Simulating an Outbreak
Try it out below!
The spread of the disease is something that has been studied for a long time, and there are a variety of ways to predict what will happen when a population is exposed to a new disease. One way of modeling these scenarios is the susceptible, infected, recovered (SIR) model.
The SIR model defines a population into 3 categories.
- Susceptible is the number of people who are at risk of the getting infected. This assumes that someone is not vaccinated and that they cannot get a disease twice.
- Infected means that someone has the disease and can infect other people.
- Recovered is the number of people who no longer can infect other people.
In the model I have created, I have also added a category called ‘Dead’. This splits the recovered category into ‘Recovered’ and ‘Dead’ based on the disease’s death rate.
Variables
- Infection chance is how likely someone is to get infected by another person.
- Recovery time is how long it takes for a disease to leave the incubation and symptom period.
- Death rate is how many of the infected will die from the symptoms.
- r0 (R-naught) tells you how many people will get infected by one person per day.
Try it out!
Go ahead and change the variables around and see what you get. Try changing the dropdown to see pre-defined outbreaks.
((days))
((recoveryTime)) Days
((infectionRate*100))%
((deathrate*100))%
((infected_start))
((s))
Results
- ((ts)) people out of ((s)) did not get sick.
- ((ti)) people are still infected.
- ((tr)) out of ((s)) people recovered.
- ((td)) people died.
- The r0 value is ((infectionRate * recoveryTime))
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