RADx-rad: The Role of 

Non-traditional 

Approaches in COVID-19 

Pandemic Response

 

MARCH 2024
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Table of Contents

Executive Summary .................................................................................................2

Program Overview and Structure..........................................................................2

Program Accomplishments.....................................................................................5

Community Engagement.........................................................................................8

Impact and the Path Moving Forward ..................................................................9

Conclusion...............................................................................................................10
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Executive Summary 

The COVID-19 pandemic presented a severe and imminent threat to lives of Americans, the 

healthcare system, and the economy. In response, the National Institutes of Health (NIH) 

launched the Rapid Acceleration of Diagnostics®(RADx) initiative to accelerate the 

development, commercialization, and implementation of innovative technologies for COVID-19 

testing. This was no small feat, as accurate, fast, easy-to-use, and widely accessible testing was 

required for effective monitoring, early treatment, and prevention of the spread of COVID-19 

before citizens could safely return to normal life. To accomplish these testing goals, the NIH 

created several programs, such as RADx Radical (RADx-rad), that made this possible. 

The NIH RADx-rad program supported new, non-traditional approaches, to address the gaps in 

COVID-19 testing and detection. These innovative research programs included the development 

of devices for the rapid detection of SARS-CoV-2 and other pathogens, novel approaches for 

environmental detection of infectious agents including SARS-CoV-2, and the generation of tools 

for predicting and diagnosing severe COVID infection as well as the long-term effects of 

infection known as Post-Acute Sequelae of SARS-CoV-2 (PASC) infection or Long COVID. 

Collectively, these proposals introduced “radical” ways to predict, detect, and diagnose 

conditions resulting from SARS-CoV-2 or other infectious agents. 

Overall, RADx-rad provided funding to support innovative research projects focused on 

developing novel platforms to detect infectious diseases and to evaluate community spread. 

Unlike other RADx programs, projects within the RADx-rad portfolio focused on new or nontraditional approaches, which may require additional time for development and could potentially 

be applicable to future pandemics and other health conditions. 

Program Overview and Structure 

In the spring of 2020, planning for RADx-rad commenced when Institutes and Centers (ICs) 

across the NIH were invited to submit concepts to improve testing for COVID-19. The 

overarching goal of RADx-rad was to support the development of novel, existing, and nontraditional approaches, and applications of tools to address the gaps in COVID-19 testing. From 
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the initial group of proposals, the NIH leadership selected a subset of concepts to be further 

developed into funding announcements. Researchers from across the U.S. submitted creative 

applications, from which 49 awards were distributed in December of 2020. The RADx-rad 

program was divided into two phases, each of which is briefly described below in addition to a

detailed list of all the RADx-rad funded projects. 

During Phase I, an estimated $108 million was provided to support the development of new, 

non-traditional approaches for COVID-19 testing. These non-traditional approaches include 

unconventional screening, identification, and use of biological or physiological markers, 

development of point-of-care devices, and utilization of data science and artificial intelligence 

technologies. Further, RADx-rad provided an opportunity for researchers to explore and identify 

new approaches to understand SARS-CoV-2, which could potentially be used in the future to 

expedite the research response to new pathogens. These research projects were funded across the 

following eight categories including:

• Automated Detection and Tracing of SARS-CoV-2: These projects consisted of the 

early-stage development of an innovative platform that integrates virus-sensing elements 

with touchscreens or other digital devices to achieve automatic, real-time detection and 

tracing of SARS-CoV-2.

• Chemosensory: This approach consisted of the development of new, objective 

chemosensory tests to be used as COVID-19 screening tools for their use at home and in 

a wide variety of settings. 

• Exosome-based Selection: Newly developed technologies and approaches for single 

exosome and extracellular RNA (exRNA) isolation and analyses were deployed for the 

detection of SARS-CoV-2 virus RNA and/or protein, and detection of IgA, IgG, and IgM 

antibodies that are part of the host response to viral infection.

• Multimodal Surveillance: These projects consisted of identifying existing surveillance 

technologies that could be rapidly deployed in communal living contexts. Additionally, 

this category included projects that explored novel methods of data collection,
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interpretation, and use of machine learning (ML) to facilitate broad-based, real-time 

assessment of the needs of high risk, vulnerable populations. 

• Novel Biosensing: This category was comprised of the development and integration of 

novel biosensing detection technologies with dedicated engineering and artificial

intelligence (AI) systems. The goal of these technologies was to leverage the accessibility 

of human skin, breath, and the oral cavity to detect biological, chemical, and other 

biometric signatures of COVID-19. 

• Predicting Viral-Associated Inflammatory Disease Severity in Children with 

Laboratory Diagnostics and Artificial Intelligence (PreVAIL kIds): This opportunity 

was created to support the development of novel, unique, and non-traditional approaches 

to identify and characterize the spectrum of SARS-CoV-2-associated illnesses, such as 

Multisystem Inflammatory Syndrome in Children (MIS-C). Algorithms generated 

through this research were used to predict the longitudinal risk of disease severity after a 

child has been exposed and infected with SARS-CoV-2 to tailor their treatments and 

optimize their health outcomes.

• Screening for COVID by Electronic-Nose Technology (SCENT): Electronic nose and 

gas chromatographic technologies were developed to monitor the onset, progression, and 

resolution of COVID-19. These technologies used highly sensitive olfactory biosensors 

to detect volatile organic compounds (VOCs) which are produced in the host as a 

response to a pathogen or other insult. These steps included detection of the compounds, 

identification of a signature that reflects COVID-19 variants and/or severity of disease, 

and then integration of this into a detection platform. The VOC signatures of COVID-19 

could be detected in human skin, breath, and the oral cavity. Technologies based on gas 

chromatography were also developed into portable VOC (odor) breath analyzers for their 

application in intensive care units, emergency rooms, and ambulatory clinics.

• Wastewater Detection of SARS-CoV-2: Wastewater-based detection (WBD) platforms 

provided a detailed mapping of the extent and spread of SARS-COV-2 in communities. 

WBD was shown to precede outbreaks of infection as detected in the clinical samples. 
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The community wastewater analysis method served as a complementary approach to 

individual-level testing and screening.

In addition, RADx-rad was supported by a Data Coordination Center (DCC) which provided 

management, direction, and overall coordination across RADx-rad awardees in areas such as 

data collection and sharing (e.g., data upload to the NIH RADx Data Hub), data management 

standards and terminologies, and provided standardized viral samples and testing protocols. 

Following the initial set of awards, the RADx-rad program launched Phase II in the spring of 

2021, which provided an additional ~$75 million in NIH funding (including access to RADx 

core resources and entrepreneur consultation, as well as additional funding support for assistance 

to projects that demonstrated the potential for commercialization). The Long COVID 

Computational Challenge (L3C) launched during Phase II aimed to support creative, data-driven 

solutions to advance the current understanding of the risks of PASC/Long COVID. The goal of 

L3C was to support development of AI or ML models to determine the likelihood of patients 

infected with SARS-CoV-2 of developing PASC. Six prizes totaling a purse of $500,000 were 

awarded, and the winning algorithms were shared as open-source software to enable further 

development by the scientific community. 

Program Accomplishments 

The RADx-rad program demonstrated the value of a collaborative system to develop novel 

platforms and methods to detect SARS-CoV-2 and monitor community spread. Over the course 

of the program, RADx-rad published 13 funding opportunity announcements and awarded 49 

projects. Some highlights from the program are: 

• The RADx-rad WBD researchers were able to detect COVID-19 variants in various

wastewater collection sites and the same variants in clinical samples using improved

technologies for collecting and managing the samples to minimize human contact, and

transport and processing of the samples.

• The RADx-rad PreVAIL kIds researchers developed various algorithms based on

standard emergency department lab tests and clinical observations. These processes
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enabled physicians to differentiate between serious childhood diseases such MIS-C,

Typhus, Kawasaki disease, and other febrile diseases within a 24-hour period, thereby 

preventing discharge of individuals who require more intensive treatment.

• Researchers developed a rapid saliva-based test that can differentiate between gram 

positive and gram negative-bacterial infections and viral infections. This test has broad 

implications for public health in combatting the emergence of antibiotic resistant 

pathogens by allowing clinicians to tailor the use of antibiotics for bacterial infections 

and eliminate the inappropriate provision of antibiotics for viral-based conditions.

• RADx-rad researchers used AI and ML technologies to determine that VOC signatures in 

breath significantly differed in response to variants of COVID-19 in the host. Early 

results showed a difference in VOC biomarkers between children and adults with 

COVID-19. Currently, scientists are exploring the patterns of VOCs in PASC.

• RADx-rad researchers developed biosensing technology for rapid and ultrasensitive 

SARS-CoV-2 detection capitalizing on the trimeric assembly of viral surface proteins. Its 

simplicity and specificity made it promising for on-site testing in various settings, 

including airports and clinics. The study also explored its adaptability for detecting other 

viruses and potential use in diagnosing Long COVID, showcasing its significance in 

infectious disease diagnostics.

• Scientists developed and validated new objective chemosensory tests that are affordable, 

highly scalable, self-administered and globally deployable as screening tools for COVID19. This was especially relevant to viral variants that frequently resulted in loss of taste 

and/or smell and is now being applied to other neurological conditions.

• RADx-rad researchers integrated radical ideas in virus sensing and device designs to 

develop several prototype devices for SARS-CoV-2 detection, ranging from an 

environmental detector, breathalyzer, touch surface detection, to pocket-sized devices 

that can be coupled with a contact tracing application. These innovative platforms can

enable automatic real-time virus detection and tracing, which will prepare us to face the 

challenge of viral outbreaks in future pandemics. High innovations of these virus 

detection platforms have attracted private investments to develop some of these 
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technologies with multiplexing capabilities further towards commercialization for 

preparation for future pandemics.

• During Phase II, L3C was a community-based challenge which supported the

development of creative data-driven solutions that meaningfully advance the current

understanding of the risks of developing PASC. The top three winners for the challenge

were:

• First Place: University of Chicago’s Convalesco team built a real-time monitoring

system based on LightGBM and XGBoost that updated a patient’s risk for

developing PASC/Long COVID as new clinical events occurred. Clinical features

for this model included conditions from the acute phase that are associated with

PASC (such as fatigue, pain, weakness, and dyspnea) as well as prior viral

exposure of any type, bloodwork, oxygen saturation, and certain drug exposures.

• Second Place: Geisinger AI Lab built a portable, efficient, and accurate model

using fewer features than the competition, resulting in a translational PASC/Long

COVID prediction clinical decision support tool that included EHR-ready

summary visualizations for clinician interpretation.

• Third Place: University of California, Berkeley team built a clinical prediction

model that combined many smaller prediction models (this combined model was

known as an ensemble or a super learner). The model used various aspects of a

patient’s health such as their cardiovascular health, respiratory health, history of

hospital use, and age to predict the patient’s risk for developing PASC/Long

COVID.

Throughout the RADx-rad program, 32 projects filed patents or released as open-source, one 

project submitted an Emergency Use Authorization (EUA), seven projects submitted pre-EUAs, 

23 projects met with the Food and Drug Administration (FDA) for authorization of their products 

for emergency use, 26 projects teams submitted primary publications, and 10 projects were 

awarded commercialization support. Overall, the accomplishments of the RADx-rad program 
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emphasized the potential of these innovative methods to be applied to other and future pathogen 

detection, surveillance, and diagnostics.

Community Engagement 

Because COVID-19 disproportionately affected the most vulnerable populations, a critical 

component of the pandemic research was to involve the community and collaborate with other 

scientists to improve health outcomes. RADx-rad funded 49 extramural research projects at 45 

unique institutions across 20 states. Ninety-six teams registered to participate, and 35 teams 

submitted completed models as part of L3C. In combination, the teams represented academic 

institutions, medical centers, technology and data firm industry partners, and a private-public 

partnership. 

Throughout the RADx-rad program, awardees had the opportunity to interact with the 

community. For example, the WBD category funded seven projects throughout the U.S., which 

focused on using wastewater to estimate population-level data within communities and to reduce 

COVID-19 spread throughout. During these projects, researchers had the opportunity to 

collaborate with local governments in both urban (e.g., New York City, New York; Tempe, 

Arizona) and rural communities (e.g., mobile vans which serviced counties in Kentucky), 

universities, such as those with contained wastewater systems, Tribal communities, and private 

sector partners to conduct their studies. The new community collaborations helped to identify the 

circulation of new variants prior to clinical detection. Furthermore, PreVAIL kId’s funded eight 

projects, whose goal was to develop prognostic algorithms to predict disease severity of 

COVID-19 in infants to adolescents. Across each of the eight studies, researchers partnered with 

over 45 domestic sites and nine international sites, which contributed to the enrollment of over 

8,000 participants. By fostering and sustaining new academic- community partnerships to carry 

out this meaningful research, the innovative and novel applications developed during the RADxrad program can be applied to future pandemics and public health emergencies. 
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Impact and the Path Moving Forward 

As the post-pandemic world continues to take shape, it is important to apply the lessons learned 

over the past few years. Several processes have been proven beneficial to the ongoing success of 

the program. First, the RADx-rad program established a working group of committed Program 

Officers. Clear, early guidance helped resolve Common Data Elements (CDEs) in advance of 

application receipt which ensured data quality and consistent data collection. Second, the 

program tailored its support and funding to the needs of the investigators. The program partnered 

with the NIH Small Business Education and Entrepreneurial Development (SEED) Office to 

support the investigators as they worked to bring their products to market. Finally, the program

provided opportunities for investigators to engage with the FDA prior to the submission of their 

applications for authorization. Some investigators are continuing to work with the SEED office 

as their projects move closer to commercialization. Lessons learned on navigating and 

expediting the regulatory process from FDA officials, and bringing a product to market from the 

SEED entrepreneurs in residence as well as the various core supports has positioned both 

individual investigators and the NIH as a whole to tackle future health emergencies. Overall, the 

RADx-rad program brought together academics, industry partners, and community members 

from across the U.S. to develop innovative approaches and reimagine uses of existing tools to 

detect and combat the SARS-CoV-2 virus, its variants, and emerging infectious diseases. 

The project teams are continuing to collect longitudinal data and develop their technologies to be 

used in detecting other diseases. Notable among future plans are: 

• SCENT is exploring the use of breath analysis for rapid screening of illicit drugs, and for

the detection of PASC, Diabetes, Respiratory Syncytial Virus (RSV), Influenza, and

Lung Cancer

• The Chemosensory Testing teams are assessing how to encourage the routine testing of

loss of olfaction function, which can be a symptom of viral infections, brain injuries, and

neurodegenerative diseases such as Parkinson’s and Alzheimer’s
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• The Exosome-Based project teams are working to develop technologies to be used for

detecting multiple viruses, characterizing cancer biomarkers, and predicting response to

therapies

• The WBD teams are expanding their testing to other viruses, bacteria, and illicit drugs.

• Multimodal Surveillance project teams are collecting longitudinal data on COVID-19

outcomes, including mortality and hospitalization rates

• The Novel Biosensing teams are working to develop a multiplexing test for COVID-19,

RSV, and Influenza

• PreVAIL kIds teams are working to implement the new KIDMATCH algorithm which

calculates a risk score for the development of MIS-C and Kawasaki disease

• Automatic Detection and Tracing teams are improving the accuracy of the testing and are

planning to expand to include the detection of other viruses such as Human

Immunodeficiency Viruses, Influenza, RSV, and Dengue Fever

Overall, the novel concepts developed from the RADx-rad program will provide an array of

devices and strategies to tackle the next pandemic as well as inform the detection and treatment 

of other health conditions. 

Conclusion 

In summary, the RADx-rad program provided a mechanism to support innovative projects to 

complement other research regarding detection and testing of SARS-CoV-2, and to improve 

diagnosis of acute and long-term conditions associated with COVID-19. Using a variety of 

technologies such as nucleic acid driven micromotors to AI generated algorithms, RADx-rad 

funded applications showed the various ways technology and informatics can be used to support 

public health responses to future pandemics. Furthermore, RADx-rad illustrated how a broad 

range of scientific collaborators across various sectors and engagement with community 

members can leverage technologies and informatics to develop new tools or improve existing 

ones for improved detection and testing during a pandemic. Although, the RADx-rad program is 

scheduled to conclude by the end of 2024, the tools developed during the program and the 

lessons learned will be used to address future public health crises.