sir_simulation


sir_simulation, an Octave 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:

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

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

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.

Usage:

sir = sir_simulation ( m, n, a, k, tau, t_max )
where

Licensing:

The computer code and data files described and made available on this web page are distributed under the MIT license

Languages:

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

Related Data and codes:

sir_simulation_test

octave_simulation, an Octave code which uses simulation to study card games, contests, and other processes which have a random element. Usually, the purpose is to try to predict the average behavior of the system over many trials.

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 16 November 2022.