<jupyter_start><jupyter_text>So I tried to mimic the situation for elemetary schools, giving the average number of kids are 473, recover rate given by a poisson dist of lambda = 1/(13.61+13.24), and trying to varying b and see the infected people at peak day for example (this will be my plot 2). And the other plot is SIR model given below.<jupyter_code>### Plot 1

## CONTINUOUS 

NumPop = 473 # Elemetary https://nces.ed.gov/programs/digest/d07/tables/dt07_095.asp
b = 1          # number of closed contact people 
k = 0.074    # 1/(13.61+13.24) for 0-19 people "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7229949/"
time=30      
ii = 0.02

sol1=SIR_continuous(b,k,time,ii)
plt.plot(sol1.t, sol1.y[0]*NumPop, c='b',label='s')    # generate the plot
plt.plot(sol1.t, sol1.y[1]*NumPop, c='g',label='r')
plt.plot(sol1.t, sol1.y[2]*NumPop, c='r',label='i')


plt.title("Continuous SIR Model(b=1, k=0.074 )")
plt.ylabel("Number of People")
plt.xlabel("Time Period")
plt.legend(['s','r','i'])
plt.savefig('C_SIR_1')

#print(sol1.y)
## I am trying to plot as varying b, how many people get infected at peak day(Maybe day 5) to illustrate the idea. 
# But my plot hasn't been successful :(

k = 0.074
time=5
ii=0.01
NumPop = 473
r=0
q=1
b = np.arange(1, 11, dtype=np.int64)
sol1=[]

for i in range(10):
    p=SIR_continuous(b[i],k,time,ii,r,q).y[2][-1]
    sol1.append(p)

    
plt.plot(b, np.array(sol1)*NumPop, c='r',label='i')  # number of infected people 
plt.title("Continuous SIR Model(b=1, k=0.074 )")
plt.ylabel("Number of People Infected")
plt.xlabel("Varying b")
plt.show()<jupyter_output><empty_output>
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