Month: November 2009

In this post, we will continue building on the basic models we discussed in the first and second tutorials. If you haven’t had a chance to take a look at them yet, definitely go back and at least skim them, since the ideas and code there form the backbone of what we’ll be doing here.

In this tutorial, we will build a model that can simulate outbreaks of disease on a small-world network (although the code can support arbitrary networks).  This tutorial represents a shift away from both:

a) the mass-action mixing of the first two and and

b) the assumption of social homogeneity across individuals that allowed us to take some shortcuts to simplify model code and speed execution. Put another way, we’re moving more in the direction of individual-based modeling.

When we’re done, your model should be producing plots that look like this:

Red nodes are individuals who have been infected before the end of the run, blue nodes are never-infected individuals and green ones are the index cases who are infectious at the beginning of the run.

And your model will be putting out interesting and unpredictable results such as these:

In order to do this one, though, you’re going to need to download and install have igraph for Python on your system.

Individual-Based Networks

It is important to make the subtle distinction between individual and agent based models very clear here. Although the terms  are often used interchangeably, referring to our nodes, who have no agency, per se, but are instead fairly static receivers and diffusers of infection, as agents, seems like overreaching. Were they to exhibit some kind of adaptive behavior, i.e., avoiding infectious agents or removing themselves from the population during the infective period, they then become more agent-like.

This is not to under- or over-emphasize the importance or utility of either approach, but just to keep the distinction in mind to avoid the “when all you have is a hammer, everything looks like a nail” problem.

In short, adaptive agents are great, but they’re overkill if you don’t need them for your specific problem.

Small World Networks

The guiding idea behind small-world networks is that they capture some of the structure seen in more realistic contact networks: most contacts are regular in the sense that they are fairly predicable, but there are some contacts that span tightly clustered social groups and bring them together.

In the basic small-world model, an individual is connected to some (small, typically <=8) number of his or her immediate neighbors. Some fraction of these network connections are then randomly re-wired, so that some individuals who were previously distant in network terms – i.e., connected by a large number of jumps – are now adjacent to each other. This also has the effect of shortening the distance between their neighbors and individuals on the other side of the graph. Another way of putting this is that we have shortened the average path length and increased the average reachability of all nodes.

These random connections are sometimes referred to as “weak ties”, as there are fewer of these ties that bridge clusters than there are within clusters. When these networks are considered from a sociological perspective, we often expect to find that the relationship represented by a weak tie is one in which the actors on either end have less in common with each other than they do with their ‘closer’ network neighbors.

Random networks also have the property of having short average path lengths, but they lack the clustering that gives the small-world model that pleasant smell of quasi-realism that makes them an interesting but largely tractable, testing ground for theories about the impact of social structure on dynamic processes.

Installation and Implementation Issues

If you have all the pre-requisites installed on your system, you should be able to just copy and paste this code into a new file and run it with your friendly, local Python interpreter. When you run the model, you should first see a plot of the network, and when you close this, you should see a plot of the number of infections as a function of time shortly thereafter.

Aside from the addition of the network, the major conceptual difference is that the model operates on discrete individuals instead of a homogeneous population of agents. In this case, the only heterogeneity is in the number and identity of each individual’s contacts, but there’s no reason we can’t (and many do) incorporate more heterogeneity (biological, etc.) into a very similar model framework.

With Python, this change in orientation to homogeneous nodes to discrete individuals seems almost trivial, but in other languages it can be somewhat painful. For instance, in C/++, a similar implementation would involve defining a struct with fields for recovery time and individual ID, and defining a custom comparison operator for these structs. Although this is admittedly not a super-high bar to pass, it adds enough complexity that it can scare off novices and frustrate more experienced modelers.

Perhaps more importantly, it often has the effect of convincing programmers that a more heavily object-oriented approach is the way to go, so that each individual is a discrete object. When our individuals are as inert as they are in this model, this ends up being a waste of resources and makes for significantly more cluttered code. The end result can often be a model written in a language that is ostensibly faster than Python, such as C++ or Java, that runs slower than a saner (and more readable) Python implementation.

For those of you who are playing along at home, here are some things to think about and try with this model:

1. Change the kind of network topology the model uses (you can find all of the different networks available in igraph here).
2. Incorporate another level of agent heterogeneity: Allow agents to have differing levels of infectivity (Easier); Give agents different recovery time distributions (Harder, but not super difficult).
3. Make two network models – you can think of them as separate towns – and allow them to weakly influence each other’s outbreaks. (Try to use the object-oriented framework here with minimal changes to the basic model.)

That’s it for tutorial #3, (other than reviewing the comment code which is below) but definitely check back for more on network models!

In future posts, we’ll be thinking about more dynamic networks (i.e., ones where the links can change over time), agents with a little more agency, and tools for generating dynamic visualizations (i.e., movies!) of stochastic processes on networks.

That really covers the bulk of the major conceptual issues. Now let’s work through the implementation.

Click Below to Review the Implementation and Commented Code!

Continue reading “Programming Dynamic Models in Python-Part 3: Outbreak on a Network”