LEADER 06251nam 22008413u 450 001 996466419903316 005 20231110213009.0 010 $a3-030-78334-0 035 $a(CKB)5340000000068536 035 $aEBL6790721 035 $a(OCoLC)1313887644 035 $a(AU-PeEL)EBL6790721 035 $a(MiAaPQ)EBC6790721 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/72814 035 $a(PPN)258299983 035 $a(EXLCZ)995340000000068536 100 $a20220617d2021|||| uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPandemics $einsurance and social protection /$feditors, Mari?a del Carmen Boado-Penas, Julia Eisenberg, ?ule ?ahin 210 $aCham $cSpringer International Publishing AG$d2021 215 $a1 online resource (xx, 298 pages) $cillustrations (some color) 225 1 $aSpringer Actuarial 300 $aDescription based upon print version of record. 311 1 $a3-030-78333-2 327 $aIntro -- 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 327 $a2.3.1 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 327 $a3.5 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 327 $a4.3 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 327 $a5.3 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 327 $a6.3.2 Insurance Preliminaries 330 $aThis open access book collects expert contributions on actuarial modelling and related topics, from machine learning to legal aspects, and reflects on possible insurance designs during an epidemic/pandemic. Starting by considering the impulse given by COVID-19 to the insurance industry and to actuarial research, the text covers compartment models, mortality changes during a pandemic, risk-sharing in the presence of low probability events, group testing, compositional data analysis for detecting data inconsistencies, behaviouristic aspects in fighting a pandemic, and insurers' legal problems, amongst others. Concluding with an essay by a practicing actuary on the applicability of the methods proposed, this interdisciplinary book is aimed at actuaries as well as readers with a background in mathematics, economics, statistics, finance, epidemiology, or sociology. 410 0$aSpringer Actuarial 517 0 $aPandemics 606 $aEpidemics 606 $aInsurance$xMathematical models 606 $aInsurance$xStatistical methods 606 $aSocial security 606 $aAssegurances$2thub 606 $aModels matemàtics$2thub 606 $aEstadística matemática$2thub 606 $aSeguretat social$2thub 606 $aEpidèmies$2thub 608 $aLlibres electrònics$2thub 610 $aEpidemics 610 $aRisk 610 $aInsurance 610 $aSocial protection 610 $aActuarial modelling 610 $aOpen Access 615 0$aEpidemics. 615 0$aInsurance$xMathematical models. 615 0$aInsurance$xStatistical methods. 615 0$aSocial security. 615 7$aAssegurances 615 7$aModels matemàtics 615 7$aEstadística matemática 615 7$aSeguretat social 615 7$aEpidèmies 700 $aBoado-Penas$b María del Carmen$4edt$01235584 701 $aBoado-Penas$b María del Carmen$01235584 701 $aEisenberg$b Julia$01235585 701 $a?ahin???$b ?ule$01235586 801 0$bAU-PeEL 801 1$bAU-PeEL 801 2$bAU-PeEL 906 $aBOOK 912 $a996466419903316 996 $aPandemics$92869611 997 $aUNISA