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Epidemic Random Network Simulations in a Distributed Computing Environment

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  • J. Villanueva-Oller
  • L. Acedo
  • J. A. Moraño
  • A. Sánchez-Sánchez

Abstract

We discuss a computational system following the paradigm of distributed computing, which will allow us to simulate the epidemic propagation in random networks with large number of nodes up to one million. 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 obtained results to the server to be stored until all tasks are finished and then ready to be analysed. Finally, we show that this technique allows us to disclose the emergence of seasonal patterns in the respiratory syncytial virus transmission dynamics which do not appear neither in smaller systems nor in continuous systems.

Suggested Citation

  • J. Villanueva-Oller & L. Acedo & J. A. Moraño & A. Sánchez-Sánchez, 2013. "Epidemic Random Network Simulations in a Distributed Computing Environment," Abstract and Applied Analysis, Hindawi, vol. 2013, pages 1-10, October.
    • Handle: RePEc:hin:jnlaaa:462801
    DOI: 10.1155/2013/462801
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    Cited by:

    1. Benlloch, José-María & Cortés, Juan-Carlos & Martínez-Rodríguez, David & Julián, Raul-S. & Villanueva, Rafael-J., 2020. "Effect of the early use of antivirals on the COVID-19 pandemic. A computational network modeling approach," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).

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