The National Institutes of Health (NIH) awarded over $107 million for what it describes as “radical approaches” to testing for COVID-19 and tracking the spread of the disease—including cutting-edge technologies that could make regular coronavirus testing less intrusive and part of our daily lives.
This includes nontraditional and repurposed technologies to address the current gaps in the current nationwide surveillance effort as well as methods that may be deployed against future outbreaks of the coronavirus and other diseases. In total, the awards will fund 49 projects at 43 U.S. research institutions.
Examples include the development of breathalyzers capable of diagnosing a COVID-19 infection as well as detectors that can identify the airborne virus in open spaces, such as meeting rooms and hospital hallways.
Separate projects will focus on sensors that can pick up the virus’s signature through contact with human skin and potentially integrating them with touchscreen and digital platforms for automatic detection.
“To solve a problem as complicated as COVID-19, we need ideas, tools and technologies that challenge the way we think about pandemic control,” NIH Director Francis Collins, M.D., said in a statement.
The grant program is part of the institutes’ Rapid Acceleration of Diagnostics initiative, or RADx, which includes the government’s $1.5 billion competitive competition for scaling up new testing technologies as well as efforts to engage underserved populations.
“These awards from the RADx-rad program provide superb examples of outside-the-box concepts that will help us overcome this pandemic and give us a cadre of devices and tactics to confront future outbreaks,” Collins said. The radical technologies effort has a total budget of $200 million, according to the NIH.
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Additional research programs include novel tests for gauging a person’s ability to smell and taste to provide earlier warnings to people at risk for COVID-19.
More broadly, the awards will fund data collection methods that use wastewater and sewage to estimate coronavirus infections at a community level as well as artificial intelligence systems that seek out signs of the disease in clinical data and predict the long-term risks for children exposed to the virus.
