New model predicts near-end of new, confirm­ed novel coronavirus infections in China by February 23

Based on publicly available data, a data model on a recently launched website set up by an ad-hoc group of scientists predicts the number of new, confirmed novel coronavirus cases in China to dwindle to almost nothing in under two weeks.

"The current model predicts a rapid decrease in new, confirmed cases in the next week and virtually no new, confirmed cases by February 23," said Dr Yi Zou, who is based in the Department of Health and Environmental Sciences at Xi'an Jiaotong-Liverpool University.

"While this appears to be a relatively rapid near-end to new, confirmed cases, we must keep in mind that factors could change the trend or that the data on current cases could be underreported, which would change the prediction," he said.

"However, the current model as of February 10 shows hope for the near future."

The model is a logistic equation based on standard mathematics.

"According to the data we collected, the virus disease pattern shows a classic sigmoid function curve," he said.

"The curve looks like a stretched version of the letter 'S.' We appear to be at the top curve of the 'S,' where the top curve indicates the maximum number of cases.

"We will continue to update this model as data comes in to keep the public abreast of the latest prediction."

Four researchers from universities in China – three from Xi'an Jiaotong-Liverpool University and one from Southern University of Science and Technology – created the website to provide scientists, journalists and other researchers open access to data from across China on the novel coronavirus outbreak and daily statistical modeling.

"To my knowledge, this is the only such open-access data website for novel coronavirus data that exists in the world," said Johannes Knops, head of the Department of Health and Environmental Sciences at Xi'an Jiaotong-Liverpool University.

Dr Zou explained that the scientists set up the website to provide a tool for people working around the globe to control the epidemic.

"We hope that through the integration of information, we can provide more people with data sharing and fight the epidemic together," he said.

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