Tracking COVID-19 spread faster, more accurately

(Image of two circular groups of dots, both of which are created by multiple layers of dots, with an arrow between them pointing from the left to right. The first circle has countless lines connecting all of the dots; the second shows fewer, more organized lines connecting only a few internal layers of dots.) — Ying’s algorithm reconstructs the spread of an entity such as an illness from single or multiple sources. (Image courtesy of Lei Ying.)

Lei Ying, a professor of electrical engineering and computer science, is developing algorithms to quickly and accurately identify COVID-19 “patient zero” and reconstruct the virus’s path with limited information.

Ying’s approach combines big data, network science, and stochastic systems, using information such as human mobility data, social network data, and genetic network analysis to track the spread of the virus. The project focuses on establishing a theoretical framework for locating the original source of anything that has spread so quickly.

Identifying the original source can help explain how the disease was transmitted, says Ying, and in turn reveal modes of transmission, dangerous exposure locations, and high-risk individuals.

As states reopen and face the possibility of additional waves of COVID-19, tracking the spread of infections in specific locations could lead to swifter decisions for individual quarantining, ultimately saving lives.