The novel coronavirus or COVID-19 is a virus causing acute respiratory disorders. It belongs to the SARS class of viruses. Beginning in a seafood market at Wuhan, Hubei, China, it spread globally and has attained pandemic status. It currently has over a million reported cases.
Epidemiologists study the spread of the disease by trying to model it mathematically. Basic models include the SI model, the SIS model and the SIR model. The population consists of people susceptible to the disease (S), currently infected by it (I) or those who are no longer affected by it, having attained immunity (recovered, R).
The datasets for India, South Korea, USA and Italy were sourced from Kaggle. They were formatted into a pandas dataframe after extracting the data for cumulative infected, recovered and dead with respect to days from first infection.
The covid.py file seeks to make realistic graphs with the data sourced from Kaggle. It has some helper functions for its purpose.
The compareproperties() function works to make a line plot comparison of a property between any two countries using seaborn, as is shown below.
The countrysituation() function makes a plot of the infected, dead and recovered for any one particular country.
The gifize() function makes the plot into a gif.
The forestfire.py file has a simulation of what could happen in a lockdown. It makes a simulation of the spread of an infection and how lockdown can affect it. Considering the population to be units in a matrix, the infected can be in anywhere between their incubation period (yellow) or terminating stage (dark red). The infected are capable of infecting their adjacent neighbours. The cured patients revert to a green color at the end.
More info can be found here.
I would also like to thank Aalaap Nair for helping me with this project.