Author Archives: Robert Muir-Wood

About Robert Muir-Wood

Chief Research Officer, RMS

Robert Muir-Wood works to enhance approaches to natural catastrophe modeling, identify models for new areas of risk, and explore expanded applications for catastrophe modeling. Robert has more than 25 years of experience developing probabilistic catastrophe models. He was lead author for the 2007 IPCC Fourth Assessment Report and 2011 IPCC Special Report on Extremes, and is Chair of the OECD panel on the Financial Consequences of Large Scale Catastrophes.

He is the author of seven books, most recently: ‘The Cure for Catastrophe: How we can Stop Manufacturing Natural Disasters’. He has also written numerous research papers and articles in scientific and industry publications as well as frequent blogs. He holds a degree in natural sciences and a PhD both from Cambridge University and is a Visiting Professor at the Institute for Risk and Disaster Reduction at University College London.

Risk and COVID-19

Perhaps the most difficult and unfamiliar feature of the coronavirus pandemic is how the associated risk is rising rapidly through time. We are all used to managing our response to risks that are relatively stable, such as street crime or dangerous driving. The risk from the COVID-19 is different.

It is going to be with us for a while, and in many countries its rise looks exponential. This steep, geometric progression is completely new to most of us. However, those who have lived through hyperinflation in Zimbabwe or Venezuela know what rapid and out-of-control feels like. In several countries, rates of incidence and of mortality have been doubling every three days: imagine fighting an army that on day three has doubled in size and after a fortnight is thirty-two times bigger than on day one.

So how do we manage our own personal risks through this extraordinary period, and how should companies help make risk-based decisions on behalf of their employees?

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The Coronavirus Outbreak: Part Two – Self-Isolation and Quarantine

The village of Eyam in Derbyshire, central England, was unlucky to discover that the pandemic, then raging 150 miles (226 kilometers) to the south in London, had arrived on its doorstep.  

The pandemic was the plague – the year was 1665. The disease had reached Eyam through the delivery of flea-ridden cloth from London to the local tailor, who would then made clothes for the villagers. The fleas carried the plague bacterium and the recipient of the cloth was the first to die.

Within three months another 41 villagers had perished. By spring 1666 a newly appointed rector proposed that, for the sake of other plague-free towns in the Peak District region, the village should self-isolate. A local Earl offered to guarantee food for the town (supplied on a rock at the edge of the village, paid with coins immersed in vinegar – see location below). In June 1666 the villagers reluctantly agreed. Over the summer the plague returned with a vengeance and there were five or six deaths each day. Eventually one third of the population died. But the nearby towns stayed plague free.

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The Coronavirus Outbreak: Part One – Modeling “Spotting”

Since 2017, in modeling the threat from wildfire on communities in California, the significant new RMS innovation has been in capturing the process of “spotting” (i.e. identifying new outbreaks of fire far from the fire-front). Strong dry winds bring swarms of glowing embers from a raging wildland fire, which can travel long distances. Should these embers settle on shingle roofs, wooden patios or a leaf-filled plastic gutter, a fire will start. Unchecked, the fire will consume a house.

In high-density housing suburbs, wind-driven fire can spread from building to building and consume a whole neighborhood – as happened in the city of Santa Rosa in 2017. And the only way to stop an outbreak is to intervene: to extinguish each ember-ignited fire before it can spread.

Modeling ember ignitions requires sampling the speed and direction of the wind and also anticipating what proportion of fire-starts get extinguished before they can spread. Still it only takes one unchecked fire to burn down the town.

This same process, in modeling “spotting”, is key to anticipating the spread of the new coronavirus into western Europe and North America.  

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How Did the Global Risk Report Become Existential?

Mid-January saw the publication of the annual World Economic Forum (WEF) “Global Risks Report” timed to set the agenda during this week’s WEF Annual Meeting in Davos.

With each new edition – and this year’s edition is the fifteenth, inevitably, one first turns to the opening page of the report, to discover the Top Five Global Risks for 2020, in terms of their “likelihood” and “impact”. What has been trending and what has slipped down the chart?

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Risk Modeling for the Future

The World Economic Forum (WEF) has celebrated its fiftieth-year at its annual meeting in Davos. Increasingly the business/political nexus has become that articulated in WEF founder Klaus Schwab’s Davos Manifesto, that corporations “… must assume the role of a trustee of the material universe for future generations.”

In 2020, “Action on climate change” has now become the number one risk in terms of impact in the World Economic Forum’s Global Risk Report. The work at RMS on quantifying risk and exploring how risk is expected to shift under climate change has never been more important or timely.

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The Storm Surge and the Tsunami

The core idea behind catastrophe modeling is that the architecture of risk quantification is the same whatever the peril. While a hurricane is not an earthquake, building a hurricane catastrophe model has elements in common with an earthquake catastrophe model. Stochastic event occurrence, the hazard footprint, the damage mechanism, clustering, post-event loss amplification are all shared concepts.

While on the university campus, disciplines may retain their nineteenth century segregations, in catastrophe modeling we are “ecumenical” about what is the driver of loss: whether it is wind, hail, vibration, flood, cyber, a virus or a terrorist attack. The track of a hurricane, the track of a fault rupture: the contagion of influenza, the contagion of NotPetya malware: the topographic controls of flooding, the topographic controls of wildfire. Exploring the parallels can be illuminating.

Which is why it is interesting to discover historical figures, who like catastrophe modelers, have looked sideways across the catastrophe disciplines. One such figure is the Anglo-Greek Lafcadio Hearn (unless you are from Japan where he is known as Koizumi Yakumo.)

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How to Deliver Sea Level Data

Global sea levels are rising. After two thousand years of stability, the transition to continuous coastal change will be jarring (although this is what our shoreline ancestors experienced more than 6,000 years ago). By the end of this century, millions of people will need to relocate. An estimated two trillion dollars of assets lies within the first meter above extreme high tide.  

Future sea levels” is one of seven “Grand Challenges” of the World Climate Research Programme (WCRP). Through the week of November 11, leading experts from around the world met in Orléans at the headquarters of the Bureau de Recherches Géologiques et Minières (BRGM), the French geological survey.

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Venice in Peril

The first time I noticed the coincidence I assumed there had been a mistake. The most-costly flooding in modern Italian history inundated the city of Florence on November 4, 1966. The Arno river burst its banks and flooded the low-lying heart of the city, with six meters (19.6 feet) of water in some riverine streets. A hundred people died and three to four million priceless medieval books and manuscripts along with precious artworks stored in basements, were damaged and destroyed. It was the worst flood in the city for at least 400 years.

Flood marker on a Venice street. Image credit: Wikimedia

But then the highest measured storm surge flood “acqua alta” in Venice, reached 1.94 meters above the sea level datum, on the same day in November 1966: November 4.   

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Crossing the Terrorism Casualty Protection Gap

This blog was first published as an article in Insurance Day

The victims of the Las Vegas shooter Stephen Paddock, the injured and the dependents of the 57 who died have one comfort following their tragic predicament. Their vicious and indiscriminate attacker (whose reported comments get the attack classified as “domestic terrorism”) chose to fire at the 20,000-plus crowd attending the Route 91 Harvest music festival from the 32nd floor of the Mandalay Hotel, part of the MGM chain with a US$735 million liability insurance coverage. As a result (reflecting in part the “moral hazard” of insurance limits), the victims will receive the distribution (after substantial lawyers’ fees) of a near-US$800 million settlement.

In the lead-up to the attack on October 1, 2017, Paddock had researched renting a high-rise condo in Las Vegas and also explored the crowd numbers on the beach at Santa Monica and considered other festivals to target in Boston and Chicago. If he had chosen to shoot from a residential building or clifftop, to the same effect, the only compensation the victims could have expected would have been the US$11 million raised in a public appeal after the shooting: equal to around US$200,000 for each of those who died. As it is, their compensation should work out 30 times more generous.

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The Year of the Kitten

Almost three months ago we passed a remarkable record in catastrophe loss.

And yet no one seems to want to celebrate it.

No banner headlines in the newspapers. No speeches at the Monte Carlo Reinsurance Rendezvous.

The first half of 2019 generated the lowest catastrophe insurance loss for more than a decade. The estimates come in at: US$15 billion (Munich Re), US$19 billion (Sigma), or US$20 billion (Aon). In straight dollar terms, independent of any adjustment for inflation or exposure, this is lower than any year since 2006.

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