session 101 panel discussion pandemic modeling for group ... · russian flu (h1n1)* 11. 2009-2010....

Session 101 PD, Pandemic Modeling for Group Life Insurance

Presenters: Timothy B. Moran ASA, MAAA

Kimiko Tan

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Pandemic Modeling in Group Life Insurance

Tim Moran2nd VP and Group ActuaryMunich Re

Agenda

1. Background and History2. Risk Management for Group Life Insurers3. Pandemic Models4. Results

13 June 2017 2© 2017 Munich American Reassurance Company. All Rights Reserved.

Agenda



Pandemics: Understanding the Risk


What can we predict about the next pandemic event?


Agenda



Group Life Insurance Risk Management Objectives

Quantify economic consequences

Evaluate options for risk mitigation

Plan for disruptions to financial markets

Consider a range of outcomes, varying frequency and severity


Capital Planning & Solvency

Minimize operational disruptions

Financial operations may be limited

Consider a range of outcomes, varying frequency and severity

Business Continuity Planning

Pandemics present a material risk from both a financial and operational view

Lack of planning and modeling can lead to incomplete/poor risk management strategy

Group Life Insurance Pandemic Strategies

Strategy Pro Con

Limit geographic exposure/concentration

Can be effective if mortality remains localized.

If pandemic spreads globally, will not provide adequate protection.

Hedge with longevity products Negative covariance with no reduction in block size or profits in favorable scenarios

Hedge may prove ineffective.

Quota Share or Excess Reinsurance

Effective way to limit losses for particular block of policies.

Does not fully immunize carrier from incidence spikes.

Tail stop-loss reinsurance/capital relief reinsurance

Relatively inexpensive way to protect capital.

Temporary surplus protection. Losses generally are paid back.

Mortality swaps Remove uncertainty about payments. Increase capacity to sell new business.

Complex and difficult to implement. Need to find a willing partner.

Sell mortality cat bonds Transfer extreme risks to the capital markets; access to deep capital pools.

May introduce basis risk if trigger is amortality index.

Purchase pandemic insurance coverage

Explicitly protects against defined mortality spike.

May not be available when needed. Difficult to pass costs on to customers.


Agenda



Definitions and Modeling Considerations


A pandemic is a widespread infectious disease.

A disease or condition is not a pandemic merely because it kills many people; it must also be infectious.

Two broad categories are influenza and “other infectious diseases”.

Influenza•Seasonal•Epidemic and pandemic

Other Infectious Diseases•Bacterial•Vector-Borne

Model Overview

Apply excess mortality probabilities to a specific block of business by age and gender to determine frequency (e.g. “return periods”) and severity


Create assumption sets for each modeled pathogen (virulence, transmissibility) and model input parameter (vaccines, pharmaceutical and non-pharmaceutical interventions.

Assign probabilities to each input parameter.

Develop a set of events by combining inputs in SIR model to calculate population mortality rates for a given combination of inputs.

Use stochastic approach to produce distribution of mortality rates

1

2

3

4

5

Input

Output

Frequency – Influenza Pandemic


Historical Influenza PandemicsYear(s) Name R0 Virulence

1 1729-1733 high high2 1761-17623 1780-1782 very high very low4 1788-1790 very high low5 1830-1837 very high low6 1889-1893 Russian Flu 2.1 0.1-0.28%7 1918-1919 Spanish Flu (H1N1) 1.5-5 >2%8 1957-1958 Asian Flu (H2N2) 1.5-1.7 0.13%9 1968-1969 Hong Kong Flu (H3N2) 1.5-2.2

Frequency – Infectious Disease Pandemic


Historical Infectious Disease Pandemics and EpidemicsYear(s) Name Disease or pathogen

1 1629-1631 Italian plague/Great Plague of Milan Bubonic plague2 1665-1666 Great plague of London Bubonic plague3 1679 Great plague of Vienna Bubonic plague4 1775-1782 North American smallpox Smallpox5 1793; 1690-1878 Yellow fever, U.S. Yellow fever6 1816-1824 First Cholera Pandemic Cholera7 1826-1837 Second Cholera Pandemic Cholera8 1846-1863 Third Cholera Pandemic Cholera9 1863-1875 Fourth Cholera Pandemic Cholera

10 1881-1896 Fifth Cholera Pandemic Cholera11 1899-1923 Sixth Cholera Pandemic Cholera

12 1962-1966 Seventh/El Tor Cholera Pandemic Cholera

13 1855-1959 Third Pandemic Bubonic plague

14 1918-1922 Russian Typhus Epidemic Typhus

15 1981-present HIV/AIDS HIV/AIDS16 2013-2016 West African Ebola Epidemic * Ebola

Expected pandemic frequency(per 100 years)

2.5 - 5.0 influenza 0.5 - 1.5 infectious disease

Based upon historical record

Basic Compartmental Model


Time series model with discrete states:

S(t) = number susceptible at time t I(t) = number infectious at time tR(t) = number recovered / immune at time t

SIR model aids in predicting the spread of pathogens in a given population and the duration of pandemic

% Dead =

Simulated Mortality using SIR

Time 0

Initial Outbreak …

…

Time 1 …

Time 2 …

…

…

Susceptible

Infected

Recovered/ Immune

Time “t”

13 June 2017© 2017 Munich American Reassurance Company. All Rights Reserved. 15

Factors impacting SIR state transition

Speed at which pandemic spreads and total number infected, measured by the

Initial Reproductive Number, R0

Ability of a disease to cause death, measured by case-fatality rate or

average deaths per case Excess mortality reduced for effective

and available vaccine

Pharmaceutical interventions include antibiotics, antivirals, anti-parasitic, etc.

NPIs attempt to slow introduction and spread of the disease. Include

quarantine, school and business closures, travel restrictions, etc.

Susceptibility of infection and recovery varies by age and other demographic

factors

Transmissibility Virulence Vaccine

Non-Pharm interventionsPharmaceutical interventions Demographics

13 June 2017© 2017 Munich American Reassurance Company. All Rights Reserved. 16

Transmissibility


R0 is the average number of secondary cases caused by one person in a population without immunity and without intervention.

R0 must be greater than 1 for an epidemic to occur.

The Initial Reproductive Number, “R0”, measures the transmissibility of an influenza-type disease.

IT is the proportion of the population that must be immunized or have prior immunity to stem infectious disease transmission

20% of historical disease pandemics have high transmissibility (>= 80)

Immunity Threshold (IT) parameter used to model transmissibility of non-influenza infectious diseases

Virulence

The virulence of a pathogen is measured in terms of its case-fatality rate (CFR) or average “deaths per case”

As with transmissibility, virulence is modeled separately for influenza and infectious disease.

CFR will vary depending on the population being studied


Vaccine, Pharmaceutical and Non-Pharm Intervention


The vaccine parameter in the model discounts excess mortality for effective and available vaccines and represents the proportion of the population with immunity to the pathogen.

Pharmaceuticals are the primary mechanisms for reducing mortality from infectious diseases.

Non-Pharmaceutical Interventions (NPIs) are measures that attempt to slow the introduction of disease and subsequent transmission throughout a population.

NPIs particularly important in the period between the emergence of an infectious disease and when a vaccine can be developed and made widely available.

Demography and Geography

The age & gender profile for a disease are critical in how it impacts various groups:


Demographic mortality profiles for influenza:

1) Seasonal – affects the youngest and oldest lives most (i.e. U-shaped)2) Pandemic – where there is a larger impact on working age lives 3) Residual immunity – where the impact is relatively flat, affecting all ages equally, due to

residual immunity in older ages

Demographic mortality distributions for infectious diseases:

1) Flat – where all ages will be affected by the disease equally2) Middle-aged – primarily affects the working-age population or otherwise healthy adults3) Young & old – primarily affects the younger and older individuals and those with weakened

immune systems

Example of Event Set Development


Description Transmissibility CFR VP Case PharmaceuticalDemographic

Case

Measles TI=95 0.01 No ImmunitySupportive

CareYoung & Old

H1N1 Influenza

R0=2 0.025 No ImmunitySupportive

Care/ResistanceResidual Immunity

Input: variables for two different types of infectious events into SIR model

Description LikelihoodTotal

Fatalities (US)

Measles 3.71 x 10-6 2,264,675

H1N1 Influenza

3.27 x 10-6 1,680,348

Output: mortality rates

Results From Similar Events


Event Description Vaccine Pharmaceutical LikelihoodTotal

Fatalities (US)

Flu909

H1N1 Influenza No immunitySupportive

Care/Resistance3.27x 10-6 1,680,348

Flu912

H1N1 Influenza No ImmunitySupportive Care/ No

Resistance2.62x 10-5 988,440

Flu 900

H1N1 Influenza 30%Supportive Care/ No

Resistance7.85x 10-5 790,752

Each event is a plausible combination of transmissibility, virulence, vaccine, pharmaceutical factors.

Agenda



Deterministic Scenarios


Study SeverityExcess

Mortality ‰Mckibbin-Sidorenko Mild 0.10US Congressional Budget Office Mild 0.34US Department of Health and Human Services Moderate 0.70Mckibbin-Sidorenko (Australia) Moderate 1.10Australian Treasury; ABARE Moderate 2.00Mckibbin-Sidorenko Severe 5.40US Department of Health and Human Services Severe 6.30US Congressional Budget Office Severe 7.50Mckibbin-Sidorenko Ultra 10.90

Various economic studies have quantified deterministic excess mortality scenarios.

“Return Period” translates the severity to frequency of pandemic events; e.g. Severe = 1 per 500 years & Moderate = 1 per 50 years

Impact of Deterministic Scenario on Insured Block

Simple Excel-based tool to model deterministic pandemic scenario against your insurance portfolio (available on SOA.org):

− Input “moderate” or “severe” pandemic− Set shape of excess mortality rate curve to “U”,

“W”, “V\”

− Set company data (either individual or group)− For group, determine insured population death

based on distribution of working ages and mortality % of general population


1918 Influenza mortality rate by age

Financial Impact From Moderate Pandemic


Annual Group Life Insurance Benefits Paid

ModeratePandemic

Average Group Life Mortality (per year)

2.0 per 1000

+ Pandemic Excess Mortality 2.0 per 1000

= Total Mortality 4.0 per 1000

Comparing Group Life Insurance Pandemic Loss to Recent Financial Industry Events


Corporation Year What happened Amount in 2008 $US

● Bear Stearns 2008JP Morgan purchased Bear Stearns for $236 million; the Federal Reserve provided a $30 billion credit line to ensure the sale could move forward. $30 billion

● Fannie Mae / Freddie Mac 2008On Sep. 7, 2008, Fannie and Freddie were essentially nationalized & placed under the conservatorship of the Federal Housing Finance Agency. The Treasury has invested billions to cover the companies' losses.

$400 billion

●American International Group (A.I.G.)

2008 On four separate occasions, the government has offered aid to AIG to keep it from collapsing issuing a credit line of $180 billion between the Treasury ($70 billion) and Fed ($110 billion). $180 billion

● Auto Industry 2008In late September 2008, Congress approved a spending bill which included a measure for $25 billion in loans to the auto industry. These low-interest loans are intended to aid the industry. $25 billion

● Troubled Asset Relief Program 2008In October 2008, Congress passed the Emergency Economic Stabilization Act, which authorized the Treasury Department to spend $700 billion to combat the financial crisis. Treasury has been doling out the money via an alphabet soup of different programs. $700 billion

● Citigroup 2008Citigroup received a $25 billion investment through the TARP in Oct 2017 and another $20 billion in Nov 2017. In addition to the Treasury's $5 billion commitment, the FDIC has committed $10 billion and the Federal Reserve up to about $220 billion. $280 billion

● Bank of America 2009Bank of America has received $45 billion through the TARP, which includes $10 billion originally meant for Merrill Lynch. In addition, the Treasury made a $7.5 billion commitment, the FDIC has committed $2.5 billion and the Federal Reserve up to $87.2 billion.

$142.2 billion

Source: Propublica.org

• Next pandemic is inevitable, but impossible to predict timing and severity.

• Group Life insurance industry has tools available to manage risks from pandemic events.

• Distribution of excess mortality from pandemic can be modeled and incorporated into enterprise risk Strategy.

• Model parameters set using known factors from historic pandemics and expert opinion.

• Key factors that impact transitions in SIR model are transmissibility, virulence, vaccine, interventions (pharmaceutical and non-pharmaceutical) and demographics.

• Deterministic modeling tools and scenarios are available and relatively simple.

Recap


Thank you!


Prepared by Aon BenfieldCatastrophe Management | Accident, Health and Life Team

Group Life – Catastrophic Perils

Kimiko TamAon Benfield

2Aon Benfield | Catastrophe Management | Accident, Health and Life TeamProprietary & Confidential

Agenda

Section 1 Peril OverviewSection 2 Modeling FrameworkSection 3 Challenges and SolutionsSection 4 Risk Management


Agenda



Total Terrorist Attacks by Region in 2016

Source: Aon’s 2017 Risk Maps for Political Risk, Terrorism and Political Violence


Terrorist Attacks in the US by Year

Source: Aon’s 2017 Risk Maps for Political Risk, Terrorism and Political Violence


US Earthquake Risk

Source: USGS


Agenda



Catastrophe Model Framework

PolicyLocation

PolicyTerms

Risk Characteristics

1Hazard or

Science Module

3Financial or Insurance Module

2Vulnerability Module


Modelling Data Requirements

Modeling

Location Data

Higher Accuracy Data

Exposure Data

Street Address, City, Zip Code, County, State, Country

Latitude and Longitude Can supplement data from other

sources

Construction Type (e.g. year built, number of stories)

Occupancy Code (e.g. type of industry and occupations in building)

Line of Business, Net Retained Sums Insured, Aggregate Limits, Payouts

At Work vs. At Home Age Bands (for Pandemic)


Agenda



Strategy and Challenges

Capture Good Location Data– Best data comes from the Policyholder– Can supplement from other sources

• Where are the people? Avention or Dun & Bradstreet (license required) – Has number of employees by branch

locations to help allocate exposure in cases where all employees were placed in the company corporate headquarters

Census and Government Databases – Can use publicly available databases to allocate exposures for spouses and dependents, association insureds, county or state employees and other quasi governmental entities

• Supplement Building Information Sanborn CitySets® database (license required) – Can supplement incomplete U.S. data with

detailed city-center databases of address and other building attributes. This includes information on all structures in the city centers of the largest U.S. cities.

Publicly Available Data -- New York City Open Database PLUTO (Primary Land Use Tax Lot Output) has extensive

land use geographic data at the tax lot level- OpenStreetMap is worldwide and includes roads, trails, cafes, railway stations, etc.- New York Department of Information Technology and Telecommunication (NYC DoITT) has

a 3-D model of every NYC building present in 2014

Gaps in data are pricedconservatively


Need to Know Where the Employees Are

3,320 Deaths 795 Deaths


Event Footprints

Pandemic FootprintEarth is ~ 57 million sq. mi

Earthquake10k sq. mi

TerrorismA couple of city blocks


Work vs Home – 60 Wall Street

Source: ImpactOnDemand


Work – Home Transform

Step 1 Step 2

Step 3 Step 4


Pre-Bind Monitoring on Underwriting DesktopDetermine Aggregation With Existing Book

Source: ImpactOnDemand

Potential New Account

Flexible Shape Drawing and Risk Isolation

Column Name: Sums InsuredShape Name: Canary Wharf

No. of Lives 7,821Min: $25,000Max: $2,500,000Avg: $175,845

Total: $1,375,283,745


NYC DoITT Visualization

Source: CESIUM (open source 3D rendering library)


Mapping the Shadows of New York City: Every Building

Source: https://www.nytimes.com/interactive/2016/12/21/upshot/Mapping-the-Shadows-of-New-York-City.html


February 9, 1996 Docklands Bombing

Pressure wave from 435kg TNT in 1996 Docklands Bombing

Source: Impact Forecasting


February 9, 1996 Docklands Bombing

Source: Mirrorpix


Agenda



Life Reinsurance Market

Risk management techniques, and potential applications to catastrophic risk:

In all cases, modeling is required to determine which risks to avoid, reduce, reinsure or retain!

• Not writing specific risks, perhaps due to capacity limits

Avoidance

• Writing reduced levels of benefits

Reduction

• Catastrophe reinsurance• Group or site specific quota share• Catastrophe bonds

Sharing

• Keep the good risks and remove the bad risks

Retention

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session 101 panel discussion pandemic modeling for group ... · russian flu (h1n1)* 11. 2009-2010....

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