Pandemics : : Insurance and Social Protection.

• Superior document: Springer Actuarial Series
• Author: Boado-Penas, María del Carmen.
• TeilnehmendeR: Eisenberg, Julia.; Şahin‬‬‬, Şule.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2021.; {copy}2022.
• Year of Publication: 2021
• Edition: 1st ed.
• Language: English
• Series: Springer Actuarial Series
• Physical Description: 1 online resource (314 pages)

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.