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Mathematical Modelling and Analysis of Infectious Diseases / / edited by Khalid Hattaf, Hemen Dutta
| Mathematical Modelling and Analysis of Infectious Diseases / / edited by Khalid Hattaf, Hemen Dutta |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XI, 342 p. 113 illus.) |
| Disciplina | 614.4015118 |
| Collana | Studies in Systems, Decision and Control |
| Soggetto topico |
Applied mathematics
Engineering mathematics Veterinary medicine Mathematical and Computational Engineering Veterinary Microbiology, Parasitology and Infectious Diseases |
| ISBN | 3-030-49896-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Pathogen Evolution when Transmission and Virulence are Stochastic -- On the relationship between the basic reproduction number and the shape of the spatial domain -- Cause and Control strategy for infectious diseases with nonlinear incidence and treatment rate -- Global stability of a delay virus dynamics model with mitotic transmission and cure rate -- Dynamics of a fractional-order hepatitis B epidemic model and its solutions by nonstandard numerical schemes On SICA models for HIV transmission -- Analytical and numerical solutions of a TB-HIV/AIDS co-infection model via fractional derivatives without singular kernel. |
| Record Nr. | UNINA-9910411932003321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Mathematical studies on human disease dynamics : emerging paradigms and challenges : AMS-IMS-SIAM Joint Summer Research Conference, competitive mathematical models of disease dynamics: emerging paradigms and challenges, July 17-21, 2005, Snowbird, Utah / Abba Gumel, editor-in-chief ; Carlos Castillo-Chavez, Ronald E. Mickens, Dominic P. Clemence, editors
| Mathematical studies on human disease dynamics : emerging paradigms and challenges : AMS-IMS-SIAM Joint Summer Research Conference, competitive mathematical models of disease dynamics: emerging paradigms and challenges, July 17-21, 2005, Snowbird, Utah / Abba Gumel, editor-in-chief ; Carlos Castillo-Chavez, Ronald E. Mickens, Dominic P. Clemence, editors |
| Autore | AMS-IMS-SIAM Joint Summer Research Conference on Modeling the dynamics of human disease : emerging paradigms and challenges <2005 ; Snowbird, Utah> |
| Pubbl/distr/stampa | Providence, R. I. : American Mathematical Society, c2006 |
| Descrizione fisica | xii, 389 p. : ill. ; 26 cm |
| Disciplina | 614.4015118 |
| Altri autori (Persone) |
Gumel, Abbaauthor
Castillo-Chávez, Carlos Mickens, Ronald E. Clemence, Dominic P. |
| Collana | Contemporary mathematics, 0271-4132 ; 410 |
| Soggetto topico |
Epidemiology - Mathematical models - Congresses
Diseases - Mathematical models - Congresses |
| ISBN | 0821837753 |
| Classificazione |
AMS 92-06
AMS 92D30 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISALENTO-991002432419707536 |
Mathematical understanding of infectious disease dynamics [[electronic resource] /] / editors Stefan Ma, Yingcun Xia
| Mathematical understanding of infectious disease dynamics [[electronic resource] /] / editors Stefan Ma, Yingcun Xia |
| Pubbl/distr/stampa | New Jersey, : World Scientific, c2009 |
| Descrizione fisica | 1 online resource (240 p.) |
| Disciplina | 614.4015118 |
| Altri autori (Persone) |
MaStefan
XiaYingcun |
| Collana | Lecture notes series |
| Soggetto topico |
Communicable diseases - Epidemiology - Mathematical models
Medicine |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-282-44100-0
9786612441004 981-283-483-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | CONTENTS; Foreword; Preface; The Basic Epidemiology Models: Models, Expressions for R0, Parameter Estimation, and Applications Herbert W. Hethcote; Epidemiology Models with Variable Population Size Herbert W. Hethcote; Age-Structured Epidemiology Models and Expressions for R0 Herbert W. Hethcote; Clinical and Public Health Applications of Mathematical Models John W. Glasser; Non-identifiables and Invariant Quantities in Infectious Disease Models Ping Yan |
| Record Nr. | UNINA-9910456724603321 |
| New Jersey, : World Scientific, c2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Mathematical understanding of infectious disease dynamics [[electronic resource] /] / editors Stefan Ma, Yingcun Xia
| Mathematical understanding of infectious disease dynamics [[electronic resource] /] / editors Stefan Ma, Yingcun Xia |
| Pubbl/distr/stampa | New Jersey, : World Scientific, c2009 |
| Descrizione fisica | 1 online resource (240 p.) |
| Disciplina | 614.4015118 |
| Altri autori (Persone) |
MaStefan
XiaYingcun |
| Collana | Lecture notes series |
| Soggetto topico |
Communicable diseases - Epidemiology - Mathematical models
Medicine |
| ISBN |
1-282-44100-0
9786612441004 981-283-483-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | CONTENTS; Foreword; Preface; The Basic Epidemiology Models: Models, Expressions for R0, Parameter Estimation, and Applications Herbert W. Hethcote; Epidemiology Models with Variable Population Size Herbert W. Hethcote; Age-Structured Epidemiology Models and Expressions for R0 Herbert W. Hethcote; Clinical and Public Health Applications of Mathematical Models John W. Glasser; Non-identifiables and Invariant Quantities in Infectious Disease Models Ping Yan |
| Record Nr. | UNINA-9910780905403321 |
| New Jersey, : World Scientific, c2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Mathematical understanding of infectious disease dynamics / / editors Stefan Ma, Yingcun Xia
| Mathematical understanding of infectious disease dynamics / / editors Stefan Ma, Yingcun Xia |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | New Jersey, : World Scientific, c2009 |
| Descrizione fisica | 1 online resource (240 p.) |
| Disciplina | 614.4015118 |
| Altri autori (Persone) |
MaStefan
XiaYingcun |
| Collana | Lecture notes series |
| Soggetto topico |
Communicable diseases - Epidemiology - Mathematical models
Medicine |
| ISBN |
1-282-44100-0
9786612441004 981-283-483-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | CONTENTS; Foreword; Preface; The Basic Epidemiology Models: Models, Expressions for R0, Parameter Estimation, and Applications Herbert W. Hethcote; Epidemiology Models with Variable Population Size Herbert W. Hethcote; Age-Structured Epidemiology Models and Expressions for R0 Herbert W. Hethcote; Clinical and Public Health Applications of Mathematical Models John W. Glasser; Non-identifiables and Invariant Quantities in Infectious Disease Models Ping Yan |
| Record Nr. | UNINA-9910824472603321 |
| New Jersey, : World Scientific, c2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Mathematics of Public Health : Mathematical Modelling from the Next Generation
| Mathematics of Public Health : Mathematical Modelling from the Next Generation |
| Autore | David Jummy |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing AG, , 2023 |
| Descrizione fisica | 1 online resource (325 pages) |
| Disciplina | 614.4015118 |
| Altri autori (Persone) | WuJianhong |
| Collana | Fields Institute Communications Series |
| ISBN | 3-031-40805-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- 1 Mathematical Models: Perspectives of Mathematical Modelers and Public Health Professionals -- 1.1 Natural History of Disease in Humans -- 1.2 Introduction to Mathematical Epidemiology -- 1.3 Model Formulation and Examples of Some Communicable Disease Models -- 1.3.1 Simple SIR Compartmental Models -- 1.3.2 Simple Endemic Models -- 1.3.3 Agent-Based Models -- 1.3.4 Network Models -- 1.3.5 Machine Learning Models -- 1.3.5.1 Estimating Parameters -- 1.3.5.2 Estimating Hidden States -- 1.4 Qualitative Analysis of Selected Models -- 1.4.1 Epidemic Model -- 1.4.2 Endemic Model -- 1.4.3 Network Model -- 1.5 Quantitative Analysis -- 1.6 Review of Mathematical Models of Selected Communicable Diseases and Their Impacts on Policy- and Decision-Making -- 1.6.1 SARS 2003 Pandemic Models -- 1.6.2 Pandemic Influenza Models -- 1.6.3 SARS-CoV-2 Pandemic Models -- 1.6.4 HIV Models -- 1.6.5 HCV Models -- 1.7 Model Algorithms for a Simple SIR Model -- 1.7.1 Python Code -- 1.7.2 Julia Code -- 1.7.3 R Code -- 1.7.4 MATLAB Code -- 1.8 Human Epidemiology Data, Model Fitting, and Parameter Estimation -- 1.9 Conclusion -- References -- 2 Discovering First Principle of Behavioural Change in Disease Transmission Dynamics by Deep Learning -- 2.1 Introduction -- 2.2 Expert-Based Behavioural Change Transmission Dynamics Models -- 2.2.1 Calculation of the Final Epidemic Size -- 2.2.2 Applications to the Ontario's First COVID-19 Pandemic Wave -- 2.3 Two-Step Recovering-Explaining Framework -- 2.3.1 Universal Differential Equations -- 2.3.2 Data-Driven Methods or Equation-Searching Methods -- 2.3.2.1 Symbolic Regression -- 2.3.2.2 Sparse Identification of Nonlinear Dynamics (SINDy) -- 2.3.3 Two-Step Recovering-Explaining Methods -- 2.4 Deep Learning-Based Behavioural Change Transmission Dynamics Models -- 2.4.1 The Behavioural Change Laws.
2.5 Discussions and Conclusions -- References -- 3 Understanding Epidemic Multi-wave Patterns via Machine Learning Clustering and the Epidemic Renormalization Group -- 3.1 Introduction -- 3.2 Renormalization Group Epidemiology: From eRG to CeRG -- 3.2.1 The Single-Wave eRG Approach -- 3.2.2 The Multi-wave CeRG Approach -- 3.3 A Machine Learning Approach to the Wave Pattern -- 3.3.1 The Status of Variants -- 3.3.2 Method -- 3.3.2.1 Cluster Algorithm -- 3.3.2.2 Emerging Variants as Persistent Time-Ordered Cluster Chains -- 3.3.3 Application to COVID-19 Data -- 3.4 An Epidemiological Theory of Variants: The MeRG Framework -- 3.4.1 The Model -- 3.4.2 Flow Among Variants: Fixed Points and (Ir)relevant Operators -- 3.4.3 Connecting Variant Dynamics to the CeRG -- 3.4.4 Fitting the Real Data -- 3.5 Conclusion -- References -- 4 Contact Matrices in Compartmental Disease Transmission Models -- 4.1 Introduction -- 4.2 Motivating Example -- 4.3 Defining Contact Matrices -- 4.3.1 What Is a Contact? -- 4.3.2 Sources of Contact Data -- 4.3.3 Assumptions and Parametric Forms -- 4.3.4 Example -- 4.4 Properties of Contact Matrices -- 4.4.1 Balancing Contact Matrices -- 4.4.2 Intrinsic Connectivity -- 4.4.3 Example -- 4.5 Restratifying Contact Matrices -- 4.5.1 Intuition and Equations for Restratification -- 4.5.2 Example -- 4.6 Mobility in Contact Matrices -- 4.6.1 Mobility Data and Mobility Matrices -- 4.6.2 Contact Matrices from Mobility Matrices -- 4.6.3 Integrating Age Mixing and Mobility Data in Contact Matrices -- 4.6.4 Example -- References -- 5 An Optimal Control Approach for Public Health Interventions on an Epidemic-Viral Model in Deterministic and Stochastic Environments -- 5.1 Introduction -- 5.1.1 A Fast Time Scale Viral Model -- 5.1.2 SIQR Epidemic Model with a Coupled Viral Model -- 5.1.3 Qualitative Analysis of the Coupled Model. 5.2 Optimal Control Analysis -- 5.2.1 Investigation of the Deterministic Optimal Control -- 5.2.2 Investigation of the Stochastic Optimal Control -- 5.3 Numerical Simulations -- 5.4 Conclusion -- References -- 6 Modeling Airborne Disease Dynamics: Progress and Questions -- 6.1 Introduction -- 6.2 Viral Matter in an Infectious Individual -- 6.3 Aerosol Size Distribution in Human Exhalations -- 6.4 Airborne Transmission of Aerosols -- 6.5 Transmission Through Fomites -- 6.6 Infection Probability of a Susceptible -- 6.7 Probability Distribution for Number of Secondary Infections Z -- 6.8 Conclusion -- References -- 7 Modeling Mutation-Driven Emergence of Drug-Resistance: A Case Study of SARS-CoV-2 -- 7.1 Introduction -- 7.2 Methods -- 7.2.1 Model Structure -- 7.2.2 Model Equations -- 7.2.3 Reproduction Number -- 7.3 Results -- 7.3.1 Baseline Scenario -- 7.3.2 Waning Immunity and Reinfection -- 7.4 Discussion -- References -- 8 A Categorical Framework for Modeling with Stock and Flow Diagrams -- 8.1 Introduction -- 8.2 The Syntax of Stock-Flow Diagrams -- 8.3 The Semantics of Stock-Flow Diagrams -- 8.3.1 ODEs (Ordinary Differential Equations) -- 8.3.2 Causal Loop Diagrams -- 8.3.3 System Structure Diagrams -- 8.4 Composing Open Stock-Flow Diagrams -- 8.5 Stratifying Typed System Structure Diagrams -- 8.6 ModelCollab: A Graphical Real-Time Collaborative Compositional Modeling Tool -- 8.7 Conclusion -- References -- 9 Agent-Based Modeling and Its Trade-Offs: An Introduction and Examples -- 9.1 Introduction -- 9.2 Characteristics of Agent-Based Models -- 9.2.1 Parameters -- 9.2.2 State, Actions, and Rules -- 9.2.3 Environment -- 9.2.4 Outputs and Emergent Behavior -- 9.2.5 Stochastics -- 9.2.6 Interventions -- 9.3 Example: Chickenpox -- 9.3.1 Chickenpox and Shingles -- 9.3.2 Model Scope -- 9.3.3 Statecharts -- 9.3.4 Model Fit -- 9.3.5 Costs and QALYs. 9.3.6 Suitability of ABM -- 9.3.7 Choice of AnyLogic as a Tool -- 9.4 Example: Pertussis -- 9.4.1 Pertussis -- 9.4.2 Model Scope -- 9.4.3 Model Structure -- 9.4.4 Model Fit -- 9.4.5 Scenarios -- 9.4.6 Suitability of ABM -- 9.5 Trade-Offs Between ABMs and Aggregate Models -- 9.6 Summary -- References -- 10 Mathematical Assessment of the Role of Interventions Against SARS-CoV-2 -- 10.1 Introduction -- 10.2 Formulation of Vaccination Model for COVID-19 -- 10.2.1 Data Fitting and Parameter Estimation -- 10.2.2 Basic Qualitative Properties -- 10.3 Existence and Asymptotic Stability of Equilibria -- 10.3.1 Disease-Free Equilibrium -- 10.3.1.1 Local Asymptotic Stability of DFE -- 10.3.1.2 Existence of Backward Bifurcation -- 10.3.1.3 Global Asymptotic Stability of DFE: Special Cases -- 10.3.2 Existence and Stability of Endemic Equilibria: Special Case -- 10.3.2.1 Existence -- 10.3.2.2 Local Asymptotic Stability -- 10.3.3 Vaccine-Induced Herd Immunity Threshold -- 10.3.4 Global Parameter Sensitivity Analysis -- 10.4 Numerical Simulations -- 10.4.1 Effect of Masking as a Singular Control and Mitigation Intervention -- 10.4.2 Assessing the Combined Impact of Vaccination and Masks on Herd Immunity Threshold -- 10.4.3 Assessing the Combined Impact of Vaccination and Masks on Daily New Cases -- 10.5 Discussion and Conclusions -- Appendix 1: Proof of Theorem 3 -- Computation of Left and Right Eigenvectors of Jβp* -- Computation of Backward Bifurcation Coefficients, a and b -- Appendix 2: Proof of Theorem 4 -- Appendix 3: Proof of Theorem 5 -- Proof of Positive Invariance and Attractivity of Ω** -- Next-Generation Matrices for the Second Special Case of the Model -- Proof of Theorem 5 -- Appendix 4: Proof of Theorem 7 -- Case 1: θ= 0 -- Case 2: θ≠0 -- References -- 11 Long-Term Dynamics of COVID-19 in a Multi-strain Model -- 11.1 Introduction -- 11.2 Methodology. 11.2.1 Model Description -- 11.2.2 Parameter Estimation -- 11.2.3 Data Sources -- 11.3 COVID-19 Long-Term Scenarios Modelling -- 11.4 Results -- 11.5 Discussion -- 11.6 Conclusion -- 11.7 Supplementary Information -- References -- Correction to: Contact Matrices in Compartmental Disease Transmission Models. |
| Record Nr. | UNINA-9910799203303321 |
David Jummy
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| Cham : , : Springer International Publishing AG, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Sociophysics approach to epidemics / / Jun Tanimoto
| Sociophysics approach to epidemics / / Jun Tanimoto |
| Autore | Tanimoto Jun <1965-> |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (297 pages) : illustrations |
| Disciplina | 614.4015118 |
| Collana | Evolutionary Economics and Social Complexity Science |
| Soggetto topico |
Epidemiology - Mathematical models
Game theory |
| ISBN | 981-336-481-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgments -- Contents -- About the Author -- Chapter 1: A Social-Physics Approach to Modeling and Analyzing Epidemics -- 1.1 Modeling of a Social-Complex System: A Human-Physics System -- 1.2 How the Spread of an Infectious Disease Can be Modeled?-Mathematical Epidemiology -- 1.3 How Human Behavior Can be Modeled?-Evolutionary Game Theory -- References -- Chapter 2: Evolutionary Game Theory: Fundamentals and Applications for Epidemiology -- 2.1 Two-Player and Two-Strategy Games -- 2.1.1 Theoretical Foundation -- 2.1.2 Social Viscosity -- 2.1.3 Multi-Agent-Simulation Approach -- 2.2 Multi-Player Games -- 2.3 Social Dilemma and its Mathematical Quantification -- 2.3.1 Concept of the Universal Scaling for Dilemma Strength -- 2.3.1.1 Direct Reciprocity -- 2.3.1.2 Indirect Reciprocity -- 2.3.1.3 Kin Selection -- 2.3.1.4 Group Selection -- 2.3.1.5 Network Reciprocity -- 2.3.2 Concept of a Social Efficiency Deficit -- 2.3.2.1 Donor and Recipient Game -- 2.3.2.2 Public Goods Game -- 2.3.2.3 PD with Social Viscosity -- 2.3.2.4 Chicken Game -- 2.3.3 Application of SED -- 2.3.3.1 Derivation of SED -- 2.3.3.2 Discussion -- References -- Chapter 3: Fundamentals of Mathematical Epidemiology and the Vaccination Game -- 3.1 Basic Model: SIR, SIS, and SEIR -- 3.1.1 Formulation of the SIR Model -- 3.1.2 Herd Immunity -- 3.1.3 Formulation of the SIS Model -- 3.1.4 Formulation of the SEIR Model -- 3.2 Theoretical Framework of a Vaccination Game -- 3.2.1 Two Models to Represent Stochastic Vaccination: Effectiveness and Efficiency -- 3.2.1.1 Effectiveness Model -- 3.2.1.2 Efficiency Model -- 3.2.2 Strategy-Updating Rule -- 3.2.2.1 Individual-Based Risk Assessment (IB-RA) -- 3.2.2.2 Strategy-Based Risk Assessment (SB-RA) -- 3.2.2.3 Direct Commitment (DC) -- 3.2.3 Global Dynamics for Strategy Updating -- 3.3 MAS Approach to the Vaccination Game.
3.3.1 Spatial Structure When Taking the MAS Approach -- 3.3.2 Effective Transmission Rate, βe, and Effective Recovery Rate, γe -- 3.3.3 Result of the Vaccination Game -- Comparison Between the MAS and ODE Models -- 3.4 Effect of the Underlying Topology -- 3.4.1 Degree Distribution -- 3.4.2 Networked SIR Model -- 3.4.3 Networked SIR/V Process with an Effectiveness Model -- 3.4.4 Networked SIR/V Process with an Efficiency Model -- 3.4.5 Payoff Structure and Global Dynamics for Strategy Updating -- 3.4.6 Result of the Networked Vaccination Game -- Comparison of Different Degree Distributions -- References -- Chapter 4: Plural Strategies: Intervention Game -- 4.1 Alternative Provisions Featuring Different Combinations of Cost-Effect Performances -- 4.2 Model Structure -- 4.2.1 Formulation of the SVMBIR Model -- 4.2.2 Payoff Structure -- 4.2.3 Strategy-Updating and Global Dynamics -- 4.2.3.1 Individual-Based Risk Assessment (IB-RA) -- 4.2.3.2 Strategy-Based Risk Assessment (SB-RA) -- 4.2.3.3 Direct Commitment (DC) -- 4.3 Result and Discussion -- References -- Chapter 5: Quarantine and Isolation -- 5.1 Social Background -- Quarantine or Isolation? -- 5.2 Model Structure -- 5.2.1 Formulation of the SVEIR Model -- 5.2.2 Payoff Structure -- 5.2.3 Strategy Updating and Global Dynamics -- 5.3 Result and Discussion -- 5.3.1 Local Dynamics in a Single Season -- 5.3.2 Social Equilibrium from Global Dynamics -- 5.3.3 Public-Based (Passive) Provision: Quarantine and Isolation vs. Individual-Based (Active) Provision: Vaccination -- 5.3.4 Passive Provision Rather Compensates the Shadow by Active Provision Than Mutually Competing -- 5.3.5 Comprehensive Discussion -- References -- Chapter 6: Media Information Effect Hampering the Spread of Disease -- 6.1 Positive Effect of Media Helps to Suppress the Spread of an Epidemic -- 6.2 Model Structure. 6.2.1 Formulation of the SVIR-UA Model -- 6.2.2 Payoff Structure -- 6.2.3 Strategy Updating and Global Dynamics -- 6.2.3.1 Individual-Based Risk Assessment (IB-RA) -- 6.2.3.2 Strategy-Based Risk Assessment (SB-RA) -- 6.2.4 Spatial Structure -- 6.2.5 Initial Condition and Numerical Procedure -- 6.3 Results and Discussion -- References -- Chapter 7: Immunity Waning Effect -- 7.1 Introduction and Background: Immunity and Its Degrading in View of Infectious Disease -- 7.2 Model Structure -- 7.2.1 Formulation of the SVnIR2n Model -- 7.2.2 Parameterization for Immunity Waning Effect -- 7.2.3 Time Evolution of Vaccination by Behavior Model -- 7.3 Result and Discussion -- 7.3.1 Fundamental Characteristic of Time Evolution -- 7.3.2 Dynamics Observed in Trajectory -- 7.3.3 Phase Diagram Analysis -- 7.3.4 Comprehensive Discussion -- References -- Chapter 8: Pre-emptive Vaccination Versus Antiviral Treatment -- 8.1 Introduction and Background: Behavioral Incentives in a Vaccination-Dilemma Setting with an Optional Treatment -- 8.2 Model Structure -- 8.2.1 Formulation of the SVITR Model -- 8.2.2 Reproduction Number -- 8.2.3 Payoff Structure -- 8.2.4 Strategy Updating and Global Dynamics -- 8.2.4.1 Individual-Based Risk Assessment (IB-RA) -- 8.2.4.2 Strategy-Based Risk Assessment (SB-RA) -- 8.2.5 Utility of Treatment -- 8.3 Result and Discussion -- 8.3.1 SVITR Dynamics -- 8.3.2 Interplay Between Vaccination and Treatment Costs -- 8.3.3 Individual-Versus Society-Centered Decision Making -- 8.3.4 Interplay Between Vaccine and Treatment Characteristics -- 8.3.5 Comprehensive Discussion -- References -- Chapter 9: Pre-emptive Vaccination Versus Late Vaccination -- 9.1 Introduction and Background: Is Pre-Emptive or Late Vaccination More Beneficial? -- 9.2 Model Structure -- 9.2.1 Formulation of the Dynamics of the Epidemic and Human Behavior -- 9.2.2 Payoff Structure. 9.2.3 Strategy Updating and Global Dynamics -- 9.3 Result and Discussion -- References -- Chapter 10: Influenza Vaccine Uptake -- 10.1 Introduction and Background: Multiple Strains and Multiple Vaccines -- 10.2 Model Structure -- 10.2.1 Dynamics of Epidemic Spread -- 10.2.2 Payoff Structure -- 10.2.3 Strategy Updating and Global Dynamics -- 10.3 Result and Discussion -- 10.3.1 Dynamics in a Single Season -- 10.3.2 Evolutionary Outcome of Vaccination Coverage -- 10.3.3 Phase Diagrams -- 10.3.4 Analysis of Social-Efficiency Deficit (SED) -- 10.3.5 Comprehensive Discussion -- Chapter 11: Optimal Design of a Vaccination-Subsidy Policy -- 11.1 Introduction and Background: Free Ticket, Discount Ticket, or a Combination of the Two-Which Subsidy Policy Is Socially O... -- 11.2 Model Design -- 11.2.1 Vaccination Game on a Scale-Free Network -- 11.2.2 Subsidy Policies -- 11.2.3 MAS Approach -- 11.3 Result and Discussion -- Chapter 12: Flexible Modeling -- 12.1 Introduction and Background: A New Cyclic Epidemic-Vaccination Model: Embedding the Attitude of Individuals Toward Vaccin... -- 12.2 Model Depiction -- 12.3 Result and Discussion -- Postscript -- Index. |
| Record Nr. | UNINA-9910483687703321 |
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Tanimoto Jun <1965->
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| Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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