I analyze damage from hurricane strikes on the United States since 1955. Using machine learning methods to select the most important drivers for damage, I show that large errors in a hurricane's predicted landfall location result in higher damage. This relationship holds across a wide range of...

Background: This paper proposes a framework to cope with the lack of data at the time of a disaster by employing predictive models. The framework can be used for disaster human impact assessment based on the socio-economic characteristics of the affected countries. Methods: A panel data of 4252...

Large-scale disasters regularly affect societies over the globe, causing large destruction and damage. After each of these events, media, insurance companies, and international institutions publish numerous assessments of the “cost of the disaster.” However these assessments are based on...

Large-scale disasters regularly affect societies over the globe, causing large destruction and damage. After each of these events, media, insurance companies, and international institu-tions publish numerous assessments of the "cost of the disaster." However these assessments are based on...

Large-scale disasters regularly affect societies over the globe, causing large destruction and damage. After each of these events, media, insurance companies, and international institu-tions publish numerous assessments of the "cost of the disaster." However these assessments are based on...