# sir_simulation

sir_simulation, a Python code which simulates the spread of a disease through a hospital room of M by N beds, using the Susceptible/Infected/Recovered (SIR) model.

We consider the evolution of a disease in a hospital in which patients are arranged on an array of beds.

We assume that the beds form an array of M rows and N columns, so that there are a total of M * N patients.

We assume that the patients can be classified as Susceptible, Infected or Recovering, with the properties that:

• Susceptible: A patient who has never been infected with the disease. A susceptible patient can get the disease.
• Infected: A patient who has never gotten the disease. A patient stays infected for K days. On the K+1 of the disease, the patient "recovers".
• Recovered: A patient who has had the disease, that is, has caught the disease and been sick for a full K days. A recovered patient never gets sick again.

We set up an M by N array A to represent the patients. A(I,J) contains information on the patient in row I, column J. A(I,J) will be

• 0, if the patient is susceptible.
• a value between 1 and K, if the patient is infected. The value is the number of days the patient has been infected.
• -1, if the patient is recovered.

The rules for transmission of the disease essentially update the patient array once a day. If patient A(I,J) was:

• 0, then check the four neighbors A(I-1,J), A(I+1,J), A(I,J-1) and A(I,J+1). For each neighbor that is infected, pick a random number, and if that random number is less than TAU, then patient A(I,J) becomes infected, that is, we set A(I,J) to 1.
• a value between 1 and K, then the value is increased by 1. But if the value was already K, it is now reset to -1, because the patient has recovered.
• -1, nothing happens.

Quantities of interest include an animation of the day to day status of patients in the hospital (the "geometry") and the values of S, I, and R, that is, the total number of patients in each category, as it evolves over time.

Since this problem contains a probabilistic element in the transmission of disease, the outcome of any single run has limited meaning. It is much more valuable to run many simulations, and thus to get both average or "expected" values, as well as a feeling for the variance of the data from these averages.

### Languages:

sir_simulation is available in a MATLAB version and a Python version.

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### Reference:

1. Dianne OLeary,
Models of Infection: Person to Person,
Computing in Science and Engineering,
Volume 6, Number 1, January/February 2004.
2. Dianne OLeary,
Scientific Computing with Case Studies,
SIAM, 2008,
ISBN13: 978-0-898716-66-5,
LC: QA401.O44.

### Source Code:

Last revised on 07 July 2022.