The graphic below allows you to model the spread of COVID-19 using the SIR model. This is a very simple model but has proven surprisingly effective; see my series of blog posts on the topic. This graphic is meant for educational purposes only and of course I make no guarantees about its ability to forecast real-world infections.

To start the model, click on the computer code below and hit shift+Enter.

The initial conditions for the model are taken from the JHU CSSE data set that is updated daily. For a description of the parameters $\beta$ and $\gamma$, see my blog posts, starting here. The other parameters you can adjust are:

• % of cases confirmed: The fraction of actual infected individuals that get tested and reported.
• % of cases critical: The fraction of actual infected individuals that require critical medical attention.
• % of cases fatal: The fraction of actual infected individuals that die from the infection or secondary infections.
• Use mitigation: If checked, the model incorporates attempts to reduce the spread of the disease. This represents the effect of quarantines, closures, etc.
• Mitigation factor: If mitigation is enabled, then during the mitigation period the contact rate $\beta$ is multiplied by this factor. Corresponds to $q(t)$ in this blog post. For instance, a mitigation factor of 0.5 would mean that human contact has been reduced by half. Of course, this is a very simplified model for mitigation. In reality, the measures implemented and their effectiveness will vary significantly over time.
• Mitigation interval: The mitigation factor is applied over this time period, measured in days from the start of the model (i.e., today). For instance, "0-180" means that mitigation is applied starting from now and for the next 180 days.

You may find it interesting to compare your predictions with those of a more detailed model used by researchers at Imperial College. Roughly, the estimates there predict the infection peak to occur in early June without mitigation, moving up to 1 month later if substantial mitigation is employed.

In [ ]:
%matplotlib inline
from SIR import *

display(SIR_gui,SIR_output)