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Severe Hindrance of Viral Infection Propagation in Spatially Extended Hosts

Overview of attention for article published in PLOS ONE, August 2011
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Title
Severe Hindrance of Viral Infection Propagation in Spatially Extended Hosts
Published in
PLOS ONE, August 2011
DOI 10.1371/journal.pone.0023358
Pubmed ID
Authors

José A. Capitán, José A. Cuesta, Susanna C. Manrubia, Jacobo Aguirre

Abstract

The production of large progeny numbers affected by high mutation rates is a ubiquitous strategy of viruses, as it promotes quick adaptation and survival to changing environments. However, this situation often ushers in an arms race between the virus and the host cells. In this paper we investigate in depth a model for the dynamics of a phenotypically heterogeneous population of viruses whose propagation is limited to two-dimensional geometries, and where host cells are able to develop defenses against infection. Our analytical and numerical analyses are developed in close connection to directed percolation models. In fact, we show that making the space explicit in the model, which in turn amounts to reducing viral mobility and hindering the infective ability of the virus, connects our work with similar dynamical models that lie in the universality class of directed percolation. In addition, we use the fact that our model is a multicomponent generalization of the Domany-Kinzel probabilistic cellular automaton to employ several techniques developed in the past in that context, such as the two-site approximation to the extinction transition line. Our aim is to better understand propagation of viral infections with mobility restrictions, e.g., in crops or in plant leaves, in order to inspire new strategies for effective viral control.

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Geographical breakdown

Country Count As %
Spain 1 5%
United States 1 5%
Germany 1 5%
Unknown 17 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 25%
Student > Ph. D. Student 3 15%
Student > Doctoral Student 2 10%
Professor 2 10%
Lecturer 1 5%
Other 3 15%
Unknown 4 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 25%
Physics and Astronomy 3 15%
Mathematics 2 10%
Agricultural and Biological Sciences 2 10%
Computer Science 1 5%
Other 3 15%
Unknown 4 20%