Macroeconomic models for predicting indirect impacts of disasters: A review

Interdependencies between critical infrastructures and the economy amplify the effects of damage caused by disasters. The growing interest in impacts beyond physical damage has spurred a surge in literature on economic modeling methodologies for estimating indirect economic impacts of disasters. In this review, we propose a mathematical framework for categorizing modeling approaches that assess indirect economic impacts across natural hazards and anthropogenic disasters such as cyber attacks. We conduct a comparative analysis of macroeconomic models, focusing on the approaches capturing sectoral inter-dependencies. These include the Leontief Input-Output (I/O) model, the Inoperability Input-Output Model (IIM), the Dynamic Inoperability Input-Output Model (DIIM), the Adaptive Regional Input-Output (ARIO) model, and the Computable General Equilibrium (CGE) model and its extensions. We evaluate their applicability to disaster scenarios based on input data availability, the compatibility of model assumptions, and output capabilities. We also reveal the functional relationships of input data and output metrics across economic modeling approaches for inter-sectoral impacts. Furthermore, we examine how the damage mechanisms posed by different types of disasters translate into model inputs and impact modeling processes.