The travel restrictions model

The travel restriction model is a computer simulation of a contagious disease, much like SARS which has been developed by Martin Camitz and Fredrik Liljreros with the aid and input of all s-gem members. It uses survey data on the traveling habits of the Swedish population and demonstrates travel restrictions as an effective means of mitigating the spread of an infectious disease.

In 2004 Hufnagel et al. published and article that was the foundation to our attempt at an epidemic model for Sweden. Hafnagel simulated SARS on top of the global avation network. The remake in Sweden would use a vast database of sureys on how people travelled within the country. This included not only aviation, but all means of transport from bicycles to trains.

For those not familiar with epidemic modelling, reading our primer on the subject is recommended.

The travel routes in Sweden and their intensity displayed as colors from dark red (lowest intensity) through red and yellow to white (highest travel instensity).

Extracting quality data from the database proved difficult but in the end we managed to produce a travel intensity network where each community was connected to all the other by a weighted link corresponding to the intensity of travelling made on that route. The main question was how reducing travel would affect the spread of a moderately contagious infectious disease, like SARS. Specifically, what would happen if you banned travelling over distances longer that 50 km? Or 20?

In our model we divide the country into municipalies of which there are 289. Within these the population mixes randomly like particles in a gas. This is a simple approach but is in many cases sufficient for producing results that have been shown to hold quite well. People move about in small communities in a manner resembling random mixing, so as a model it has some merits. Modeling the whole country in this way is a worse idea precisely because of the structuring of the population into communites where people live, work and go to school.

Random mixing means that everybody has an equal probability meeting averybody else but we can compensate for this flaw by adjusting the infectiousness of the disease. The circumstances favour us since Hufnagel calibrated his parameters from the real outbreak using a model very similar to ours.

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