Guest post by Taunton Paine, MA, Director of the Division of Scientific Data Sharing Policy, NIH Office of Science Policy
Behind the NIH Genomic Data Sharing Policy
In November 2021, NIH published a request for information seeking public input on the future of the NIH Genomic Data Sharing (GDS) Policy. Originally published in 2014, the NIH GDS Policy expanded and refined an existing framework for the broad and responsible sharing of genomic research data originally created for genome-wide association studies. Since this policy framework was first implemented, NIH has accepted data from more than 1,200 studies in the NIH database of Genotypes and Phenotypes (dbGaP) hosted by NLM and facilitated more than 64,000 additional research uses. Many more studies involving non-human data and human data with study participant consent for full public access have been shared as a result of the GDS Policy through a variety of additional NIH repositories, such as GenBank and the Sequence Read Archive, which are also hosted by NLM.
While the GDS Policy has been remarkably successful at spurring the timely, productive, and secure sharing of genomic data, NIH has devoted substantial effort to maintaining the relevance of this framework by issuing updates as needed. NIH has provided substantial guidance to account for trends in science, technology, and society. For example, the policy and related guidance evolved to accommodate a growing shift toward cloud computing in genomic research.
Evolving Priorities: Help Us Shape the Future of Genomic Data Sharing
In October 2020, NIH issued the Final NIH Policy for Data Management and Sharing. The final policy will be effective on January 25, 2023. To better align the GDS and the NIH Policy for Data Management and Sharing policies, NIH is soliciting input about proposed changes to the GDS policy. Described below are some of the key proposed issues for which NIH is seeking comment in the request for information.
The use of genomic data in research continues to evolve. Specifically, there is growing interest in the use of human data elements that might be considered identifiable, which cannot currently be submitted to NIH genomic data repositories, and in the ability to match participants’ data across repositories or with data from other sources. The request for information seeks comment on whether NIH should permit these activities, and if so, what additional protections may be necessary.
To reduce the technical burden of analyzing genomic data, NIH has developed additional resources for storing, sharing and analyzing human genomic data in addition to dbGaP, resulting in an increasingly federated landscape of platforms and repositories hosted by NIH and awardee institutions. To ensure consistency of operations and protections, NIH is proposing core principles for NIH-supported genomic data repositories and platforms.
NIH frequently receives questions about other types of high-dimensional “omics” data, such as microbiomic or proteomic data, which describes new and comprehensive approaches for analyzing molecular profiles of humans and other organisms. In some cases, non-genomic data types may pose similar risks of re-identification as large-scale genomic data but may not be subject to the GDS Policy in all scenarios. Furthermore, the GDS Policy may not apply even when genomic data are generated in some scenarios, such as for very small studies. As a longer-term consideration, NIH is soliciting views on whether the more specific sharing expectations of the GDS Policy or the protective framework it offers should be adjusted to account for these other data types or scenarios.
We are Listening!
We are working to ensure that the framework established by the GDS Policy keeps pace with the needs of the research enterprise, research participants, and the patients it is ultimately intended to benefit. This RFI may result in updates to the GDS Policy, related guidance, or implementation. That’s why we’re asking you, the community, for your input. Please visit the request for information page today; comments are due by February 28. We look forward to hearing your input and appreciate your efforts!
Taunton Paine, MA is the Director of the Scientific Data Sharing Policy Division in the Office of Science Policy at the NIH. Taunton has been with the Office of Science Policy since 2011. His division is responsible for issues relating to data sharing policy, including issuance of the recent NIH Data Management and Sharing Policy, oversight of the NIH Genomic Data Sharing Policy, and management of the Data Science Policy Council. He holds a dual master’s degree from Columbia University and the London School of Economics and Political Science where he studied the history of international relations
Guest post by Clem McDonald, MD, Chief Health Data Standards Officer at the National Library of Medicine
COVID-19 testing equips individuals with the information they need to protect themselves and others, and arms public health professionals with data that can inform response efforts.
Recently, leadership across NIH articulated why widespread testing is necessary, important, and achievable. Equally important is understanding the different types of testing available. As a leader and pioneer in the development of clinical data standards, NLM supports the electronic exchange of clinical health information data, including those related to COVID-19 testing, for approved purposes and with appropriate privacy protections.
Three types of testing are available to identify COVID-19 (the disease caused by the SARS-CoV-2 virus).
1) Nucleic acid amplification tests (NAAT), also called molecular tests, detect the virus’s genetic material;
2) Antigen tests detect parts of specific proteins produced by the virus; and
3) Antibody tests detect COVID-19 antibodies in the blood (serum) that infected people develop to fight off the virus.
NAAT tests are dependent upon a method used to multiply the relatively few copies of viral nucleic acid that might be present in a specimen into a very large number of copies — making it much easier detect the virus. At present, most NAAT tests use an amplification method called polymerase chain reaction (PCR).
PCR uses small segments of DNA, called primers, to pick out the DNA that it needs to multiply. The PCR instruments process the sample in repeated cycles of heating and cooling. During each cycle, the number of copies of the targeted nucleic acid doubles. From a few original copies, it can generate up to a billion new copies to make the virus easier to see in the final detection step.
The FDA recently authorized a different NAAT test method called loop-mediated isothermal amplification (LAMP). This test method warms the sample to a constant temperature and uses six different primers to drive the replication of different segments of the novel coronavirus’s genome. It does not require multiple cycles of heating and cooling. By many accounts, this method is faster and easier to use than real-time PCR. Other methods of COVID-19 detection are under development.
Different SARS-CoV-2 NAAT testing products target different parts of the virus, use different primers to start the PCR reaction, apply to different specimens, and differ in the ability to detect the virus.
The primary methods for collecting a sample are through nasal, throat, and saliva (spit). Nasopharyngeal (NP) samples are believed to be the most sensitive for detecting the virus, but pushing the swab through the nostril into the nasopharynx at the base of the skull can be uncomfortable. The collection of other samples from nasal swabs and saliva can be easier on the person being tested and are becoming increasingly accessible.
The spread of SARS-CoV-2 is particularly challenging to manage because people can be contagious and spread the infection to others, even before they begin to show symptoms. NAAT tests can sometimes detect the virus in early stages before symptoms appear, but not always, and do not necessarily turn positive immediately with the onset of symptoms.
One strategy with NAAT tests involves the use of pooled samples. Pooled sampling involves mixing several samples together in a batch, or pooled sample, then analyzing the pooled sample with a diagnostic test. If the test on the pooled specimen is negative, then all the individuals who contributed to the pool are considered negative for COVID-19. If the pooled sample is positive, the lab must run separate tests on each of the samples to determine who is positive and who is negative. When the prevalence of COVID-19 in a population is low (in the 1-2% range), the total number of tests needed is reduced, and an organization’s testing capacity increases.
Antigen tests for COVID-19 detect the presence of a protein that is part of the SARS-CoV-2 virus. Today, the NP and mid-nasal samples are the primary sampling methods used for antigen testing, but the development of antigen tests for saliva are underway.
Antigen tests are relatively inexpensive and provide results almost immediately. These tests perform best in the early days after an infection begins. While they are not as sensitive as NAAT tests, some have suggested that repeated testing with a fast, although less sensitive test, may do more to help end the epidemic more quickly than perfect tests done infrequently.
Antibody SARS-CoV-2 tests detect the antibodies, or the “virus fighting proteins”, that a person’s immune system produces to fight infection. Antibody testing is generally done on the serum component of a blood sample. Antibodies may appear just a week or so after symptoms of SARS-CoV-2 infection appear. Antibody tests are not used to diagnose an active COVID-19 infection; however, they are useful for detecting whether someone has had a past infection.
Two different kinds of antibodies can be measured: IgM (immunoglobulin M) and IgG (immunoglobulin G). IgM antibodies appear early after infection (usually after the first week or so). Somewhat later, IgG antibodies, a more durable antibody, is produced. Today, there is no clear advantage of IGM or IgG antibody testing and not everyone will develop antibodies after a known COVID-19 infection. Importantly, scientists do not know how well or for how long antibody levels might protect someone against a future infection.
All three types of tests can be evaluated locally with a point-of-care (POC) machine or sent to laboratory for processing (in-lab testing). POC tests are carried out in close proximity to a patient and typically take 5-15 minutes, but only one or a handful of samples can be processed at a time. Not all POC machines have the capability to communicate electronically to public health and other reporting systems. In-lab testing machines can process hundreds of samples at time and, with the right safeguards, can deliver results electronically to patients, providers and public health reporting systems. However, in-lab testing has built-in delays due to its batch testing nature and the time it can take to deliver samples to laboratories.
There are many opportunities for innovation in testing methods to improve upon the efficiency, specificity, and scalability of currently available tests. Having a good set of well performing tests for SARS-CoV-2 is very important, but we also need to be able to deliver the results of such tests accurately and quickly (electronically) to the responsible care providers and to public health authorities.
To facilitate electronic delivery of such content, NLM has long supported the development of formal health care terminologies including LOINC (Logical Observation Identifiers Names and Codes), RxNorm, along with SNOMED CT, and more recently, communication structures such as HL7 FHIR(R). These capabilities are especially important during this time of COVID-19. In the last six months, the FDA has authorized more than 80 SARS-CoV-2 test products for emergency use, the CDC has defined a COVID-19 Case Report Form, and the Centers for Medicare & Medicaid Services has specified content that should accompany every SARS-CoV-2 test. NLM-supported LOINC codes have been defined for all of this content, as well as SNOMED CT codes for coded test values. The FDA, CDC, and industry have produced a compendium of the all SARS-CoV-2 tests and their standard codes. The use of standardized test codes for test results is essential to smooth delivery of test results into electronic health records and for the aggregation of test results for research and public health purposes.
Testing for COVID-19 is important, safe, and easy. Getting tested early and often and following best practices, such as wearing a mask, washing hands often, and limiting social contact will help get us back to normal.
Did you learn something new about testing methods? How else can NLM help support testing activities?
Clem McDonald, MD, is the Chief Health Data Standards Officer at NLM. In this role, he coordinates standards efforts across NLM and NIH, including the FHIR interoperability standard and vocabularies specific to clinical care (LOINC, SNOMED CT, and RxNorm). Dr. McDonald developed one of the nation’s first electronic medical record systems and the first community-wide clinical data repository, the Indiana Network for Patient Care. Dr. McDonald previously served 12 years as Director of the Lister Hill National Center for Biomedical Communications and as scientific director of its intramural research program.
Guest post by Susan Gregurick, PhD, associate director for data science and director of the Office of Data Science Strategy, National Institutes of Health.
There is an African proverb that says, “If you want to go fast, go alone. If you want to go far, go together.”
As I approach my first anniversary as the associate director for data science at NIH, this statement could not ring truer for me. By going together, NIH has made astonishing progress during this past year to enable more advanced data science, impressive data and computational infrastructure advances, and better FAIR data sharing.
Togetherness means collaboration that harnesses the power and strength of a diverse team. At NIH, women are using their expertise in data science and their teamwork skills to rapidly enable transformative programs.
“This is such an exciting time for innovation at the intersection of biomedical, medical, and technology domains. It’s dynamic and fast moving. Whether you have scientific skills, business expertise or know technology, there’s a role — an important role — for you in this space, especially here at NIH.”
I spoke with 11 women who are significantly impacting data science activities at NIH about how they enable data science; their advice for young, aspiring women data scientists; and the data science accomplishments that make them proud.
Collaboration and the role that NIH has played in responding to the COVID-19 pandemic were common themes in our discussions. These women also spoke about the importance of having a mentor, the four antidotes to challenging times, and the necessity of diverse perspectives.
I lead central fellowship programs to bring talented computer and data scientists to NIH. Our external outreach efforts encourage women and other minorities to apply for the programs we support. And, internally, we support engagement across NIH to place students in diverse positions.
Breaking down silos to advance data science.
Talented and driven staff across NIH have mobilized to lead implementation tactics under the strategic plan for data science, and we’ve built a forum for discussion in monthly town hall meetings. Most importantly, teams across NIH are working together and communicating widely to break down silos to continue advancing data science.
Teresa Zayas Cabán, PhD, Coordinator, Fast Healthcare Interoperability Resources (FHIR) Acceleration, National Library of Medicine (NLM)
I’m leading efforts to enable the use of standardized clinical and research data sharing to advance discovery. We’re not only working collaboratively within NIH to advance data science, but also across departments, government offices, and the field itself. Together, we are leading the field in a new direction with the use in research, as appropriate, of the same standards used in health care.
Be confident in what you know.
Don’t sell yourself short — speak up about what you know. Find good mentors who can advise you and be in your corner throughout your career. Find a good cohort of colleagues to collaborate and commiserate with.
We all have varying perspectives and visions for data science. Nonetheless, we have become nuclei of the NIH data science community. Through our collaborations, we are emissaries for data science to extramural grantee communities. I see this as a concentric circle of expanding national and even global communities of data science.
Technical and sociocultural accomplishments in data science.
A sociocultural accomplishment is that many silos have been dismantled, and the willingness and readiness to collaborate are demonstrably strong. On the technical front, there are successful examples of progress toward an NIH data ecosystem, both at the foundational level and at the leading edge.
Lisa Federer, PhD, Data Science and Open Science Librarian, Office of Strategic Initiatives, NLM
Leads the NIH Data Science Training Committee
Be a lifelong learner.
Embrace lifelong learning — there’s always something new to learn! I’ve made it a priority to learn new things that I can bring to my work, including going back to school to get a PhD in information science with a focus on data science.
Open science practices advancing our understanding of COVID-19.
NIH has been doing impressive work in advancing our understanding of COVID-19 and has been a leader in making data related to SARS-CoV-2 widely available so that researchers around the world can help tackle this important issue. In the face of this global problem, open science practices will help us make progress toward therapies and vaccines more quickly.
Jennie Larkin, PhD, Deputy Director, Division of Neuroscience, National Institute on Aging
Engage and embed data science in different programs.
Ask questions, learn, and engage. We need more bright people who can bring new perspectives, expertise, and energy to data science and help embed data science in different research programs.
Working with the community to address the COVID-19 pandemic.
The increasing breadth and depth of data science expertise across NIH and the larger biomedical enterprise has allowed us to rapidly accomplish much more than was possible just a few years ago. We have seen the best of our community, in the willingness to come together to meet the challenge of the COVID-19 pandemic.
Rebecca Rosen, PhD, Program Lead, NIMH Data Archive and Senior Advisor, Office of Technology Development and Coordination, National Institute of Mental Health
Learn from traditional and nontraditional resources.
I encourage young women in all biomedical science fields to incorporate data science into their career development plans. Look for data science educational resources from both traditional and nontraditional sources and network within those sources.
Collaboration to realize a data ecosystem.
The NIH data ecosystem has an increasingly tangible presence. We have growing numbers of researchers analyzing data across NIH cloud-based platforms, thanks in part to the new Office of Data Science Strategy, the NIH STRIDES Initiative, and a greater level of collaboration across NIH Institutes and Centers.
Heidi Sofia, PhD, Program Director, National Human Genome Research Institute (NHGRI)
Co-leads the Biomedical Information Science and Technology Initiative consortium and organized supplements to enhance software tools for open science (NOT-OD-20-073)
Beauty, awe, love, and humor.
I am never happier than when some brilliant young or established scientist in the community brings forward innovative, transformative science which I can endeavor to foster. In these instances, I find the first two of the four antidotes to our challenging times (beauty, awe, love, and humor). And my colleagues often provide the last one.
Use your power for good.
Among the first “computers” were women who performed the mathematical calculations needed to advance science, starting in 1757 in the search for Halley’s comet. Today, data science is a superpower for women in fields ranging from medicine to the natural sciences to business. So empower yourself, and use your power for good!
Maryam Zaringhalam, PhD, Data Science and Open Science Officer, Office of Strategic Initiatives, NLM
The lived experiences and perspectives of women — particularly women who are Black, Indigenous and People of Color (BIPOC); members of the LGBTQIA+ community; or members of the disability community — are critically important in ensuring that the products of data science have the greatest benefit for us all. Every chance I get, I tell women that they not only belong in data science, but that data science is better because of them.
Enabling researchers to make COVID-19 data available.
I was proud to be involved in quickly planning and organizing a joint NLM-ODSS webinar on sharing, discovering, and citing COVID-19 data and code using generalist repositories. It’s been inspiring to see the research community so eager to share the data and tools they’ve been generating, so this workshop felt like a timely and impactful contribution in support of researchers.
Valentina Di Francesco, MS, Lead Program Director, Computational Genomics and Data Science Program, NHGRI
Among the variety of projects I am involved in, I am particularly enthusiastic about the NIH Cloud Platform Interoperability Effort, which aims to establish and implement guidelines and technical standards to empower end-user analyses across participating cloud-based platforms established across NIH in order to facilitate the realization of a trans-NIH federated data ecosystem.
Data science is a science at NIH.
After many years at NIH, only recently have I noticed a solid appreciation of the essential contributions of the statistical, mathematical, and computer science approaches to better understand biological systems. Finally, data science is respected as a field at NIH! I can’t think of a better time to join the ranks of women data scientists in biomedical research.
Kim Pruitt, PhD, Chief, Information Engineering Branch, National Center for Biotechnology Information, NLM
Persevere, find a mentor, understand expectations, persevere.
My advice to someone entering this field is to persevere, to find an excellent mentor, to go into collaborations with a clear understanding of each member’s role and publication expectations, and to continually look for lessons learned when an analysis strategy fails (that is, cycle back to persevere).
Providing data access in the cloud
Providing access to data on the NIH STRIDES Initiative cloud-based platform is a prerequisite to supporting and growing the biomedical data science field. Most notable to me is the significant achievement of providing the complete Sequence Read Archive data (roughly 40 PB and growing) in two formats and ahead of the planned schedule.
Jennifer Couch, PhD, Chief, Structural Biology and Molecular Applications Branch, National Cancer Institute
NIH Citizen Science Coordinator
Bringing new approaches to biomedical research.
My focus is on bringing new, diverse, and often outsider perspectives, tools, approaches, and methods into the biomedical research space. Together with many talented colleagues and collaborators, I look for ways to bring new approaches to biomedical research. Sometimes that involves creating opportunities for different research communities to come together and find ways to collaborate.
On finding the right collaborators.
Hone your skills, don’t be afraid to try out new methods, and find collaborators with interesting questions who will know the answer when they see it. Find those collaborators who appreciate that your skills and insights are critical to your joint project’s success.
Dr. Gregurick leads the implementation of the NIH Strategic Plan for Data Science through scientific, technical, and operational collaborations with the Institutes, Centers, and offices that make up NIH. She has substantial expertise in computational biology, high-performance computing, and bioinformatics.
Guest post by Doug Joubert, head of User Services and the National Information Center on Health Services Research and Health Care Technology, National Library of Medicine.
NLM has a strong record of involving its stakeholders in the strategic decisions that drive the products we develop and the services we offer. As the world’s largest biomedical library, NLM is committed to thinking strategically about how we can promote discovery while supporting the 21st-century data, data science, and information needs of our diverse user community.
As we consider how to better address the needs of everyone who produces or uses health services research, we invite you to be part of the process by responding to this Request for Information (RFI).
Through this RFI, NLM is seeking input on future resource and program directions in support of information related to health services research, practice guidelines, and health technology, including technology assessment. Specifically, feedback is requested on the following:
Products that NLM currently offers in the areas of health services delivery or health services research
Information types necessary for organizations to successfully support health services research or public health
Tools, resources, or health services literature that are the most critical for NLM to collect or support
Any other comments that would enable NLM to support future work related to health services delivery or health services research
The health services research community is supported by NLM’s many databases, tools, and services, including PubMed and PubMed Central, Bookshelf, MedlinePlus, and ClinicalTrials.gov. Our Unified Medical Language System and clinical vocabulary and data standards resources are used by individuals in clinical research and health practice in the United States and globally. Through our intramural and extramural research and training investments in biomedical informatics, computational biology, and genomics, we are advancing projects that address real-world challenges in public health surveillance, opioid intervention, social determinants of health, and other domains. NLM also promotes the use and reuse of data for research and discovery from both research studies and clinical data sources through publicly available national health surveys, diagnostic images, administrative claims, and electronic health records.
At the core of NLM’s service model is meeting the information needs of all those who seek current and trusted biomedical information. To this end, NLM has continued to increase, refine, and evaluate the health services research resources of NICHSR. These efforts reflect the changing needs of users and the ways in which health services delivery is evaluated. Through our products, services, and programs, we continue to strive to support the information needs of researchers, clinicians, health care professionals, policymakers, librarians, and the public.
Doug Joubert is the head of Users Services and the and the product owner for the NLM Health Services Research product portfolio. He supports a team that provides research and information services to the public. He also supports the NLM Strategic Plan by leveraging NLM tools and services to facilitate the management of data throughout the entire lifecycle. Doug works collaboratively to develop and support data science training for NLM Reference and Web Services staff.
This piece was authored in collaboration with the leadership across NIH and represents a unified effort to meet the challenges presented by the COVID-19 pandemic with excellence and innovation.
One thing we know for sure – every single person can help our country control the COVID-19 pandemic. From wearing a mask to washing your hands to maintaining physical distance and avoiding large indoor gatherings, each of us can follow proven public health practices that not only reduce our own chance of getting infected by SARS-CoV-2 (the virus that causes coronavirus disease, or COVID-19), but also prevent the spread of COVID-19 to our coworkers, friends and loved ones. Another thing that will help is testing as many people as possible.
Testing for COVID-19 is so important that in April 2020, the NIH launched the Rapid Acceleration of Diagnostics (RADx) Initiative to develop rapid, easy-to-use, accurate testing and make it available nationwide. As part of this effort, the RADx Underserved Populations (RADx-UP) program is about finding solutions to stop the spread of COVID-19, particularly among racial and ethnic minorities, and other vulnerable populations that have been disproportionately affected by this pandemic. Previously, we reported about the launch of this project and our plans to develop community-based approaches to study how best to implement testing and prevention strategies for populations who are disproportionately affected by, have the highest infection rates of, or are most at risk for complications or poor outcomes from COVID-19.
Scientists from the NIH and across the country are working around the clock to establish programs that will ensure access to and acceptance of rapid and reliable testing around the country. Testing can help people determine if they are infected with SARS-CoV-2 – regardless of whether they have symptoms – and whether they are at risk of spreading the infection to others. Taking measures to prevent the spread of infection will be the most effective strategy for getting us safely back to work and school.
We want to take this opportunity to articulate why widespread testing is necessary, important, and achievable.
Testing saves lives
Testing of all people for SARS-CoV-2, including those who have no symptoms, who show symptoms of infection such as trouble breathing, fever, sore throat or loss of the sense of smell and taste, and who may have been exposed to the virus will help prevent the spread of COVID-19 by identifying people who are in need of care in a timely fashion. A positive test early in the course of the illness enables individuals to isolate themselves – reducing the chances that they will infect others and allowing them to seek treatment earlier, likely reducing disease severity and the risk of long-term disability, or death.
Testing of people who have been in contact with others who have a documented infection is also important. A negative test doesn’t mean you’re in the clear; you could become infectious later. Therefore, even if you test negative, you need to continue to protect yourself and others by washing your hands frequently, physically distancing, and wearing a face mask. A positive test makes it clear that you have to isolate yourself, and that others with whom you have been in contact since the time of your exposure should also get tested.
Since it is recognized that nearly half of all SARS-CoV-2 infections are transmitted by people who are not showing any symptoms, identifying infected individuals while they are presymptomatic, as well as those who are asymptomatic, will play a major role in stopping the pandemic.
Testing can be easy and quick
Initially, the only test available required getting a sample from the back of a person’s throat. New developments, some of which are supported by two other NIH projects, RADx Tech and RADx-ATP (Advanced Technology Platforms), will provide more comfortable and equally accurate tests that obtain the sample from inside the nose. On the horizon for large-scale use are tests that will use a simple mouth swab or a saliva sample.
A positive test for SARS-CoV-2 alerts an individual that they have the infection. Not only can they get treated faster, but they can take steps to minimize the spread of the virus.
This is why it is so important to get the test results quickly, ideally within a few hours or less.
Early in the pandemic, there was not enough capacity and limited supplies to collect and process the tests, which resulted in delays. However, lab equipment has improved, capacity and supply have expanded, and results are being returned, on average, within 3-4 days. In fact, point-of-care tests will be available that provide a result in less than 15 minutes!
Testing matters more in the communities affected the most
Communities of color are disproportionately burdened by the COVID-19 pandemic. Some individuals in these communities are essential workers, who cannot work from home, increasing their risk of being exposed to the virus. In addition, multi-generational living situations or multi-family housing arrangements can allow the virus to spread more quickly if one household member gets infected. Comorbid conditions that worsen the health risks of COVID-19, such as heart disease, obesity and diabetes, are also more common in minority communities because of long-standing societal and environmental factors and impediments to healthcare access. Therefore, COVID-19 can spread quickly in these communities, and the impact of that spread is great. Testing, particularly of asymptomatic and pre-symptomatic individuals, is key to interrupting this spread.
Unfortunately, there still is a lot of confusion about where to get a test and who should get tested. It is becoming clear that for a person to test positive, they have to have a significant amount of the virus in their system. This means that if you have no symptoms but think or were told that you were in contact with a person with COVID-19, you should isolate yourself immediately, call your health care provider, and then get a test. If you have any question, always call your health care provider or local county public health office. You can also contact the CDC Hotline at 800-CDC-INFO (800-232-4636).
Staying informed is essential. We encourage you to look to up-to-date, trusted sources of information about COVID-19, such as resources from the NIH website or MedlinePlus, the National Library of Medicine’s consumer information resource.
Over the next few months, you’ll have opportunities, such as those listed at the NIH’s vaccine trial sites, to help scientists discover if the vaccines being evaluated now are effective. If you become ill with COVID-19, you can to participate in clinical trials underway to develop and evaluate a wide range of potential treatments, as well as several possible vaccines. So that these therapies will work for everyone, it is important for people from diverse communities across the country to participate in this research. We hope that in the not too distant future, these efforts will lead to therapies that will put an end to the pandemic.
In the meantime, let’s all continue to protect ourselves and others from getting infected, and get tested if you believe you have been in contact with someone with COVID-19.