Table of Contents: Pandemics :

Pandemics : Insurance and Social Protection.

Bibliographic Details
Main Author:	Boado-Penas, María del Carmen.
Other Authors:	Eisenberg, Julia., Şahin‬‬‬, Şule.
Format:	eBook
Language:	English
Published:	Cham : Springer International Publishing AG, 2021.
Edition:	1st ed.
Series:	Springer Actuarial Series
Subjects:	Electronic books.
Online Access:	Click to View

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
6.4 Risk Management Platforms
6.4.1 Risk-Sharing Platform
6.4.2 Insurance Platform
6.4.3 Market Platform
6.5 Systematic Risk Model and Common Shocks
6.5.1 Additive Common Shock Model
6.5.2 Multiplicative Common Shock Model
6.5.3 Risk Rate Common Shock Model
6.6 Conclusion
References
7 All-Hands-On-Deck!-How International Organisations Respond to the COVID-19 Pandemic
7.1 Introduction
7.2 The EU Response to COVID-19
7.2.1 EU Commission Response
7.2.2 The ECDC Response
7.3 The World Bank Response to COVID-19
7.3.1 Pandemic Emergency Financial Facility (PEF)
7.4 Other International Responses to COVID-19
7.4.1 The UN Response
7.4.2 The WHO Response
7.4.3 The OECD Response
7.4.4 The ILO Response
7.5 Consequences of COVID-19 Responses on Social Security and Pensions.
7.6 The Need of a United Action Tactic
References
8 Changes in Behaviour Induced by COVID-19: Obedience to the Introduced Measures
8.1 Introduction: Pandemics and Isolation
8.2 Obedience to the Introduced Rules After COVID-19 Across Countries
8.2.1 Citizens' Demographic Characteristics
8.2.2 Cultural Tradition
8.3 Behavioural Changes Due to COVID-19
8.3.1 Consumption Patterns
8.3.2 Unhealthy Consumption Habits
8.3.3 Teleworking
8.3.4 Gender and Family Violence
8.4 Discussion and Conclusions
References
9 COVID-19 and Optimal Lockdown Strategies: The Effect of New and More Virulent Strains
9.1 Introduction
9.1.1 The Challenge of New Virus Variants
9.1.2 Review of Past Findings
9.1.3 Review of Other Related Literature
9.2 The Optimal Start and Length of a Lockdown
9.2.1 The Model
9.2.2 Results
9.3 The Optimal Lockdown Intensity
9.3.1 Results
9.4 Discussion
References
10 Diagnostic Tests and Procedures During the COVID-19 Pandemic
10.1 Introduction
10.2 Laboratory Diagnosis: Pre-analytical Issues
10.2.1 Specimen Types and Specimen Collection
10.2.2 Biosafety Considerations
10.3 Laboratory Diagnosis: Analytical Issues
10.3.1 Non-molecular Methods
10.3.2 Molecular Methods
10.3.3 Point-of-Care and Home Sample Collection and Testing
10.3.4 Assay Selection
10.3.5 Pooled Screen Testing
10.3.6 Viral Load Testing
10.4 Laboratory Diagnosis: Post-analytical Issues
10.5 Interpretation of Serology Results
10.5.1 Interpretation of Molecular Results
10.5.2 Tests Beyond Detection and Diagnosis
10.6 Concluding Remarks
References
11 Pooled Testing and Its Applications in the COVID-19 Pandemic
11.1 Introduction
11.1.1 Testing for COVID-19
11.1.2 Stages of a Pooled Testing Algorithm
11.1.3 Who and Why to Test.
11.2 Pooled Testing Algorithms for Perfect Tests
11.2.1 Outline and Model
11.2.2 Individual Testing
11.2.3 Dorfman's Algorithm
11.2.4 Grid Algorithms
11.2.5 Pooling Algorithms Based on (r,s)-Regular Designs
11.3 Pooled Testing Algorithms for Imperfect Tests
11.3.1 The Model
11.3.2 Analysis of Individual Testing and Dorfman's Algorithm
11.3.3 One-Stage Testing
11.4 Practical Challenges for Pooled Testing
11.5 Uses of Pooled Testing in the COVID-19 Pandemic
11.5.1 Dorfman's Algorithm at the University of Cambridge
11.5.2 The Grid and P-BEST Algorithms in Israel
11.5.3 A Multi-stage (r,s)-Regular Algorithm in Rwanda
11.5.4 Other Uses of Pooled Testing
11.6 Applications of Pooled Testing for COVID-19: Some Conclusions
11.6.1 Pooled Testing for Asymptomatic Subpopulations
11.6.2 Pooled Testing and Vaccination Programmes
11.6.3 Pooled Testing for Surveillance
References
12 Outlier Detection for Pandemic-Related Data Using Compositional Functional Data Analysis
12.1 Introduction
12.1.1 Compositional Data Analysis Concepts
12.1.2 Functional Data
12.2 Smoothing for CODA Time Series
12.3 Outlier Detection in Compositional FDA
12.4 Application to COVID-19 Data
12.5 Summary and Conclusions
References
13 The Legal Challenges of Insuring Against a Pandemic
13.1 Introduction
13.2 Summary of the Traditional Approach to Insurance
13.2.1 The Origins of Insurance
13.2.2 The Insurance Indemnity Principle
13.3 The Effect of COVID-19 on the Insurance Industry
13.3.1 The Effect of COVID-19 on Business Interruption Insurance Policyholders
13.3.2 The Effect of COVID-19 on Insurers
13.4 Life Insurance Versus Business Interruption Insurance
13.5 Why Existing Indemnity Based Pandemic Insurance Products Are Not Working.
13.6 Proposals Across the World for Resolving the Business Interruption Insurance Deficit
13.7 What Is Parametric Insurance?
13.7.1 Working Examples of Parametric Insurance
13.7.2 Challenges
13.7.3 Opportunities
13.7.4 Could Parametric Insurance Be the Answer for SMEs During a Pandemic?
References
14 An Actuary's Opinion: How to Get Through a Pandemic
14.1 Questions to Be Tackled from an Actuary's Perspective
14.2 Managing a Pandemic as a (Re)insurer
14.2.1 Insurability of a Pandemic
14.2.2 Risk Management During a Pandemic
14.2.3 Reflecting Potential Future Consequences of COVID-19
14.3 Managing Pandemics from a Governmental Perspective
14.3.1 Deciding on the Right Measures During a Pandemic
14.3.2 Mitigating Economic Risks for Future Pandemics
14.4 Conclusion: Our Learnings
References.

Pandemics : Insurance and Social Protection.

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