TECH MATTERS: What's the role of technology during an epidemic?

Information and technology are both playing a crucial role in tracking and monitoring cases of Coronavirus 2019-nCoV.
Workers inspect face masks at the Yokoi Co. Ltd. factory in Nagoya, Japan. (Tomohiro Ohsumi/Getty Images)

Where are you getting information about the coronavirus?

Information and technology are both playing a crucial role in tracking and monitoring cases of the coronavirus. 

Technology is also playing a role in the spread of information – or disinformation – as authorities struggle to control the virus.
Aaron Langille, a computer science professor at Laurentian University, said one of the most challenging things to track is the contact infected people have with others.

"That's one of the most important sources of data when you're trying to get ahead of something that sort of in the epidemic status," Langille said. "Is how do you figure out who those people that are currently infected were in contact with."

The ultrastructural morphology exhibited by the 2019 Novel Coronavirus (2019-nCoV), which was identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China, is seen in an illustration released by the Centers for Disease Control and Prevention (CDC) in Atlanta, Georgia, U.S. January 29, 2020. (Alissa Eckert, MS; Dan Higgins, MAM/CDC/)

Airlines in particular were able to provide what Langille calls "easy data"– the names of people on a plane who may have sat near an infected person.

"But when somebody goes to a market, or when children that are infected go to school, for example, some of those things aren't quite as easy to track." 

In the future, Langille said health providers may be able to track possible infections using RFID technology, similar to the RFID tags used in clothing to trigger security alarms.

"They've done a handful of studies, particularly in hospitals with medical practitioners, doctors, nurses, technicians and patients in various critical care units," Langille said. 

"They'll put a tag on a practitioner, they'll put a tag on a patient and then they'll track how many interactions actually happen at close proximity, and the number is actually quite high," Langille said. 

"They've done similar studies in controlled environments like high schools and universities, as well. And then they simulate sort of what an outbreak would look like and what those contact points would do." 

Aaron Langille is a professor of computer science and game design at Laurentian University in Sudbury. (Aaron Langille/Supplied)

Researchers are also looking at cell phones as a way of tracking data, Langille said.

"If somebody shows positive signs of a particular pathogen [researchers] can look at their phone records and see exactly where they've been. Not just roughly, but sort of a G.P.S. location of everywhere that they've been in the last week, the last 48 hours."

Apps, too, are being developed to help users keep track of their own symptoms and stay aware of possible locations where the virus is present.

"You pop in to the app, you write down your symptoms, you say 'I've been positively identified for'.. it could be as simple as the flu, or could be something more complicated."

"Then that gets uploaded to a particular site or data center which can then be shared with other people to track the spread of whatever you happen to have reported," Langille said. 

John Hopkins University is mapping real-time cases of the Coronavirus 2019-nCoV. (

Groups are already at work trying to map out the coronavirus and study its behaviour, he added. 

"You can bet that there's a number of groups in centres that are looking at its spread and comparing it to models, so, computer-based simulations of the transmission of these kinds of pathogens, diseases and outbreaks to see whether or not it's tracking the way that they thought."

The Center for Systems Science and Engineering (CSSE) at John Hopkins University has created a real-time tracking of coronavirus cases. 

Langille said the idea behind inputting the right kinds of data is to integrate "deep learning" into following the virus' spread.

"If we have people that are in this densely populated metropolitan area, how fast do we expect it to spread, and also introducing things like travel patterns and environmental conditions, which have an impact as well."

"In theory, the more data we feed into the model,  the more accurate they get at predicting these kinds of things in the future."


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