This is a program that simulates a pandemic based on parameters and hyper-parameters. It generates a data frame of virtual people. And within the program, these people can move from one community to another. They tend to visit landmarks more often than in other places. And a foreigner could only directly go to the landmark of a different community. When the infection starts spreading the government set up lockdown.But the lock-downs aren't absolute. Many tend to disobey.

The figure represents the population data frame. Each person has an x,y coordinate for their location. A value ranging from 0 to 1. Represent their acceptance of the lockdown. 1 represent going for a perfect lockdown. 0 represents not caring about a lockdown at all. The region represents which region the person belongs to.

The figure represents the infected data frame. Each person has an x,y coordinate for their location. Time represents the number of days since the person has been infected. The region represents which region the person belongs to.

spread_limit --> Maximum distance between infected and an uninfected person needed for infection to spread
recovery_prob --> Probability that a person will recover from the disease(at least 3 days after infection)
intial_count --> The number of people infected at the very beginning
infection_rate --> Probability of getting infected if within spread_limit
population = --> The number of people at the beginning of the simulation

landmark --> An array storing the x,y coordinates of the landmarks of each region
landmark_prob --> The probability of a random person to visit a landmark on a particular day
landmark_prob_dec_rate --> Rate of decrease in the tendency to visit a landmark on a particular day

lock_ratio --> Initial ratio of people who go on lockdown when government notice is put up
lock_decrease_rate --> ratio of people who go will decrease their lockdown value(Quar) each day
lock_increase_rate --> ratio of people who go will increase their lockdown value(Quar) each day
lock_infected_count --> minimum number of infected people after which lockdown starts

no_of_region --> No. of regions in which the people are divided

Creating a simpler model initially, in this model there are no landmark(every location in equally probable). People don't go into lockdown. The space is not divided into regions. Values of parameters are present in the image itself. x axis represnts days,and y axis represents no of people.

This creates several models(simple model as in the General plot) and with different Infection_Rate value. The number of days within which the number of infected people becomes zero are found for each value. And the data is plotted above This creates several models(simple model as in the General plot) and with different Infection_Rate value. The number of people infected and the number of recovered in found for each infection_rate value. And the data is plotted above