Python code for impacts of class size on SEIR epidemic model
This code implements stochastic simulations, using a Gillespie algorithm, of a SEIR (Susceptible-Exposed-Infected-Recovered) model where individuals are divided into households and classes. The code runs 100 replicates for 10 different average class sizes, from 5 to 50, and presents the resukts as boxplots for the peak infected, total infected and calculated R_0.