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A markovian random walk model of epidemic spreading

Abstract : We analyze the dynamics of a population of independent random walkers on a graph and develop a simple model of epidemic spreading. We assume that each walker visits independently the nodes of a finite ergodic graph in a discrete-time markovian walk governed by his specific transition matrix. With this assumption, we first derive an upper bound for the reproduction numbers. Then we assume that a walker is in one of the states: susceptible, infectious, or recovered. An infectious walker remains infectious during a certain characteristic time. If an infectious walker meets a susceptible one on the same node there is a certain probability for the susceptible walker to get infected. By implementing this hypothesis in computer simulations we study the space-time evolution of the emerging infection patterns. Generally, random walk approaches seem to have a large potential to study epidemic spreading and to identify the pertinent parameters in epidemic dynamics.
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https://hal-cnrs.archives-ouvertes.fr/hal-02968842
Contributor : Thomas Michelitsch <>
Submitted on : Friday, October 16, 2020 - 10:30:04 AM
Last modification on : Monday, October 19, 2020 - 3:10:59 AM

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arXiv-2010.07731.pdf
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  • HAL Id : hal-02968842, version 1
  • ARXIV : 2010.07731

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Michael Bestehorn, Alejandro Riascos, Thomas Michelitsch, Bernard Collet. A markovian random walk model of epidemic spreading. 2020. ⟨hal-02968842⟩

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