Pandemics : : insurance and social protection / / editors, María del Carmen Boado-Penas, Julia Eisenberg, Şule Şahin.

• Superior document: Springer Actuarial
• TeilnehmendeR: Boado-Penas, María del Carmen.; Eisenberg, Julia.; Şahin‬‬‬, Şule.
• Year of Publication: 2021
• Language: English
• Series: Springer Actuarial
• Physical Description: 1 online resource (xx, 298 pages) :; illustrations (some color)
• Notes: Description based upon print version of record.

This open access book collects expert contributions on actuarial modelling and related topics, from machine learning to legal aspects, and reflects on possible insurance designs during an epidemic/pandemic. Starting by considering the impulse given by COVID-19 to the insurance industry and to actuar...

Table of Contents

• Intro
• Preface
• Acknowledgements
• Contents
• Contributors
• 1 COVID-19: A Trigger for Innovations in Insurance?
• 1.1 Introduction
• 1.2 Discussions from the Perspective of Insurance and Social Protection
• 1.2.1 Commercial Insurance
• 1.2.2 The Role of the Governments and Social Protection
• 1.3 Listening to the Wind of Change
• References
• 2 Epidemic Compartmental Models and Their Insurance Applications
• 2.1 Introduction
• 2.2 Compartmental Models in Epidemiology
• 2.2.1 SIR Model
• 2.2.2 Other Compartmental Models
• 2.3 Epidemic Insurance
• 2.3.1 Annuities and Insurance Benefits
• 2.3.2 Reserves
• 2.3.3 Further Extensions
• 2.3.4 Case Studies: COVID-19
• 2.4 Resource Management
• 2.4.1 Pillar I: Regional and Aggregate Resources Demand Forecast
• 2.4.2 Pillar II: Centralised Stockpiling and Distribution
• 2.4.3 Pillar III: Centralised Resources Allocation
• 2.5 Conclusion
• References
• 3 Some Investigations with a Simple Actuarial Model for Infections Such as COVID-19
• 3.1 Introduction
• 3.2 Multiple State Actuarial Models
• 3.3 A Simple Daily Model for Infection
• 3.4 Comparisons with the SIR Model
• 3.5 Enhancements for COVID-19 and Initial Assumptions
• 3.6 Estimating Parameters Model 1
• 3.7 Estimating Parameters Model 2
• 3.8 Comments on Results of Models 1 and 2
• 3.9 Further Extensions: Models 3 and 4
• 3.10 Comments on Results of Models 3 and 4
• 3.11 Projection Models
• 3.12 Problems and Unknowns
• 3.13 Other Countries
• 3.14 Conclusions
• References
• 4 Stochastic Mortality Models and Pandemic Shocks
• 4.1 Stochastic Mortality Models and the COVID-19 Shock
• 4.2 The Impact of COVID-19 on Mortality Rates
• 4.3 Stochastic Mortality Models and Pandemics: Single-Population Models
• 4.3.1 Discrete-Time Single Population Models
• 4.3.2 Continuous-Time Single-Population Models
• 4.4 Stochastic Mortality Models and Pandemics: Multi-population
• 4.4.1 Discrete-Time Models
• 4.4.2 Continuous-Time Models
• 4.5 A Continuous-Time Multi-population Model with Jumps
• 4.6 Conclusions
• References
• 5 A Mortality Model for Pandemics and Other Contagion Events
• 5.1 Introduction
• 5.2 Highlights of Methodology and Findings
• 5.2.1 Summary of Methodology
• 5.2.2 Summary of Findings
• 5.3 Semiparametric Regression in MCMC
• 5.3.1 MCMC Parameter Shrinkage
• 5.3.2 Spline Regressions
• 5.3.3 Why Shrinkage?
• 5.3.4 Cross Validation in MCMC
• 5.4 Model Details
• 5.4.1 Formulas
• 5.4.2 Fitting Process
• 5.5 Results
• 5.5.1 Extensions: Generalisation, Projections and R Coding
• 5.6 Conclusions
• References
• 6 Risk-Sharing and Contingent Premia in the Presence of Systematic Risk: The Case Study of the UK COVID-19 Economic Losses
• 6.1 Introduction
• 6.2 Risk Levels and Systematic Risk in Insurance
• 6.3 Mathematical Setup
• 6.3.1 Probability Space
• 6.3.2 Insurance Preliminaries