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      Effects of delayed recovery and nonuniform transmission on the spreading of diseases in complex networks

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          Abstract

          We investigate the effects of delaying the time to recovery (delayed recovery) and of nonuniform transmission on the propagation of diseases on structured populations. Through a mean-field approximation and large-scale numerical simulations, we find that postponing the transition from the infectious to the recovered states can largely reduce the epidemic threshold, therefore promoting the outbreak of epidemics. On the other hand, if we consider nonuniform transmission among individuals, the epidemic threshold increases, thus inhibiting the spreading process. When both mechanisms are at work, the latter might prevail, hence resulting in an increase of the epidemic threshold with respect to the standard case, in which both ingredients are absent. Our findings are of interest for a better understanding of how diseases propagate on structured populations and to a further design of efficient immunization strategies.

          Highlights

          ► We present a novel epidemic model which simultaneously considers the delayed recovery and non-uniform transmission effect. ► Mean-field approximation is used to derive the critical threshold of the epidemic model. ► Numerical results well agree with the mean-field result of critical threshold. ► Compared to the standard model, the delayed recovery can largely promote the outbreak of epidemics. ► Compared to the standard model, the non-uniform transmission can inhibit the outbreak of epidemics.

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          Author and article information

          Contributors
          Journal
          Physica A
          Physica A
          Physica a
          Elsevier B.V.
          0378-4371
          0378-4371
          26 November 2012
          1 April 2013
          26 November 2012
          : 392
          : 7
          : 1577-1585
          Affiliations
          [a ]Key Laboratory of Computer Vision and System (Ministry of Education), Tianjin University of Technology, Tianjin 300384, PR China
          [b ]Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin 300384, PR China
          [c ]Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, 50018 Zaragoza, Spain
          [d ]Department of Physics, Hong Kong Baptist University, Kowloon Tong, Hong Kong
          [e ]Center for Nonlinear Studies and the Beijing-Hong Kong-Singapore Joint Center for Nonlinear and Complex Systems (Hong Kong) Baptist University, Kowloon Tong, Hong Kong
          [f ]Department of Theoretical Physics, University of Zaragoza, 50009 Zaragoza, Spain
          Author notes
          [* ]Corresponding author at: Key Laboratory of Computer Vision and System (Ministry of Education), Tianjin University of Technology, Tianjin 300384, PR China. xialooking@ 123456163.com
          Article
          S0378-4371(12)01008-4
          10.1016/j.physa.2012.11.043
          7126830
          32288088
          9753b9ea-b6fc-4ee1-9407-19630907d7f7
          Copyright © 2012 Elsevier B.V. All rights reserved.

          Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

          History
          : 31 July 2012
          : 29 October 2012
          Categories
          Article

          disease spreading,complex networks,sis model,heterogeneous mean-field approach

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