Feb 03 update on timelapse of confirmed Wuhan coronavirus infections in Mainland China and its adaption to fit

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by tipfom

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The animation shows the (total) number of confirmer infections in Mainland Chinese with the nCoV-2019 and how fits to an exponential and sigmoidal growth function.

TLDR: Do NOT panic, this plot is meant to be beautiful and maybe increase interest and no one should use it to predict the future of humanity. Use the research provided instead. Or maybe look at this NY Times article for some more facts about the outbreka

DISCLAIMER: To start off, this fit is very complicated and the animation should not be taken as a serious attempt to model the spread of nCoV-2019. The exponential model renders the virus’s growth infinite, while obviously the world’s population is indeed finite and the growth would slow down as the virus reaches its spread limit. Nevertheless I found the fit very compelling but strongly advise everyone not to make “sciency” conclusions based on my fit, because that’s not his purpose. To cite u/desfirsit

“A turkey that tried to predict the quality of his life the next day on the basis of days so far would always have very strong statistical evidence that the next day would turn out fine – until Christmas. New events don’t factor into these simple models.“

Furthermore the sigmoidal fit assumes the virus spread to be restrained by environmental factors and seems to fit the current data more likely. It is based on a simple SI-model only restraining the virus spread by a total number of susceptible individuals. A reference on it can be found here and also one of the references provided further below.

My tries to illustrate the behaviour of such a prediction model, its adoption to new data points and maybe convey an overall interest on the topic. For real world predictions you may refer to papers linked in two days agos post (e.g. by u/Agent_03), some of which are:

or reports by news outlets and government agencies.

There also is a AskScience thread going on you may direct some questions to.

A heat map of cases may be taken from here:

It is also important to note that my animation depicts the confirmed number of infections. It is therefore questionable, whether you can see that the virus still spreads or whether the simply the amount of confirmed cases increases. Furthermore there have been suggestions that my method of retrieving the error bands is wrong, which I will investigate.

The data is retrieved from Wikipedia, which itself gathers the data from the daily report of Chinas National Health Comission.

Link to the source:


The plots and animations were created using a Python script which can be found on my github


My model used two fits for the data. The first one is an simple exponential y = a * exp(b * x) to fit the data (x starts at 16.01. e.g. x=1 for the 17. Jan). An extrapolation of the fit for using the data points available is shown as a blue line. The bluearea shows the certainty in which the actual fit lays within 66% accuracy. For the last day (28.01.) the fit-parameters where

a = 300+- 50
b = 0.242 +- 0.011

The second one is a fit to the sigmoidal function y = a / (1 + exp(-b * (x – c))) shown in orange, which converged to these parameters in the final frame:

a = (2.59 +- 0.15)e+04
b = 0.383 +- 0.015
c = 15.33 +- 0.33

All frames can be found here: imgur.com/a/EviGbNH

The blue is showing an exponential fit (infinite growth at the same rate). The yellow one is showing an sigmoidial fit. Basically this assumes that the virus is constrained by how much it can growth and is based on an epidemiology model called “SIS” (see my sources if you are interested). Both are very much different. The virus obviously has to stop growing at some point, but it is not yet clear if it will follow the yellow path right now as many models predict way higher infection numbers.

For the future I will try to incorporate more advanced models, but their parameters are hard to fit. But it is going to be necessary as the simple SIS-model seems to be breaking apart like the exponential did.


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