Keywords: random network, distributed computing, epidemic diseases, transmission dynamics.

Random networks are emerging in epidemiology as a way of simulationg more realistic random contact behaviours. A network is a set of nodes representing individuals. Labels or properties may be assigned to each node, for instance, age, sex, state with respect to disease (susceptibility, infection, recovery, etc.). Nodes are connected by ties that represent disease transmission paths. Once the network model and the disease evolution rules are stated, it is possible to simulate the evolution of the network over time and study the effect of disease on the population.

When the edges are assigned randomly, we are in a random network. There are a lot of ways to assign ties to nodes randomly, depending on the probability distribution chosen: Poisson, exponential distribution, power-law distribution (also known as scale-free networks), etc. Papers studying the transmission dynamics of certain diseases using scale-free networks have recently published [1]. Scale-free networks allow the dynamics analysis of infectious diseases in a similar way to the continuous models, which is an intersting advantage. However, only diseases with specific trasnsmission structures, for example, sexually transmitted diseases, can be approached using scale-free networks.

On the other hand, there are more democratic diseases (with more and different paths for spreading) like influenza, rotavirus or respiratory syncytial virus, which are better modelled by a Poisson distribution ties assignment. Nevertheless, this modelling cannot be approached like continuous models and parameter estimation should be done carrying out simulations with a large set of parameters with an intensive computing environment.

In this paper, we present the description of a computational system following the paradigm of distributed computing, which will allow the estimation of parameters in random network epidemic models. This paradigm consists of a server that delivers tasks to be carried out by client computers. When the task is finished, the client sends the results obtained to the server to be stored until all tasks are finished and ready to be analysed. The idea is similar to the one used in the BOINC project [2] but simpler, using less resources and with quicker development and implementation.

1

R. Pastor-Satorras, A. Vespignani, "Epidemic Spreading in Scale-Free Networks", Phys. Rev. Lett., 86, 3200-3203, 2001. doi:10.1103/PhysRevLett.86.3200

2

http://boinc.berkeley.edu/

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