Stop coronavirus in New York

Data source

The historical data from March 1 to March 17 is collected and two ways are used to fit the historical data and make a roughly prediction for what will happen in the future. Data will tell and hope that we can better understand the situation through the data and actively protect ourselves and slow down the spreading of the virus.

Fit an exponential function

Using historical data (March 1 to March 17) to fit an exponential function: The x-axis means the day of March, for example 2 means March 2.

Fit a logistic function

I hope that the virus cases in New York will be stopped at 10,000 cases. Since right now, we already have 3,000 cases, let’s set this number as 20,000 and get ready.

Using historical data (March 1 to March 17) to fit a logistic function

Exponential VS logistic

Quick summary

From the trend line, we can see if we don’t slow down the spreading by taking active actions, the cases will be increasing exponentially. The exponential function is increasing very fast. If we take active actions, the number can slow down and hopefully it will follow a logistic function.

Let’s pray for the US and hope that the virus spreading can be slowed down soon.

Please wash your hand frequently. Wear gloves and facial mask if you have to go outside. Do not touch your eyes, ears, mouth, and face if you are not 100% sure that your hands are clean.

God bless the USA.

The raw data and the trend function curve can be found here. The logistic function is based on

https://en.wikipedia.org/wiki/Generalised_logistic_function.

Thanks.

Data Scientist/MLE/SWE @takemobi