Risk Management

Executive Summary: Best practices that have evolved around the use of natural catastrophe models have value for managing future pandemics, writes risk modeling expert Karen Clark. Among them is the use of probability distributions around important model outputs—fatalities and hospitalizations in the case of pandemics—reflecting the uncertainty around model assumptions such as transmission and fatality rates. Continuous model revisions to incorporate new data and the use of random data samples are other takeaways from hurricane and earthquake modeling that would be useful for pandemics.

By Karen Clark

COVID-19 isn’t the first and won’t be the last pandemic to threaten the well-being of the global population, but it will likely turn

out to be the worst in the last 100 years. It would be hard to argue that the handling of this crisis has been ideal, and there is much to be learned and improved upon before the next virus strikes. Policy responses should be based on the science and the data, but because there is so much uncertainty around the data in the early phases of a pandemic, and in particular the data used in the pandemic models, a more

robust framework for leveraging the science is called for. As the insurance industry knows very well, despite wide inherent uncertainty, robust models used correctly can be valuable tools for decision-makers. Insurers also know that models can produce widely divergent results, so it’s imperative to understand the model assumptions, the variability around those assumptions and what’s ultimately driving the

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Karen Clark, the President

of Karen Clark & Company,

is internationally

recognized as the founder

of the first catastrophe

modeling firm and as the

expert in the field of

catastrophe risk

assessment and

management. She may be

reached at info90410@

karenclarkandco.com.

Managing a Pandemic:

Are There Lessons From Catastrophe Modeling?

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Risk Management

model output. Best practices that have evolved around the use of catastrophe models have value for managing future pandemics.

Probability Distributions vs. Deterministic Scenarios The catastrophe models are complex and include many random variables represented by statistical distributions that are used to generate a large sample of potential outcomes. The primary output of the catastrophe models is a complete probability distribution providing insurers with the full range of losses and the probabilities of exceeding losses of different sizes. Rather than deterministic “best” and “worst” case scenarios, it would be useful for policymakers to have more complete distributions of the primary pandemic model output—the likely number of fatalities—under different scenarios. Like catastrophe models, the pandemic models include random variables that must be characterized using incomplete and imperfect data combined with scientific expertise and assumptions. One of the most important variables is the transmission rate, R0, which is based on assumptions around the rate at which an infectious person infects a susceptible person and how long a person remains infectious. If, for example, an infectious

person infects one susceptible person every five days and is infectious for 11 days, the R0 is 2.2. Scientists first used what is known about previous virus outbreaks along with early data from Wuhan, China, to estimate the R0 for COVID-19. Expected values were generally in the range of 2.2 to 2.4, but given the small sample size, the 90 percent confidence interval was estimated to be 1.4 to 3.8. This is a wide range for such an important variable; a lower R0 indicates less mitigation is necessary to control the virus, and a higher R0 suggests extreme measures are in order. Along with the transmission rate, the severity of the virus in terms of how many infected people will become sick enough to require hospitalization is estimated by the models. The objective of social distancing is to slow the spread of the virus so that medical facilities don’t become overwhelmed, and medical staff are able to treat all of those who are seriously ill—i.e., to “flatten the curve.” Columbia University predicted in late March that New York City alone would require 136,000 hospital beds when the virus peaked. The University of Washington model predicted 73,000 for New York state. When COVID-19 peaked in New York on April 12, the highest daily number of hospitalizations statewide stood at 18,825.

COVID-19 has shown that in the early phases of an outbreak there are many unknowns and a high degree of uncertainty around the pandemic model variables. The probability distributions of likely hospitalizations and deaths start out wide and flat, and point estimate predictions from the models will almost certainly be wrong. What would be more useful than point estimates is the ability to test, against the model assumptions, various mitigation steps such as school closures, business closures, home quarantine and social distancing measures along with combinations of these measures to see how the probability distribution of fatalities changes. This would give policymakers a more informative and transparent framework for decision-making.

Incorporating New Data as It Develops Catastrophe models typically are updated every few years. Major landfalling

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What would be more useful

than point estimates is the

ability to test, against the

model assumptions, various

mitigation steps such as

school closures, business

closures, home quarantine

and social distancing

measures along with

combinations of these

measures to see how the

probability distribution of

fatalities changes.

www.carriermanagement.com JULY/AUGUST 2020 | 11

hurricanes and earthquakes are relatively rare, but when they do occur, they produce a lot of valuable data that is then used to fine-tune and improve the model assumptions. During an outbreak, the pandemic model assumptions can be updated continuously with new information. As the virus spreads, data on cases, hospitalizations and deaths are collected and will shed more light on the various unknowns to give a clearer picture of how the virus will develop. For example, by early March, the data were clearly showing that COVID-19 was not an indiscriminate killer but was specifically targeting the elderly. Early estimates of the infection fatality rate (IFR)—specifying how many people who become infected will die—were around 1 percent for COVID-19, 10-times higher than normal influenza. This assumption drives the projections of deaths from the models. As data were collected, it became clear just how skewed the IFR was and that those aged 80 and above were an order of magnitude more likely to die from COVID-19 than people under 60. At the same time, the data showed mounting deaths in nursing homes and other long-term care facilities. In some states, these deaths account for over 50 percent of the total. Scientists also noted that children seemed to be less infectious than adults. Health care workers seemed to dominate the fatalities in the younger age groups, suggesting that not just being exposed but the amount of exposure to COVID-19 was an important determinant of severity. In other words, emerging data and analyses suggested the demographically weighted IFR could be significantly less than 1 percent. Evolving information can be used to fine-tune the model assumptions and to guide policy responses to the pandemic. Are the mitigation steps being targeted to the most vulnerable populations? Are the right policies in place to have the maximum impact on reducing fatalities?

Mitigation Strategies Tailored to the Hazard Natural catastrophes don’t impact everywhere equally. Earthquakes are much more likely in California than New York, and hurricanes are more likely to make landfall in the Gulf than the Northeast. Local building codes and construction practices reflect these differences in hazard. Similarly, COVID-19 has not impacted all areas equally. Along with varying by age and other exposure factors, the transmission and death rates appear to be strikingly different by country, city and region. Factors influencing these variables could include population density, use of public transportation, intercity travel and climate. Mitigation strategies tailored to specific locations may be more effective and sustainable over time than broad-brush approaches. Rather than lockdown or no lockdown, different levels of mitigation may be more appropriate to control the virus while helping to alleviate the other

burdens imposed by the outbreak. Death rates have not been highly correlated with the length or severity of the lockdowns, as shown in the accompanying chart. This doesn’t mean that lockdowns don’t have an impact, but perhaps only certain places require complete social lockdowns to control the transmission of the virus. For example, sparsely populated states, such as Montana, never imposed a complete lockdown, and the state has one of the lowest death rates per capita.

Beneficial Use of Random Sampling The pandemic models use the IFR to estimate the likely deaths from an outbreak, and this assumption is critical for testing the effectiveness of mitigation strategies and informing policy decisions. But the IFR cannot be estimated accurately when the total number of infected people is unknown. Based on different scientific studies, the IFR could be as low as 0.2 or as high as 1.6 percent. A shortage of supplies has meant that

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only people showing severe symptoms of COVID-19 are tested, and wide-scale testing has not been possible. Scientists know the confirmed number of COVID-19 cases; they don’t know the actual number of cases, which is likely much higher. As the actual cases rise, the IFR falls, given the number of deaths. Random sampling offers a viable alternative to wide-scale testing. With just a few thousand tests and well-constructed samples, it’s possible to narrow the range of estimates for the IFR. Random sampling is a standard statistical technique. Catastrophe models use the historical data over relatively short time periods (typically about 100 years) as the sample from which to extrapolate thousands of years of potential events and the true long run distribution (exceedance probability curve) of losses. As in other sampling processes, such as political polling, a well-constructed random sample

can usually produce a relatively accurate prediction of the outcome. Stanford University and the University of Southern California did conduct such studies to determine the number of people who had already been infected with COVID-19, and both studies found that the actual cases in certain California counties were likely 50 times greater than the confirmed cases. By using samples representing a cross section of the population, scientists could determine that the true IFR is likely near the low end of the range.

The Way Forward Every virus has its own unique personality: No two are exactly alike, and much is unknown at the start of an outbreak. Scientists must work with scarce data and past knowledge to infer values for the pandemic model variables. Projections

from the models can be sharpened as valuable data are collected and used to refine the model assumptions. Before the next pandemic, the models that will drive many governmental policy responses should be fully vetted by multidisciplinary teams, stress tested and well understood by the policymakers who will use them in emergency situations. Policymakers must understand the uncertainty in the models and how that uncertainty changes over time and impacts the effectiveness of their decisions. The COVID-19 crisis has been fraught with heated debates over lockdowns versus no lockdowns, when to start and when to end lockdowns, and whether it was better to under- or overreact. Instead of taking polarized positions, it seems wiser to respond appropriately to the next crisis by establishing a better risk management framework around the tools and models that can best inform policy decisions.

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