Predicting 1,000,000 of COVID-19 done with R before March 18 – call for help

March 30, 2020
By

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Due to the unprecedented technological progress that has been made in years past, the current climate allows us to monitor this pandemic better than any other pandemic in the past. We will argue, however, that R was instrumental in predicting when the 1,000,000th case of COVID-19 will occur, as demonstrated here in our collaboration spread out on three continents:

https://www.sciencedirect.com/science/article/pii/S2590113320300079

Since India is currently in lockdown and the correction process is in India, it has not been concluded as of writing.

The first draft of our paper was prepared on March 18 and can be accessed here: http://www.cs.laurentian.ca/wkoczkodaj/p/?C=M;O=D
Open this link and click twice on “last modified” to see the data (the computing was done a few days earlier). 
Our heuristic developed for the prediction could not be implemented so quickly had it not been for our use of R. The function ‘nls‘ is crucial for modelling only the front incline part of the Gaussian function (also known as Gaussian). Should this pandemic not stop, or at the very least slow down, one billion cases could occur by the end of May 2020.

The entire world waits for the inflection point (https://en.wikipedia.org/wiki/Inflection_point) and if you help us, we may be able to reach this point sooner.

A few crucial R commands are: 
modE <- nls(dfcov$all ~ a * exp(bdfcov$report), data = dfcov,
start = list(a = 100, b = 0.1))
a <- summary(modE)$parameters[1]
b <- summary(modE)$parameters[2]
summary(modE)
x <- 1:m + dfcov$report[length(dfcov$report)]
modEPr <- a * exp(b
x)

Waldemar W. Koczkodaj (email: wkoczkodaj [AT] cs.laurentian.ca)
(for the entire Collaboration)

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