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Technology Could Be Key in Tracking COVID-19 Spread

Technology Could Be Key in Tracking COVID-19 Spread

While diagnostic testing is crucial to staying on top of the illness’ spread, some tech could also lend a hand

Rachel Muenz

Though diagnostic testing is currently one of the best ways to monitor the spread of COVID-19 in the US and the rest of the world, testing capacity remains a challenge with many tests still in development and labs facing a large backlog.

However, there are a few technologies that could help ease the strain.

The US Centers for Disease Control and Prevention recently launched a chatbot called Clara that helps people determine whether they should contact a health care provider about COVID-19. While not intended to diagnose COVID-19 or suggest treatment options, the bot may help ease the demand on health care systems by steering those who are just paranoid about regular cold symptoms away from their local hospitals.

The bot, labeled “Coronavirus Self-Checker” on the CDC website, was developed through a partnership with the CDC Foundation and uses Microsoft’s Azure cloud platform.

Some technologies that could help further down the line include a monitoring drone and a portable device that uses artificial intelligence to detect crowd size and coughing in waiting rooms.

The AI device, created by University of Massachusetts Amherst researchers, is called FluSense and could be used to predict not only seasonal influenza outbreaks but also outbreaks of respiratory illnesses such as COVID-19 which involve similar symptoms.

Similarly, the “pandemic drone” in development by University of South Australia (UniSA) researchers and Canadian company Draganfly Inc, could help remotely detect people with illnesses such as COVID-19.


Related Article: Recent Developments in COVID-19 Diagnostic Testing


Like FluSense, the drone will also be able to detect coughing and sneezing in public places through its computer vision system and sensor. It can also monitor respiratory and heart rates as well as temperature. However, it’s not clear whether there would be privacy issues around the use of the drone.

UniSA and Draganfly aim to get the technology to government, medical, and commercial customers right away, given the more pressing need for the drone,   which was originally meant for use in areas hit by natural disasters, war zones, and for monitoring premature babies in incubators, according to a UniSA press release.

“We see a need for its use immediately, to help save lives in the biggest health catastrophe the world has experienced in the past 100 years,” says Javaan Chahl, one of the UniSA researchers working on the project. “It might not detect all cases, but it could be a reliable tool to detect the presence of the disease in a place or in a group of people.”

As for technologies currently helping in the COVID-19 fight, the computer science world has been a big boost in general with supercomputers, AI, and computer modeling playing a big role in developing treatments and vaccines for COVID-19, as while as monitoring its spread.

For example, a team at University of Texas Health Science Center at Houston used AI to create a computer model based on COVID-19 cases in Italy and China to predict when the virus will peak in Houston, depending on when “strict interventions” are put in place.

Also in Texas, scientists are creating a gigantic computer model of SARS-CoV-2, the virus that causes COVID-19, to better understand how it infects the human body. Researchers recently began testing the model’s initial portions on the Frontera supercomputer at the University of Texas at Austin's Texas Advanced Computing Center. It’s expected the complete model will be a big help in efforts to create vaccines and treatments for COVID-19.

"These simulations will give us new insights into the different parts of the coronavirus that are required for infectivity,” said Rommie Amaro, a professor of chemistry and biochemistry at the University of California, San Diego who is leading development of the model. “Why we care about that is because if we can understand these different features, scientists have a better chance to design new drugs; to understand how current drugs work and potential drug combinations work. The information that we get from these simulations is multifaceted and multidimensional and will be of use for scientists on the front lines immediately and also in the longer term.”