How does COVID-19 continue to spread? - A simulation 2.0 (Results)

This post shows some of the results we can find by using the simulation. As in the first version I made some tests, now I focused the new tests on the travelling and vaccination processes. These two processes were added in the last simulation version and represent some critical behaviour and processes in the virus spread.

Photo by Sharon McCutcheon on Unsplash

Vaccination process impact

Using the standard static configuration values we can find the following results:

The vaccination process does not have a considerable impact if we close our borders. By not receiving new agents with the infection, the simulation reaches the number of 0 infected agents on the 38th day using a vaccination percentage of 0.1 If we increase the vaccination percentage to 0.9 the 0 infected agents threshold is reached on the 39th day. Thus, we can infer that if we control the flow of agents in a city/simulation, the vaccination process does not have a considerable impact as it takes some time until the people become immune to the virus.

Travelling impact

Using the standard static configuration values we can find the following results:

Travelling agents have a huge impact on the simulation results. In the two simulation runs you can see relatively equal results in terms of days until reaching the 0 agents infected. However, when more agents travel in and out of the simulation the number of total infected agents is much higher. So, although it takes practically the same until the virus loses the battle, more agents will suffer from it when we have a high flow of agents travelling around.

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