As we do not have an analytic solution to this system, we can use numerical methods to find the number of people in each class over time. While there are many methods that could be employed to solve this system, such as the popular one-step Runge-Kutta methods, it is preferable to use methods that can adapt to the stiffness of the ODE. Our system was solved using Scipy's \citep*{others2001--} odeint, a method that uses the LSODA solver to both employ adaptive time-stepping as well as switch to using implicit methods if the ODE is stiff.