Epidemiology: The SIR model: Difference between revisions

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Simulation of differential equations with turtle graphics.
Simulation of differential equations with turtle graphics using [http://jsxgraph.org JSXGraph].


===SIR model without vital dynamics===
===SIR model without vital dynamics===
A single epidemic outbreak is usually far more rapid than the vital dynamics of a population, thus, if the aim is to study the immediate consequences of a single epidemic, one may neglect the birth-death processes. In this case the SIR system described above can be expressed by the following set of differential equations:
The SIR model measures the number of susceptible, infected, and recovered individuals in a host population.
Given a fixed population, let <math>S(t)</math> be the fraction that is susceptible to an infectious, but not deadly, disease at time t; let <math>I(t)</math> be the fraction that is infected at time <math>t</math>;
and let <math>R(t)</math> be the fraction that has recovered. Let <math>\beta</math> be the rate at which an infected person infects a susceptible person. Let <math>\gamma</math> be the rate at which infected people recover from the disease.


    \frac{dS}{dt} = - \beta I S   
A single epidemic outbreak is usually far more rapid than the vital dynamics of a population, thus, if the aim is to study the immediate consequences of a single epidemic, one may neglect birth-death processes. In this case the SIR system can be expressed by the following set of differential equations:
   
    \frac{dI}{dt} = \beta I S - \nu I


    \frac{dR}{dt} = \nu I
:<math> \frac{dS}{dt} = - \beta I S </math>
 
:<math> \frac{dR}{dt} = \gamma I </math>
 
:<math> \frac{dI}{dt} = -(\frac{dS}{dt}+\frac{dR}{dt}) </math>
 
====Example Hong Kong flu====
* initially 7.9 million people,
* 10 infected,
* 0 recovered.
* estimated average period of infection: 3 days, so <math>\gamma = 1/3</math>
* infection rate: one new person every other day, so <math>\beta = 1/2</math>
 
Thus S(0) = 1, I(0) = 1.27E-6, R(0) = 0, see [http://www.cs.princeton.edu/introcs/94diffeq/].
 
The lines in the JSXGraph-simulation below have the following meaning:
* <span style="color:Blue">Blue: Rate of susceptible population</span>
* <span style="color:red">Red: Rate of infected population</span>
* <span style="color:green">Green: Rate of recovered population (which means: immune, isolated or dead)
<html>
<form><input type="button" value="clear and run a simulation of 100 days" onClick="clearturtle();run()">
<input type="button" value="stop" onClick="stop()">
<input type="button" value="continue" onClick="goOn()"></form>
</html>
 
<jsxgraph box="box" width="600" height="450">
var brd = JXG.JSXGraph.initBoard('box', {axis: true, boundingbox: [-5, 1.2, 100, -1.2]});
 
var S = brd.create('turtle',[],{strokeColor:'blue',strokeWidth:3});
var I = brd.create('turtle',[],{strokeColor:'red',strokeWidth:3});
var R = brd.create('turtle',[],{strokeColor:'green',strokeWidth:3});
           
var s = brd.create('slider', [[0,-0.3], [30,-0.3],[0,1.27E-6,1]], {name:'s'});
brd.create('text', [40,-0.3, "initially infected population rate (on load: I(0)=1.27E-6)"]);
var beta = brd.create('slider', [[0,-0.4], [30,-0.4],[0,0.5,1]], {name:'&beta;'});
brd.create('text', [40,-0.4, "&beta;: infection rate"]);
var gamma = brd.create('slider', [[0,-0.5], [30,-0.5],[0,0.3,1]], {name:'&gamma;'});
brd.create('text', [40,-0.5, "&gamma;: recovery rate = 1/(days of infection)"]);
 
var t = 0; // global
 
brd.create('text', [40,-0.2,
        function() {return "Day "+t+": infected="+(7900000*I.Y()).toFixed(1)+" recovered="+(7900000*R.Y()).toFixed(1);}]);
           
S.hideTurtle();
I.hideTurtle();
R.hideTurtle();
 
function clearturtle() {
  S.cs();
  I.cs();
  R.cs();
 
  S.hideTurtle();
  I.hideTurtle();
  R.hideTurtle();
}
           
function run() {
  S.setPos(0,1.0-s.Value());
  R.setPos(0,0);
  I.setPos(0,s.Value());
               
  delta = 1; // global
  t = 0;  // global
  loop();
}
           
function turtleMove(turtle,dx,dy) {
  turtle.moveTo([dx+turtle.X(),dy+turtle.Y()]);
}
           
function loop() {
  var dS = -beta.Value()*S.Y()*I.Y();
  var dR = gamma.Value()*I.Y();
  var dI = -(dS+dR);
  turtleMove(S,delta,dS);
  turtleMove(R,delta,dR);
  turtleMove(I,delta,dI);
               
  t += delta;
  if (t<100.0) {
    active = setTimeout(loop,10);
  }
}
 
function stop() {
  if (active) clearTimeout(active);
  active = null;
}
function goOn() {
  if (t>0) {
    if (active==null) {
      active = setTimeout(loop,10);
    }
  } else {
    run();
  }
 
}
</jsxgraph>
 
===The underlying JavaScript code===
<source lang="javascript">
var brd = JXG.JSXGraph.initBoard('box', {axis: true, boundingbox: [-5, 1.2, 100, -1.2]});
 
var S = brd.create('turtle',[],{strokeColor:'blue',strokeWidth:3});
var I = brd.create('turtle',[],{strokeColor:'red',strokeWidth:3});
var R = brd.create('turtle',[],{strokeColor:'green',strokeWidth:3});
           
var s = brd.create('slider', [[0,-0.3], [30,-0.3],[0,1.27E-6,1]], {name:'s'});
brd.create('text', [40,-0.3, "initially infected population rate (on load: I(0)=1.27E-6)"]);
var beta = brd.create('slider', [[0,-0.4], [30,-0.4],[0,0.5,1]], {name:'&beta;'});
brd.create('text', [40,-0.4, "&beta;: infection rate"]);
var gamma = brd.create('slider', [[0,-0.5], [30,-0.5],[0,0.3,1]], {name:'&gamma;'});
brd.create('text', [40,-0.5, "&gamma;: recovery rate = 1/(days of infection)"]);
 
var t = 0; // global
 
brd.create('text', [40,-0.2,
        function() {return "Day "+t+": infected="+(7900000*I.Y()).toFixed(1)+" recovered="+(7900000*R.Y()).toFixed(1);}]);
           
S.hideTurtle();
I.hideTurtle();
R.hideTurtle();
 
function clearturtle() {
  S.cs();
  I.cs();
  R.cs();
 
  S.hideTurtle();
  I.hideTurtle();
  R.hideTurtle();
}
           
function run() {
  S.setPos(0,1.0-s.Value());
  R.setPos(0,0);
  I.setPos(0,s.Value());
               
  delta = 1; // global
  t = 0;  // global
  loop();
}
           
function turtleMove(turtle,dx,dy) {
  turtle.moveTo([dx+turtle.X(),dy+turtle.Y()]);
}
           
function loop() {
  var dS = -beta.Value()*S.Y()*I.Y();
  var dR = gamma.Value()*I.Y();
  var dI = -(dS+dR);
  turtleMove(S,delta,dS);
  turtleMove(R,delta,dR);
  turtleMove(I,delta,dI);
               
  t += delta;
  if (t<100.0) {
    active = setTimeout(loop,10);
  }
}
 
function stop() {
  if (active) clearTimeout(active);
  active = null;
}
function goOn() {
  if (t>0) {
    if (active==null) {
      active = setTimeout(loop,10);
    }
  } else {
    run();
  }
 
}
</source>
 
===See also===
* [[Epidemiology: The SEIR model]]
* [[Population growth models]]
* [[Lotka-Volterra equations]]
 
===References===
* [http://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology http://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology]
* [http://mathworld.wolfram.com/SIRModel.html http://mathworld.wolfram.com/SIRModel.html]
* [http://www.cs.princeton.edu/introcs/94diffeq http://www.cs.princeton.edu/introcs/94diffeq]
 
[[Category:Examples]]
[[Category:Turtle Graphics]]
[[Category:Calculus]]

Latest revision as of 14:59, 20 February 2013

Simulation of differential equations with turtle graphics using JSXGraph.

SIR model without vital dynamics

The SIR model measures the number of susceptible, infected, and recovered individuals in a host population. Given a fixed population, let [math]\displaystyle{ S(t) }[/math] be the fraction that is susceptible to an infectious, but not deadly, disease at time t; let [math]\displaystyle{ I(t) }[/math] be the fraction that is infected at time [math]\displaystyle{ t }[/math]; and let [math]\displaystyle{ R(t) }[/math] be the fraction that has recovered. Let [math]\displaystyle{ \beta }[/math] be the rate at which an infected person infects a susceptible person. Let [math]\displaystyle{ \gamma }[/math] be the rate at which infected people recover from the disease.

A single epidemic outbreak is usually far more rapid than the vital dynamics of a population, thus, if the aim is to study the immediate consequences of a single epidemic, one may neglect birth-death processes. In this case the SIR system can be expressed by the following set of differential equations:

[math]\displaystyle{ \frac{dS}{dt} = - \beta I S }[/math]
[math]\displaystyle{ \frac{dR}{dt} = \gamma I }[/math]
[math]\displaystyle{ \frac{dI}{dt} = -(\frac{dS}{dt}+\frac{dR}{dt}) }[/math]

Example Hong Kong flu

  • initially 7.9 million people,
  • 10 infected,
  • 0 recovered.
  • estimated average period of infection: 3 days, so [math]\displaystyle{ \gamma = 1/3 }[/math]
  • infection rate: one new person every other day, so [math]\displaystyle{ \beta = 1/2 }[/math]

Thus S(0) = 1, I(0) = 1.27E-6, R(0) = 0, see [1].

The lines in the JSXGraph-simulation below have the following meaning:

* Blue: Rate of susceptible population
* Red: Rate of infected population
* Green: Rate of recovered population (which means: immune, isolated or dead)

The underlying JavaScript code

var brd = JXG.JSXGraph.initBoard('box', {axis: true, boundingbox: [-5, 1.2, 100, -1.2]});

var S = brd.create('turtle',[],{strokeColor:'blue',strokeWidth:3});
var I = brd.create('turtle',[],{strokeColor:'red',strokeWidth:3});
var R = brd.create('turtle',[],{strokeColor:'green',strokeWidth:3});
            
var s = brd.create('slider', [[0,-0.3], [30,-0.3],[0,1.27E-6,1]], {name:'s'});
brd.create('text', [40,-0.3, "initially infected population rate (on load: I(0)=1.27E-6)"]);
var beta = brd.create('slider', [[0,-0.4], [30,-0.4],[0,0.5,1]], {name:'&beta;'});
brd.create('text', [40,-0.4, "&beta;: infection rate"]);
var gamma = brd.create('slider', [[0,-0.5], [30,-0.5],[0,0.3,1]], {name:'&gamma;'});
brd.create('text', [40,-0.5, "&gamma;: recovery rate = 1/(days of infection)"]);

var t = 0; // global

brd.create('text', [40,-0.2, 
        function() {return "Day "+t+": infected="+(7900000*I.Y()).toFixed(1)+" recovered="+(7900000*R.Y()).toFixed(1);}]);
            
S.hideTurtle();
I.hideTurtle();
R.hideTurtle();

function clearturtle() {
  S.cs();
  I.cs();
  R.cs();

  S.hideTurtle();
  I.hideTurtle();
  R.hideTurtle();
}
            
function run() {
  S.setPos(0,1.0-s.Value());
  R.setPos(0,0);
  I.setPos(0,s.Value());
                
  delta = 1; // global
  t = 0;  // global
  loop();
}
             
function turtleMove(turtle,dx,dy) {
  turtle.moveTo([dx+turtle.X(),dy+turtle.Y()]);
}
             
function loop() {
  var dS = -beta.Value()*S.Y()*I.Y();
  var dR = gamma.Value()*I.Y();
  var dI = -(dS+dR);
  turtleMove(S,delta,dS);
  turtleMove(R,delta,dR);
  turtleMove(I,delta,dI);
                
  t += delta;
  if (t<100.0) {
    active = setTimeout(loop,10);
  }
}

function stop() {
  if (active) clearTimeout(active);
  active = null;
}
function goOn() {
   if (t>0) {
     if (active==null) {
       active = setTimeout(loop,10);
     }
   } else {
     run();
   }

}

See also

References