Coronavirus, responsible for the respiratory disease known as COVID-19, was first confirmed in the UK at the end of January. Professor Chris Whitty, the UK chief medical adviser, said he was expecting the numbers to “increase initially slowly, but really quite fast after a while, and we have to catch it before the upswing begins”.
Scientists are using complex mathematical models to attempt to predict the spread of coronavirus.
These evolutionary changes have a huge impact
Professor Osman Yagan
They typically study the first few steps in which the subject spreads, and use that rate to project how far and wide the spread will go.
A new Carnegie Mellon University study has for the first time revealed the importance a mutating pathogen on changing the speed at which coronavirus spreads.
Osman Yagan, an associate research professor in Electrical and Computer Engineering and the study’s author, said: “These evolutionary changes have a huge impact.
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“If you don’t consider the potential changes over time, you will be wrong in predicting the number of people that will get sick or the number of people who are exposed to a piece of information.”
Although many are familiar with epidemics of disease, information itself, traveling at light speed over social media, can experience its own kind of epidemic and “go viral”.
Whether a piece of information goes viral or not can depend on how the original message is tweaked.
Professor Yagan said: “Some pieces of misinformation are intentional, but some may develop organically when many people sequentially make small changes like a game of ‘telephone’.
“A seemingly boring piece of information can evolve into a viral tweet, and we need to be able to predict how these things spread.”
The researchers developed a mathematical theory that takes these evolutionary changes into consideration.
They then tested their theory against thousands of computer-simulated epidemics in real-world networks, such as Twitter for the spread of information or a hospital for the spread of disease.
In the context of spreading of infectious disease, the team ran thousands of simulations using data from two real-world networks: a contact network among students, teachers, and staff at a US high school, and a contact network among staff and patients in a hospital in Lyon.
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These simulations served as a test bed: the theory that matches what is observed in the simulations would prove to be the more accurate one.
Dr Rashad Eletreby, the study’s first author, said: “We showed that our theory works over real-world networks.
“Traditional models that don’t consider evolutionary adaptations fail at predicting the probability of the emergence of an epidemic.”
Although the study does not claim to be capable of predicting the spread of coronavirus or the spread of news in today’s volatile political environment with total% accuracy, the authors are confident a big step has been made.
Dr Eletreby said: “We’re one step closer to reality.”
The landmark study coincides with the news more than 370 cases of the coronavirus have been confirmed in the UK.
Prime Minister Boris Johnson describes the outbreak presents “significant challenges” to the country.
Almost 26,000 people in the UK have been tested for the virus so far, with 373 found to be positive for the respiratory infection.
Six people who tested positive have died as of today.
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