Difference between revisions of "SIR Modeling"


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:<math>
 
:<math>
\begin{align}
 
g_{\mu \nu} & = [S1] \\[6pt]
 
{R^\mu}_{\alpha \beta \gamma} & = [S2] \\[6pt]
 
G_{\mu \nu} & = [S3]
 
\end{align}
 
</math>
 
 
 
<math>
 
 
\begin{align}
 
\begin{align}
 
S_{t+1} & = S_t - \beta S_t I_t) \\[6pt]
 
S_{t+1} & = S_t - \beta S_t I_t) \\[6pt]

Revision as of 14:00, 2 April 2020

Compartmental Modeling

​Discrete-time SIR modeling of infections/recovery

The model consists of individuals who are either Susceptible ([math]S[/math]), Infected ([math]I[/math]), or Recovered ([math]R[/math]).

The epidemic proceeds via a growth and decline process. This is the core model of infectious disease spread and has been in use in epidemiology for many years.

The dynamics are given by the following 3 equations.

[math] \begin{align} S_{t+1} & = S_t - \beta S_t I_t) \\[6pt] I_{t+1} & = I_t + \beta S_t I_t - \gamma I_t \\[6pt] R_{t+1} & = R_t + \gamma I_t \end{align} [/math]
[math]S_{t+1} = S_t - \beta S_t I_t[/math]


[math]I_{t+1} = I_t + \beta S_t I_t - \gamma I_t[/math]


[math]R_{t+1} = R_t + \gamma I_t[/math]


where;

[math]S[/math] number of susceptible individuals,

[math]I[/math] number of infected individuals,

[math]R[/math] number of recovered individuals,

[math]\beta[/math] the average number of contacts per person per time,

[math]\gamma[/math] the transition rate.