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Open Access 2022 | Open Access | Buch

Pandemics: Insurance and Social Protection

herausgegeben von: María del Carmen Boado-Penas, Julia Eisenberg, Şule Şahin‬‬‬

Verlag: Springer International Publishing

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

Inhaltsverzeichnis

Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter

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Chapter 1. COVID-19: A Trigger for Innovations in Insurance?

Abstract

This chapter gives an overview of the consequences of the novel coronavirus, COVID-19 on the insurance branch. The main problems caused by the pandemic on the commercial insurance, and in particular, on the business interruption and possible innovations are discussed. The aim is to prepare the reader for the following chapters specifically by demonstrating connections between different aspects of modelling a pandemic. These models are necessary to create new insurance products supplementing governments’ actions in response to a pandemic.

María del Carmen Boado-Penas, Julia Eisenberg, Şule Şahin

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Chapter 2. Epidemic Compartmental Models and Their Insurance Applications

Abstract

Our society’s efforts to fight pandemics rely heavily on our ability to understand, model and predict the transmission dynamics of infectious diseases. Compartmental models are among the most commonly used mathematical tools to explain reported infections and deaths. This chapter offers a brief overview of basic compartmental models as well as several actuarial applications, ranging from product design and reserving of epidemic insurance, to the projection of healthcare demand and the allocation of scarce resources. The intent is to bridge classical epidemiological models with actuarial and financial applications that provide healthcare coverage and utilise limited healthcare resources during pandemics.

Runhuan Feng, José Garrido, Longhao Jin, Sooie-Hoe Loke, Linfeng Zhang

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Chapter 3. Some Investigations with a Simple Actuarial Model for Infections Such as COVID-19

Abstract

In this chapter the author adds an infection feature to an actuarial multiple state model to give a simple model for an infection such as COVID-19. The model is simple enough to be replicated in an Excel worksheet, with one row per day of calculations. The whole population is treated as homogenous, with no distinction by age, sex or anything else; to that extent it is unrealistic, but to include these features would complicate it considerably. To fit it to observed data requires successive optimisation by programme, and this is described. Different variations of the model allow it to fit better and take account of, for example, immunisation by vaccine. It is shown to fit the past events in the United Kingdom (U.K.) quite well, and it has also been fitted to other countries, but this is not shown in this chapter. It is also observed that this, or any other model, is of less use for forecasting the future, because it cannot predict the behaviours of governments or of populations. But various assumptions can be made about the future, as at the latest date of calculation (1 March 2021), and interesting consequences are shown.

A. D. Wilkie

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Chapter 4. Stochastic Mortality Models and Pandemic Shocks

Abstract

After decades of worldwide steady improvements in life expectancy, the COVID-19 pandemic produced a shock that had an extraordinary immediate impact on mortality rates globally. This shock had largely heterogeneous effects across cohorts, socio-economic groups, and nations. It represents a remarkable departure from the secular trends that most of the mortality models have been constructed to capture. Thus, this chapter aims to review the existing literature on stochastic mortality, discussing the features that these models should have in order to be able to incorporate the behaviour of mortality rates following shocks such as the one produced by the COVID-19 pandemic. Multi-population models are needed to describe the heterogeneous impact of pandemic shocks across cohorts of individuals. However, very few of them so far have included jumps. We contribute to the literature by describing a general framework for multi-population models with jumps in continuous-time, using affine jump-diffusive processes.

Luca Regis, Petar Jevtić

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Chapter 5. A Mortality Model for Pandemics and Other Contagion Events

Abstract

The crisis caused by COVID-19 has had various impacts on the mortality of different sexes, age groups, ethnic and socio-economic backgrounds and requires improved mortality models. Here a very simple model extension is proposed: add a proportional jump to mortality rates that is a constant percent increase across the ages and cohorts but which varies by year. Thus all groups are affected, but the higher-mortality groups get the biggest increases in number dying. Every year gets a jump factor, but these can be vanishingly small for the normal years. Statistical analysis reveals that even before considering pandemic effects, mortality models are often missing systemic risk elements which could capture unusual or even extreme population events. Adding a provision for annual jumps, stochastically dispersed enough to include both tiny and pandemic risks, improves the results and incorporates the systemic risk in projection distributions. Here the mortality curves across the age, cohort, and time parameters are fitted using regularised smoothing splines, and cross-validation criteria are used for fit quality. In this way, we get more parsimonious models with better predictive properties. Performance of the proposed model is compared to standard mortality models existing in the literature.

Gary Venter

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Chapter 6. Risk-Sharing and Contingent Premia in the Presence of Systematic Risk: The Case Study of the UK COVID-19 Economic Losses

Abstract

Motivated by macroeconomic risks, such as the COVID-19 pandemic, we consider different risk management setups and study efficient insurance schemes in the presence of low probability shock events that trigger losses for all participants. More precisely, we consider three platforms: the risk-sharing, insurance and market platform. First, we show that under a non-discriminatory insurance assumption, it is optimal for everybody to equally share all risk in the market. This gives rise to a new concept of a contingent premium which collects the premia ex-post after the losses are realised. Insurance is then a mechanism to redistribute wealth, and we call this a risk-sharing solution. Second, we show that in an insurance platform, where the insurance is regulated, the tail events are not shared, but borne by the government. Third, in a competitive market we see how a classical solution can raise the risk of insolvency. Moreover, in a decentralised market, the equilibrium cannot be reached if there is adequate sensitivity to the common shock events. In addition, we have applied our theory to a case where the losses are calibrated based on the UK Coronavirus Job Retention Scheme.

Hirbod Assa, Tim J. Boonen

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Chapter 7. All-Hands-On-Deck!—How International Organisations Respond to the COVID-19 Pandemic

Abstract

The COVID-19 pandemic is affecting all countries. Since the World Health Organization declared the COVID-19 outbreak a Public Health Emergency of International Concern on 30 January 2021, governments across the world have mobilised on a tremendous scale and put in place different policies to contain the spread of the virus and its negative effects on society. International organisations have supported these efforts through evidence-based policy recommendations and emergency financing packages. This chapter presents a brief overview of the responses made by international organisations and European Union towards COVID-19. Special attention is given to the guidance of these organisations on the changes in social insurance and pension plans to protect the most vulnerable population groups.

María del Carmen Boado-Penas, Gustavo Demarco, Julia Eisenberg, Kristoffer Lundberg, Şule Şahin

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Chapter 8. Changes in Behaviour Induced by COVID-19: Obedience to the Introduced Measures

Abstract

The pandemic of COVID-19 that has plagued our planet since the beginning of 2020, has disrupted the way of life of society in general. As in other pandemics suffered throughout history, isolation has been a crucial measure to avoid contagion, causing effects beyond health, in many areas of life. How society obtains economic resources, spends them, enjoys leisure, or simply interacts, is now different. The political and economic context has changed, freedom of movements and expectations are also different. All this generates changes in the behaviour of society that does not react uniformly in all countries. This chapter reviews some of the modifications in behaviour caused by the present circumstances, as what will happen in future pandemics is not predictable for sure. The emphasis is placed on obedience observed in different contexts to imposed restrictions. Homes have become workplaces, consumption patterns have changed, and the derived effects are not always beneficial or distributed equally across the social strata.

Nuria Badenes-Plá

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Chapter 9. COVID-19 and Optimal Lockdown Strategies: The Effect of New and More Virulent Strains

Abstract

Most nations have responded to the COVID-19 pandemic by locking down parts of their economies starting in early 2020 to reduce the infectious spread. The optimal timing of the beginning and end of the lockdown, together with its intensity, is determined by the tradeoff between economic losses and improved health outcomes. These choices can be modelled within the framework of an optimal control model that recognises the nonlinear dynamics of epidemic spread and the increased risks when infection rates surge beyond the healthcare system’s capacity. Past work has shown that within such a framework very different strategies may be optimal ranging from short to long and even multiple lockdowns, and small changes in the valuation on preventing a premature death may lead to quite different strategies becoming optimal. There even exist parameter constellations for which two or more very different strategies can be optimal. Here we revisit those crucial questions with revised parameters reflecting the greater infectivity of variants such as the “UK variant” of the SARS-CoV-2 virus and describe how the new variant may affect levels of mortality and other outcomes.

Jonathan P. Caulkins, Dieter Grass, Gustav Feichtinger, Richard F. Hartl, Peter M. Kort, Alexia Prskawetz, Andrea Seidl, Stefan Wrzaczek

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Chapter 10. Diagnostic Tests and Procedures During the COVID-19 Pandemic

Abstract

Coronavirus disease 2019 (COVID-19) has brought a huge impact on global health and the economy. Early and accurate diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections is essential for clinical intervention and pandemic control. This book chapter addresses the evolving approach to the laboratory diagnosis of COVID-19 covering preanalytical, analytical, and postanalytical steps. The rapidly changing dynamics of the COVID-19 pandemic serve as an example which will be important for laboratories to plan for future pandemics. With the quick identification of the causative pathogen and availability of the genome sequence, it will be possible to develop and implement diagnostic tests within weeks of an outbreak. Laboratories will need to be flexible to continuously adapt to changing testing needs and burdens on the healthcare system, plan mitigation strategies for bottlenecks in testing and workflow due to limitations on resources and supplies, and prepare back-up plans now in order to be better prepared for future pandemics.

Sherry A. Dunbar, Yi-Wei Tang

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Chapter 11. Pooled Testing and Its Applications in the COVID-19 Pandemic

Abstract

When testing for a disease such as COVID-19, the standard method is individual testing: we take a sample from each individual and test these samples separately. An alternative is pooled testing (or ‘group testing’), where samples are mixed together in different pools, and those pooled samples are tested. When the prevalence of the disease is low and the accuracy of the test is fairly high, pooled testing strategies can be more efficient than individual testing. In this chapter, we discuss the mathematics of pooled testing and its uses during pandemics, in particular the COVID-19 pandemic. We analyse some one- and two-stage pooling strategies under perfect and imperfect tests, and consider the practical issues in the application of such protocols.

Matthew Aldridge, David Ellis

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Chapter 12. Outlier Detection for Pandemic-Related Data Using Compositional Functional Data Analysis

Abstract

With accurate data, governments can make the most informed decisions to keep people safer through pandemics such as the COVID-19 coronavirus. In such events, data reliability is crucial and therefore outlier detection is an important and even unavoidable issue. Outliers are often considered as the most interesting observations, because the fact that they differ from the data majority may lead to relevant findings in the subject area. Outlier detection has also been addressed in the context of multivariate functional data, thus smooth functions of several characteristics, often derived from measurements at different time points (Hubert et al. in Stat Methods Appl 24(2):177–202, 2015b). Here the underlying data are regarded as compositions, with the compositional parts forming the multivariate information, and thus only relative information in terms of log-ratios between these parts is considered as relevant for the analysis. The multivariate functional data thus have to be derived as smooth functions by utilising this relative information. Subsequently, already established multivariate functional outlier detection procedures can be used, but for interpretation purposes, the functional data need to be presented in an appropriate space. The methodology is illustrated with publicly available data around the COVID-19 pandemic to find countries displaying outlying trends.

Christopher Rieser, Peter Filzmoser

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Chapter 13. The Legal Challenges of Insuring Against a Pandemic

Abstract

COVID-19 has raised, and continues to raise, questions about the traditional approach to insurance cover. For instance, business interruption insurance covering “pandemics” under all risks insurance policies are likely to be a thing of the past. With tensions between businesses and the insurance industry on the rise, what can be done to offer businesses some protection at a premium they can afford, without emptying insurers’ reserves? In this chapter we talk about legal challenges related to traditional insurance against the risk of losses caused by a pandemic, and whether parametric insurance is the solution.

Rachel Hillier

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Chapter 14. An Actuary’s Opinion: How to Get Through a Pandemic

Abstract

We discuss in this chapter how the insights and methods presented in the previous chapters can be effectively and practically implemented to manage and mitigate pandemics. The findings are not only analysed for the current COVID-19 crisis, but we also present some insights that could be gained for future pandemics and other extreme events. Coming from an actuarial background, the main focus lies on practical and technical aspects of the presented articles, namely on data, models and possible risk mitigation through (re)insurance, capital markets and similar approaches.

Frank Schiller

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Titel: Pandemics: Insurance and Social Protection
herausgegeben von: María del Carmen Boado-Penas
Julia Eisenberg
Şule Şahin‬‬‬
Verlag: Springer International Publishing
Electronic ISBN: 978-3-030-78334-1
Print ISBN: 978-3-030-78333-4
DOI: https://doi.org/10.1007/978-3-030-78334-1