Source: Eric A. Blair
The coronavirus that swept across the U.S. in the past several months, peaking in mid-April before starting its predicted slide, could be over as early as mid-November, new modeling shows.
Researchers at Singapore University of Technology and Design have designed a complex model “predicting the exact date the pandemic will end” in countries around the world, the Daily Mail reported.
“According to the data, the US is on track to be coronavirus-free by November 11, while the UK could see an earlier end date of September 30. The model predicts the trajectory of the spread of the virus over time while tracking the actual number of new confirmed cases per day in a given country,” The Mail said.
In the US, changes in predictions were tracked over a one-week period between May 6 and May 12, and found to be relatively stable, suggesting a ‘long time to reach its theoretical ending’.
‘The estimated curves of USA for a week together, showing a high stability, while one might still want additional policies or actions to further shorten the tails of the curves,’ the report states.
The study also found predictive monitoring in early May showed the US – and second worst-hit country Brazil – could ‘still suffer’ for the remainder of the year if current measures remain in place and without the development of a vaccine.
For Italy, which once led the world in confirmed coronavirus cases, the modelling showed it was predicted to recover by October 24, as of May 8.
But the scientists said the data could change, pushing back their predicted dates. ‘The model and data are inaccurate to the complex, evolving, and heterogeneous realities of different countries over time. Predictions are uncertain by nature,’ the report said.
“Over-optimism based on some predictions is dangerous because it may loosen our disciplines and controls and cause the turnaround of the virus and infection, and must be avoided.”
As of Saturday, there were 1,622,990 confirmed cases of COVID-19 in the U.S. and 96,046 deaths, according to Johns Hopkins Center for Systems Science and Engineering.