Basic Exercises

  1. Generalise the simple SIR model to an SEIR model, where the susceptibles move into an “exposed” compartment for some time before they start being infectious. In this compartment, they cannot infect others, nor can they get infected.

  2. Generalise your SIR model to an SIRD model, where some fraction of the infected individuals (say, 1%) transit to the “Dead” compartment instead of recovering.

  3. Generalise the simple SIR model to an SAIR model: infected individuals can either be Asymptomatic (\(A\)) or Symptomatic (\(I\)), and they can both recover from this state. You will need to define new parameters that determine the relative fraction of asymptomatics, for example.

  4. For the simple SIR model (or any other model that you prefer), compute out the “residence time” distribution for the susceptible compartment. In other words, find all the times at which individual agents transition from \(S\to I\), and plot a histogram.

Intermediate Exercises

  1. How does our simple SIR model scale up with the population \(N\)? You will need to study not only the epidemic curves, but also their spread. Run the SIR model for around 10 or 20 times for a given population size (say, 10,000 individuals), and record the output. Repeat the process for a population of 20,000, and so on, for as high as you can go. Next, find some way to quantify the spread of the epidemic curves (for example, the standard deviation in the number of recovered at the end of the epidemic). Plot a graph that shows how this “spread” varies with the initial population \(N\).

  2. Generalise the simple SIR model to include reinfections (the SIRS model): the recovered people do not stay recovered but – at some rate \(\zeta\) – transit back into the susceptible compartment. Try to choose the parameters well such that an endemic equilibrium is reached, meaning that the disease never truly dies out, some small fraction of the population is always infected.

  3. Introduce a lockdown to a simple SIR model. Say that only some fraction of the population (say, only the essential workers) will “violate” the lockdown and continue to go to work. Observe what happens to the total number of recovered at the end of the epidemic as you increase the duration of the lockdown.

Advanced Exercises

  1. Consider a simple model with multiple types of infected individuals (say, the SAIR model I described above). Introduce the possibility that one infected group is much more infectious than another infected group. In other words, say that Symptomatic individuals (“I”) are 2 times more infectious than Asymptomatic individuals.

  2. For the simple SIR model, in one of the earlier exercises we showed that the time that an individual spent in the infected compartment was an exponentially distributed Random Number with mean \(1/\lambda_I\). Therefore, there should be another way to decide how long infected individuals stay in a compartment, instead of generating a probability of transitioning at every step \(\Delta t\). When an individual transits from \(S\to I\), choose a fixed amount of time randomly drawn from an exponential distribution of mean \(\tau_I = 1/\lambda_I\). After this time, the individuals will exit from \(I\to R\).

  3. Compute the effective reproductive ratio (\(R\)) as a function of time. This number is defined as the average number of individuals a single infected individual is responsible for having infected. In order to do this, whenever an \(S\to R\) transition occurs, choose one of the infected individuals who could be responsible for this an increment some parameter representing the the “number of people they have infected”. At every time step, average this number out over the entire population, and finally plot a graph of this as a function of time.