This technique is usually used to pair computer simulations with real weather observations to forecast future weather, the researchers said
London: Scientists have employed methods normally used to forecast weather to predict how rapidly COVID-19 could spread in different countries as lockdown is eased, as well as assess the effectiveness of measures put in place.
An international team, including meteorologists from the University of Reading in the UK, applied data assimilation, a technique that combines multiple sources of information to estimate how a situation will develop over time, to the pandemic.
The study, submitted to the journal Foundations of Data Science, suggests it is possible to make reasonably accurate predictions of how easing measures might affect the spread of the virus up to two weeks in advance.
This technique is usually used to pair computer simulations with real weather observations to forecast future weather, the researchers said.
"A key result from this work is that we can estimate accurately how the reproductive (R) number varies in time in response to implementing or loosing up various mitigation measures," said Professor Geir Evensen, from the NORCE: Norwegian Research Centre, who led the study.
R number is the number of people a person with COVID-19 is likely to infect.
Previous computer model forecasts can be tested against the subsequent weather data to help make future short-term predictions more accurate, the researchers said.
When applied to the novel coronavirus, observations including hospital admissions, the number of patients in intensive care, and the number of daily deaths can be combined with models calculating risk of vulnerability, exposure, infection and death, according to the researchers.
"Most data is uncertain to some degree, but combining as much of it as possible from different sources can iron out some of this uncertainty when predicting future events," said Javier Amezcua, one of three Reading scientists that worked on the study.
"Meteorologists use this method all the time to understand and forecast natural processes like weather, but its uses extend beyond that," Amezcua said.
The study allows estimations to be made of how situations will develop in different scenarios, and can be used to create longer-term forecasts, the researchers said.
This means it could be useful to predict the impact changes of lockdown policies, such as reopening schools and shops or increasing permitted socialising, might have on the spread of infections, they said.
The team applied the technique to estimate coronavirus spread in eight different countries around the world -- England, France, the Netherlands, Norway, the US, Canada, Brazil and Argentina -- which have all seen the virus spread in different ways.