New York, June 30 (IANS) In the fight against Covid-19, researchers have developed a strategy to use data from existing cellular wireless networks to identify areas that are at the greater risk of disease.
The new method helps identify places where asymptomatic carriers have a higher chances of coming in close contact with large numbers of healthy people.
The technique, published in the IEEE Open Journal of Engineering in Medicine and Biology, could help regions manage risks and avoid scenarios like the recent outbreak in New York in the US.
"Our findings could help risk managers with planning and mitigation. It might prompt them to cordon off a busy plaza, for example, or implement stricter social distancing measures to slow the spread of virus," said study researcher Edwin Chong from Colorado State University in the US.
The researchers tried to understand how mobile device users moved and gathered over time in an area by leveraging what's known as handover and cell (re)selection protocols - the cellular network technologies that allow us to move about freely with our mobile devices without losing service.
Using data collected through these networks, Chong's team measured handover and cell (re)selection activity, called HO/CS rates, to calculate localised population density and mobility. Offering real-time updates, the data allowed them to flag at-risk areas for further monitoring.
The method was built on the premise that the higher the HO/CS rates, which meant higher density and mobility, the higher the risk of spreading infectious diseases.
Chong said it could also be used to estimate the percentage of people staying home to determine whether communities were following recommended public health policies.
While Chong refers to mobile devices as "always-on human trackers," he is sensitive to and concerned with privacy and security issues. Unlike contact tracing applications that are often difficult to deploy and required widespread adoption, his approach protects the privacy and anonymity of individuals without needing active participation from device users.
"Our method overcomes the downside of contact tracing apps. All we have to do is to do the measurements using anonymous data that is already being collected for other reasons. We are not tracking individuals," the study author said.