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      Pattern transitions in spatial epidemics: Mechanisms and emergent properties

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          Abstract

          Infectious diseases are a threat to human health and a hindrance to societal development. Consequently, the spread of diseases in both time and space has been widely studied, revealing the different types of spatial patterns. Transitions between patterns are an emergent property in spatial epidemics that can serve as a potential trend indicator of disease spread. Despite the usefulness of such an indicator, attempts to systematize the topic of pattern transitions have been few and far between. We present a mini-review on pattern transitions in spatial epidemics, describing the types of transitions and their underlying mechanisms. We show that pattern transitions relate to the complexity of spatial epidemics by, for example, being accompanied with phenomena such as coherence resonance and cyclic evolution. The results presented herein provide valuable insights into disease prevention and control, and may even be applicable outside epidemiology, including other branches of medical science, ecology, quantitative finance, and elsewhere.

          Highlights

          • Transitions between patterns are emergent properties in spatial epidemics.

          • Two types of pattern transitions in infectious diseases are shown.

          • We provide possible mechanisms of pattern transition in spatial epidemics.

          • Pattern transition promotes complexity in spatial epidemics.

          • The results are applicable in medical science, ecology, quantitative finance and so on.

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          Most cited references 171

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          Pattern formation outside of equilibrium

           M Cross,  P Hohenberg (1993)
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            THE WAVE OF ADVANCE OF ADVANTAGEOUS GENES

             R Fisher (1937)
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              Epidemic Spreading in Scale-Free Networks

              The Internet has a very complex connectivity recently modeled by the class of scale-free networks. This feature, which appears to be very efficient for a communications network, favors at the same time the spreading of computer viruses. We analyze real data from computer virus infections and find the average lifetime and persistence of viral strains on the Internet. We define a dynamical model for the spreading of infections on scale-free networks, finding the absence of an epidemic threshold and its associated critical behavior. This new epidemiological framework rationalizes data of computer viruses and could help in the understanding of other spreading phenomena on communication and social networks.
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                Author and article information

                Contributors
                Journal
                Phys Life Rev
                Phys Life Rev
                Physics of Life Reviews
                Elsevier B.V.
                1571-0645
                1873-1457
                9 August 2016
                December 2016
                9 August 2016
                : 19
                : 43-73
                Affiliations
                [a ]Complex Systems Research Center, Shanxi University, Taiyuan, Shanxi 030006, PR China
                [b ]School of Mathematical Sciences, Fudan University, Shanghai 200433, PR China
                [c ]Department of Vector Ecology and Environment, Nagasaki University Institute of Tropical Medicine (NEKKEN), Nagasaki 852-8523, Japan
                [d ]Center of Mathematics for Social Creativity, Hokkaido University, Sapporo 060-0812, Japan
                [e ]Department of Mathematics, Southeast University, Nanjing 210096, PR China
                [f ]Department of Mathematics and Statistics, University of Victoria, Victoria BC V8W 3R4, Canada
                [g ]Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Fukuoka, 816-8580, Japan
                Author notes
                Article
                S1571-0645(16)30075-6
                10.1016/j.plrev.2016.08.002
                7105263
                27567502
                © 2016 Elsevier B.V. All rights reserved.

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