CAPE TOWN – A team of researchers has developed a computer model that predicted the spread of Covid-19 in major cities accurately by analysing key factors that contribute towards infection rush such as where people go during the day, how long they stay for and how many people visit the same place at the same time.
The study report, which was published in the journal Nature, merged demographic data, public virus control measures and estimates as well as anonymous cellphone location data which had confirmed ‘superspreader’ locations were a hotspot for the transmission of Covid-19.
“We built a computer model to analyze how people of different demographic backgrounds, and from different neighborhoods, visit different types of places that are more or less crowded. Based on all of this, we could predict the likelihood of new infections occurring at any given place or time,” said Jure Leskovec, the Stanford computer scientist who led the research and development which included researchers from the Northwestern University.
The researchers tracked the movements for around 98 million Americans in 10 of the country’s biggest metropolitan areas including various establishments from finding the risk of transmission high in places such as restaurants, cafes and fitness centres where people remain in confined spaces for extended periods of time.
The study made use of SafeGraph, a company that collects anonymised location data from mobile application, which provided the researchers with data on around 553,000 public locations to determine places that were visited each day, for how long they were there for and the size of the establishment to determine the density of the space.
The information was then used to develop a computer model that predicts the possible spread of Covid-19 with the researchers believing their system could serve as a vital tool for officials to help reduce the spread of the virus, assist the reopening of businesses and establishments in certain areas with development of a user-friendly version underway in hopes to assist the general public.
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