Simulation of epidemic processes looking at Susceptibles, Infected and Recovered trends, adding hospitalization measures and other phisical barriers for slowing down the infections
The goal of Lab L7 is to define and simulate simple strategies to control an epidemic (SIR) process through non-pharmaceutical interventions (I.e. by introducing mobility restrictions).
Consider a homogeneous population of 50M individuals. Fix R(0)=4 and gamma= 1/14 days (recovering rate). Assume that 10% (6%) of the infected individuals needs to be Hospitalized (H) (undergo Intensive Treatments (IT).)
Fix the fatality rate of the epidemic to 3%. H/IT places are limited (10k/50k). Design some non pharmaceutical intervention strategy that avoids H/IT overloads, and limits the number of death in 1 year to 100K. To design your strategy you can use a mean-field SIR model.
Then, once you have defined your strategy simulate both the stochastic SIR and its mean field. Are there significant differences, why? What happens if you scale down your population N to 10k (of course you have to scale down also other parameters, such as H and IT places)?
🔗 PDF Report attached
