LEARNING THE HARD WAY: DESIGNING BETTER RISK MODELS FOR THE NEXT PANDEMIC
by Simon Young, Snr Director, Climate and Reslience Hub, Willis Towers Watson It will take time and resources to build an infectious disease risk model – re/insurers will have to be more innovative in their pandemic coverage and exposure management. Natural catastrophe risk models have revolutionised the property/casualty re/insurance business over the past 30 years. They have allowed more efficient deployment of capital by providing a rigorous way of estimating potential losses, better quantifying the tail and increasing trust in the probabilities assigned to natural disaster events and the damage and losses they produce. All of these models have been developed from common foundational assumptions: an event happens and produces impacts on a known (although somewhat uncertain) exposure (property or other fixed asset), which has a known (although, again, somewhat uncertain) vulnerability to the consequences (hazard) of the originating event. Using an intricate mix of physics (through natural science and engineering lenses) and statistics, such models produce insurance loss estimates that are, generally, robust and defensible. As new systemic and non-natural risks have emerged, establishing the potential future loss range of perils, such as terrorism and cyber, has required the introduction of social science disciplines (and greater levels of uncertainty), but did not greatly disrupt the established logic of the cat model; the components and controls remained familiar. Not so infectious disease models. First introduced to the insurance sector to capture excess mortality
from global pandemics in the life insurance business, they began as a combination of stochastic elements of natural catastrophe models with a well-established form of epidemiological model, the Susceptible – Infectious – Recovered compartmental model (and its many and varied siblings). Unknowns From a traditional cat modelling perspective, there remained a lot of unknowns. For example, the two components of “hazard” – location and intensity – were both poorly understood, thanks to a very sparse and poorly documented experiential history, and only a rudimentary understanding of the zoonotic viruses that are the dominant cause of epidemics and pandemics. And the model architecture required was more Gaudí than Brutalism. There is no fixed exposure or vulnerability; both are dynamic and feed directly back into the model in its next time step. And exposure and vulnerability are not controlled by engineering equations, they are assumed impacts of political decisions and human behaviour, of travel webs and social networks. The Sars-CoV-2 virus has brought epidemiological modelling to our living rooms (many doubling as home offices). Previously obscure epidemiological modellers have become household names and the concepts of reproduction numbers, nonpharmaceutical control measures and even herd immunity have become all too familiar. Covid-19 is by far the best-documented pandemic ever, but even after many months of live information being available
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