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:
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.
The computer code and data files described and made available on this web page are distributed under the MIT license
sir_simulation is available in a MATLAB version and a Python version.
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