00811cam2 22002531 450 SOBE0004779520150622121120.020150622e19751772|||||ita|0103 bafreIT F <<11: >>PlanchesParmaF. M. Ricci19751 v. (varie sequenze)in gran parte ill.40 cm001E6002000315722001 Encyclopédie di Diderot e d'Alembert11ITUNISOB20150622RICAUNISOBUNISOB000|Enc29856SOBE00047795M 102 Monografia moderna SBNMCons000|Enc000011-11CON29856acquistoNmenleUNISOBUNISOB20150622120941.020150622121120.0menlePlanches929252UNISOB03720nam 22006495 450 991025417410332120251116171646.010.1007/978-3-319-53208-0(CKB)3710000001072460(DE-He213)978-3-319-53208-0(MiAaPQ)EBC4812831(PPN)198871287(EXLCZ)99371000000107246020170225d2017 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierInfectious Disease Modeling A Hybrid System Approach /by Xinzhi Liu, Peter Stechlinski1st ed. 2017.Cham :Springer International Publishing :Imprint: Springer,2017.1 online resource (XVI, 271 p. 72 illus., 67 illus. in color.)Nonlinear Systems and Complexity,2195-9994 ;193-319-53206-5 3-319-53208-1 Includes bibliographical references.Introduction -- Modelling the Spread of an Infectious Disease -- Hybrid Epidemic Models -- Control Strategies for Eradication -- Discussions and Conclusions -- References -- Appendix.This volume presents infectious diseases modeled mathematically, taking seasonality and changes in population behavior into account, using a switched and hybrid systems framework. The scope of coverage includes background on mathematical epidemiology, including classical formulations and results; a motivation for seasonal effects and changes in population behavior, an investigation into term-time forced epidemic models with switching parameters, and a detailed account of several different control strategies. The main goal is to study these models theoretically and to establish conditions under which eradication or persistence of the disease is guaranteed. In doing so, the long-term behavior of the models is determined through mathematical techniques from switched systems theory. Numerical simulations are also given to augment and illustrate the theoretical results and to help study the efficacy of the control schemes.Nonlinear Systems and Complexity,2195-9994 ;19Mathematical modelsInfectious diseasesComputational complexityStatistical physicsEpidemiologyMathematical Modeling and Industrial Mathematicshttps://scigraph.springernature.com/ontologies/product-market-codes/M14068Infectious Diseaseshttps://scigraph.springernature.com/ontologies/product-market-codes/H33096Complexityhttps://scigraph.springernature.com/ontologies/product-market-codes/T11022Applications of Nonlinear Dynamics and Chaos Theoryhttps://scigraph.springernature.com/ontologies/product-market-codes/P33020Epidemiologyhttps://scigraph.springernature.com/ontologies/product-market-codes/H63000Mathematical models.Infectious diseases.Computational complexity.Statistical physics.Epidemiology.Mathematical Modeling and Industrial Mathematics.Infectious Diseases.Complexity.Applications of Nonlinear Dynamics and Chaos Theory.Epidemiology.003.3Liu Xinzhiauthttp://id.loc.gov/vocabulary/relators/aut41492Stechlinski Peterauthttp://id.loc.gov/vocabulary/relators/autBOOK9910254174103321Infectious Disease Modeling2203574UNINA