Agent based infection model

Label

Title

Description

The
<a href="http://op12no2.me/toys/pox?scenario=exp">exp</a>
scenario shows how a classic iterative
model can be replicated in an agent based framework.
Inherrent in such models is that latent (E) and infectious (I)
periods are exponentially distributed; i.e. while the
periods operate around a mean value, there will be some
very large and unrealistic values.

This scenario is similar, but the exponential state
lengths for E and I have been replaced with
<a href="http://rsif.royalsocietypublishing.org/content/10/84/20130098.short">more representative</a>
Gaussian ones by simply calling a different random number
generator. Specifically this:-

<pre>
states.e.Len = {func: randE, args: {mean: 10}};
states.i.Len = {func: randE, args: {mean: 30}};
</pre>

Has become:-

<pre>
states.e.Len = {func: randG, args: {mean: 10, sd: 3}};
states.i.Len = {func: randG, args: {mean: 30, sd: 9}};
</pre>

This is one of the advantages of agent
based models; it is very easy to explore different
distributions - much harder in iterative ones.

The comparative state history graphs are interesting.

Click Start to run the model...</b

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