000			a
999			_c29752 _d29752
008			191120b xxu||||| |||| 00| 0 eng d
020			_a9783319508047
082			_a511.8 _bKIS
100			_aKiss, Istvan Z.
245			_aMathematics of epidemics on networks : from exact to approximate models
260			_aCham _bSpringer _c2017
300			_axviii, 413 p. _bill. _c24 cm.
365			_b74.99 _cEUR _d82.00
490			_aInterdisciplinary applied mathematics _vv. 46
504			_aIncludes bibliographical references and index.
520			_aThis textbook provides an exciting new addition to the area of network science featuring a stronger and more methodical link of models to their mathematical origin and explains how these relate to each other with special focus on epidemic spread on networks. The content of the book is at the interface of graph theory, stochastic processes and dynamical systems. The authors set out to make a significant contribution to closing the gap between model development and the supporting mathematics. This is done by: Summarising and presenting the state-of-the-art in modeling epidemics on networks with results and readily usable models signposted throughout the book; Presenting different mathematical approaches to formulate exact and solvable models; Identifying the concrete links between approximate models and their rigorous mathematical representation; Presenting a model hierarchy and clearly highlighting the links between model assumptions and model complexity; Providing a reference source for advanced undergraduate students, as well as doctoral students, postdoctoral researchers and academic experts who are engaged in modeling stochastic processes on networks; Providing software that can solve the differential equation models or directly simulate epidemics in networks. Replete with numerous diagrams, examples, instructive exercises, and online access to simulation algorithms and readily usable code, this book will appeal to a wide spectrum of readers from different backgrounds and academic levels. Appropriate for students with or without a strong background in mathematics, this textbook can form the basis of an advanced undergraduate or graduate course in both mathematics and biology departments alike.
650			_aMathematical Models
650			_aMathematical Statistics
650			_aProbabilities
650			_aMathematical and Computational Biology
650			_aProbability Theory and Stochastic Processes
650			_aApplications of Graph Theory and Complex Networks
650			_aDifferentiable Dynamical Systems
650			_aDynamical Systems and Ergodic Theory
650			_aEpidemiology and medical statistics
650			_aBiomathematics
650			_aNonlinear Science
710			_aMiller, Joel C.
710			_aSimon, Peter L.
942			_2ddc _cBK