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    Pandemics: Insurance and Social Protection

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    Contributor(s)
    Boado-Penas, María del Carmen (editor)
    Eisenberg, Julia (editor)
    Şahin‬‬‬, Şule (editor)
    Collection
    Austrian Science Fund (FWF)
    Language
    English
    Show full item record
    Abstract
    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 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.
    URI
    https://library.oapen.org/handle/20.500.12657/51459
    Keywords
    Epidemics; Risk; Insurance; Social protection; Actuarial modelling; Open Access
    DOI
    10.1007/978-3-030-78334-1
    ISBN
    9783030783341, 9783030783341
    Publisher
    Springer Nature
    Publisher website
    https://www.springernature.com/gp/products/books
    Publication date and place
    2022
    Grantor
    • Austrian Science Fund - [grantnumber unknown]
    • University of Liverpool - [grantnumber unknown]
    Imprint
    Springer International Publishing
    Series
    Springer Actuarial,
    Classification
    Applied mathematics
    Probability and statistics
    Education
    Public international law: economic and trade
    Pages
    298
    Rights
    https://creativecommons.org/licenses/by/4.0/
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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