antibodies to some other less serious coronavirus that’s been in the human population for a while.
More work needs to be done to sort this out. In fact, the NIH’s National Institute of Allergy and Infectious Diseases (NIAID), which is our lead institute for infectious disease research, recently launched a study to help gauge how many adults in the U. S. with no confirmed history of a SARS-CoV-2 infection have antibodies to the virus. In this investigation, researchers will collect and analyze blood samples from as many as 10,000 volunteers to get a better picture of SARS-CoV-2’s prevalence and potential to spread within our country.
There’s still an enormous amount to learn about this major public health threat. In fact, NIAID just released its strategic plan for COVID-19 to outline its research priorities. The plan provides more information about the challenges of tracking SARS-CoV-2, as well as about efforts to accelerate research into possible treatments and vaccines. Take a look!
 Temporal dynamics in viral shedding and transmissibility of COVID-19. He X, Lau EHY, Wu P, Deng X, Wang J, Hao X, Lau YC, Wong JY, Guan Y, Tan X, Mo X, Chen Y, Liao B, Chen W, Hu F, Zhang Q, Zhong M, Wu Y, Zhao L, Zhang F, Cowling BJ, Li F, Leung GM. Nat Med. 2020 Apr 15. [Epub ahead of publication]
 Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. Li, Q. et al. N. Engl. J. Med. 2020 Mar 26;382, 1199–1207.
 Apple and Google partner on COVID-19 contact tracing technology. Apple news release, April 10, 2020.
 USC-LA County Study: Early Results of Antibody Testing Suggest Number of COVID-19 Infections Far Exceeds Number of Confirmed Cases in Los Angeles County. County of Los Angeles Public Health News Release, April 20, 2020.
 COVID-19 Antibody Seroprevalence in Santa Clara County, California. Bendavid E, Mulaney B, Sood N, Sjah S, Ling E, Bromley-Dulfano R, Lai C, Saavedra-Walker R, Tedrow J, Tversky D, Bogan A, Kupiec T, Eichner D, Gupta R, Ioannidis JP, Bhattacharya J. medRxiv, Preprint posted on April 14, 2020.
Coronavirus (COVID-19) (NIH)
COVID-19, MERS & SARS (NIAID)
NIH Support: National Institute of Allergy and Infectious Diseases
Posted on by Dr. Francis Collins
For centuries, scientists have trained themselves to look through microscopes and carefully study their structural and molecular features. But those long hours bent over a microscope poring over microscopic images could be less necessary in the years ahead. The job of analyzing cellular features could one day belong to specially trained computers.
In a new study published in the journal Cell, researchers trained computers by feeding them paired sets of fluorescently labeled and unlabeled images of brain tissue millions of times in a row . This allowed the computers to discern patterns in the images, form rules, and apply them to viewing future images. Using this so-called deep learning approach, the researchers demonstrated that the computers not only learned to recognize individual cells, they also developed an almost superhuman ability to identify the cell type and whether a cell was alive or dead. Even more remarkable, the trained computers made all those calls without any need for harsh chemical labels, including fluorescent dyes or stains, which researchers normally require to study cells. In other words, the computers learned to “see” the invisible!
Posted on by Dr. Francis Collins
The brand new $3 million Breakthrough Prize in the Life Sciences  delivered a very nice reward and well deserved recognition to eleven exceptionally creative scientists who have devoted their careers to biology and medicine. And, with five awards to be given each year, I hope this inspires other life scientists to embark on innovative and high-risk endeavors.
For this inaugural round, I’m proud to say that nine of the eleven winners were NIH grant recipients—some for more than three decades. Now, you may not have heard of most of these scientists. Quite frankly, that’s a shame. These folks have discovered fundamental principles of biology—everything from cancer causing genes to techniques for creating stem cells. These discoveries have boosted our understanding of health and disease, and led to the development of many drugs and therapies.
So these individuals really should be household names—and more of that kind of recognition would be a good thing to inspire youth to explore careers in science. In the United States, virtually everyone can list names of multiple movie stars and athletes, but two-thirds of Americans can’t name a single living scientist .