Status
As of 30 July 2026, the model runs at a global scale, representing most sectors in most countries as a single firm each, with the shipping networks that link them now built in. It is being validated against historical disasters and already reproduces the economic effects of the 2011 Tohoku earthquake.
Next, the team will merge it with a finer model that captures Japan firm by firm, add further case studies, and work with early users to confirm its value in practice and refine the dashboard.
Disaster supply-chain shock modelling
Summary
When an earthquake halts a factory or a blockade strands a fleet of ships, the losses rarely stop at the point of impact — they cascade through suppliers and customers worldwide. Those knock-on effects are the hardest part to anticipate, and they shape the cost of pricing business-interruption cover, modelling credit risk, and planning for disruption.
In collaboration with Macrocosm, an Oxford spin-out, OXFIDA is building a simulation of the global economy that traces those cascades, disaster by disaster. It grew out of a working model of Japan's economy and now reaches worldwide, covering every country and industry sector and the shipping routes that connect them.
The problem
A modern supply chain can involve hundreds of millions of firms and billions of supplier–customer links, all interdependent. When a natural disaster strikes one point in that web, the disruption spreads in ways that are hard to foresee: a supplier stops, its customers run short, and the shock travels far from where it began. For an insurer pricing business-interruption cover, a bank sizing its capital reserves, or a manufacturer building supply-chain resilience, that uncertainty is expensive. Without a way to trace how a shock propagates, it is difficult to set fair premiums, reserve the right amount, or plan for the disruptions ahead.
Solution
The model treats the economy as millions of interacting firms and sectors that buy, sell, and hold inventory, rather than as a set of averages. A natural disaster enters as a shock to what those firms can produce, and the simulation plays out how the effect ripples through supplier networks and international shipping routes. What makes it distinctive is that it is causal: it is built from real trade and production data and the logic of how firms respond to shortages, not from past correlations.
A planned dashboard will let underwriters, credit-risk modellers, and supply-chain analysts test their own disaster scenarios and read off the financial impact.