Simulate SIR model

UQ_SIR_Simulation.py

# Assign infection and recovery rate parameters
beta, gamma = 0.22, 0.1

# Grid of time points (in days)
t = np.arange(0, 101, 2)

# Run SIR model to predict epidemic evolution
S, I, R = SIR_model(beta, gamma, t, I0=8, N=1000, R0=0)

# Extract output of interest
max_infection = I.max() 
max_time = t[I.argmax()]

# Visualize epidemic evolution
fig, ax = plt.subplots(figsize=(7,5))
ax.plot(t, S, 'b', lw=2, label='Susceptible')
ax.plot(t, I, 'r', lw=2, label='Infected')
ax.plot(t, R, 'g', lw=2, label='Recovered')
ax.set_xlabel('Time /days', fontsize=15)
ax.set_ylabel('Number', fontsize=15)
ax.set_ylim(-20,1020)
ax.legend(loc="center right",prop={'size': 12});