Algorithm 1. Importance of social distancing by flattening the curve of afflicted population over specific days
	Step 1: Initialize necessary parameters as follows to create a simulated town infected with COVID-19 –
	days = 100/*lockdown days*/
	population = 200,000/*population of the town*/
	spread_factor = 0.25/*COVID-19 transmission rate (0 < f ≤ 5) */
	days_to_recover = 10/*maximum recovery days from COVID-19*/
	inital_afflicted_people = 5/*initial infected people of the town with COVID-19*/
	Step 2: Initialize a data frame (“town”) for the simulated town with the following four features –
	id = range(population)/* id € (0- population) */
	infected = false
	recovery_day = none
	recovered = false
	Step 3: Initialize the initial cases (“initialCases”) with inital_afflicted_people variable,
	update corresponding infected feature as true, and
	update recovery_day feature with days_to_recover variable
	Step 4: Initialize the initial active cases (“active_cases”) with initally_afflicted variable and
	initial recovered cases (“recovery”) with 0.
	Step 5:
	for day = 1 to days do
	Step 5.1: Mark the people of town data frame, who have recovered on current day
	- update the feature recovery_day as True and infected feature as False
	if they have crossed days_to_recover else ignore.
	Step 5.2: Calculate the number of people who are afflicted today with spread_factor
	- calculate number of people infected in the town data frame based on
	feature infected = True
	-multiply the count of total infected people with spread_factor to
	calculate total possible cases of infected people on current day
	Step 5.3: Forget people who were already infected in cases of current day
	Step 5.4: Mark the new cases as afflicted, and their recovery day by updating
	active_cases and recovery lists of the town data frame.
	Step 6: Repeat the step 5 for spread_factor = 0.25 to 5.0 and plot every distribution graph of active_cases over days.