Scenario: One chokepoint for an hour

We have already seen that if you pack people into a few locations, it tends to facilitate viral spread under our model.

But what if you are just packing people for a short amount of time into one location? Surely going for a soccer match or for a church sermon for an hour can’t be that bad?

That is, of course, wrong.

And you can see it clearly visually below in our tweaked model.

Let’s assume this parameters:

Simulation Parameters

The day people go to church sermon (always from 11am to 12pm): Infection chance: Simulation Speed:

Simulation Results

Insights

As expected, gathering lots of people in one or even a few locations is bad. It does not need to be for long, and just one hour in our model is enough.

Immediately what jumps out at you is that after the church sermon, the number of infected almost unfailingly spikes.

Especially once the town has a base of infected people, this multiplies exponentially, and then it quickly spreads to the rest of the town.

This is why mass gatherings (e.g. at a cinema, etc.) are not feasible in the short-term until the world can get a grip on COVID19 (antibody testing, vaccine, etc.)

Probability

I am going to take a short detour into probability on how the risk is compounded, and if you're not interested in the math, please feel free to skip!

Repeated simulations

And just like in the baseline model, we're going to run 10,000 simulation runs, comparing a town without a church sermon and a town with one.

No Church Sermon

With Church Sermon

Parameters: 150 people town, 50 homes, 30 workplaces, 5 days simulation time, 2% infection rate.

Everything is the same except one town has a church sermon on day 3 at 11am where everyone attends. The other town everyone remains at work.

Both scenario simulations run 10,000 times.

As can be clearly seen, in the town with the church model, the infection rate is extremely likely to be high in this scenario.

Again, if you would like to see this and play with it yourself, check out the code on GitHub.