A Bayesian Approach for Earthquake Impact Modelling

TL;DR

This paper introduces a Bayesian impact estimation method for earthquakes that provides spatial impact maps, quantifies uncertainty, handles multiple shocks, and updates predictions with new data, improving response planning.

Contribution

It develops a novel likelihood-free Bayesian framework using ABC-SMC for earthquake impact modeling, offering advantages over existing empirical methods.

Findings

Achieves comparable mortality prediction accuracy to leading tools

Provides spatial impact maps with uncertainty quantification

Handles multi-shock earthquake events and updates predictions

Abstract

Immediately following a disaster event, such as an earthquake, estimates of the damage extent play a key role in informing the coordination of response and recovery efforts. We develop a novel impact estimation tool that leverages a generalised Bayesian approach to generate earthquake impact estimates across three impact types: mortality, population displacement, and building damage. Inference is performed within a likelihood-free framework, and a scoring-rule-based posterior avoids information loss from non-sufficient summary statistics. We propose an adaptation of existing scoring-rule-based loss functions that accommodates the use of an approximate Bayesian computation sequential Monte Carlo (ABC-SMC) framework. The fitted model achieves results comparable to those of two leading impact estimation tools in the prediction of total mortality when tested on a set of held-out past events. The proposed method provides four advantages over existing empirical approaches: modelling produces a gridded spatial map of the estimated impact, predictions benefit from the Bayesian quantification and interpretation of uncertainty, there is direct handling of multi-shock earthquake events, and the use of a joint model between impact types allows predictions to be updated as impact observations become available.