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      Mathematical and computational approaches to epidemic modeling: a comprehensive review

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          Abstract

          Mathematical and computational approaches are important tools for understanding epidemic spread patterns and evaluating policies of disease control. In recent years, epidemiology has become increasingly integrated with mathematics, sociology, management science, complexity science, and computer science. The cross of multiple disciplines has caused rapid development of mathematical and computational approaches to epidemic modeling. In this article, we carry out a comprehensive review of epidemic models to provide an insight into the literature of epidemic modeling and simulation. We introduce major epidemic models in three directions, including mathematical models, complex network models, and agent-based models. We discuss the principles, applications, advantages, and limitations of these models. Meanwhile, we also propose some future research directions in epidemic modeling.

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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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            Emergence of scaling in random networks

            Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
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              Modelling disease outbreaks in realistic urban social networks.

              Most mathematical models for the spread of disease use differential equations based on uniform mixing assumptions or ad hoc models for the contact process. Here we explore the use of dynamic bipartite graphs to model the physical contact patterns that result from movements of individuals between specific locations. The graphs are generated by large-scale individual-based urban traffic simulations built on actual census, land-use and population-mobility data. We find that the contact network among people is a strongly connected small-world-like graph with a well-defined scale for the degree distribution. However, the locations graph is scale-free, which allows highly efficient outbreak detection by placing sensors in the hubs of the locations network. Within this large-scale simulation framework, we then analyse the relative merits of several proposed mitigation strategies for smallpox spread. Our results suggest that outbreaks can be contained by a strategy of targeted vaccination combined with early detection without resorting to mass vaccination of a population.
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                Author and article information

                Contributors
                weiduan.mz@gmail.com
                Journal
                Front Comput Sci
                Front Comput Sci
                Frontiers of Computer Science
                Higher Education Press (Beijing )
                2095-2228
                2095-2236
                9 October 2015
                2015
                : 9
                : 5
                : 806-826
                Affiliations
                GRID grid.412110.7, ISNI 0000000095482110, Center of Computational Experiments and Parallel Systems Technology, College of Information Systems and Management, , National University of Defense Technology, ; Changsha, 410073 China
                Article
                3369
                10.1007/s11704-014-3369-2
                7133607
                764333d9-ef98-4a05-9c2a-213ae0716a18
                © Higher Education Press and Springer-Verlag Berlin Heidelberg 2014

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 30 September 2013
                : 4 August 2014
                Categories
                Review Article
                Custom metadata
                © Higher Education Press and Springer-Verlag Berlin Heidelberg 2015

                mathematics,complex networks,agent-based models,epidemicmodeling,human dynamics,infectious diseases

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