Quantifying post-disaster business recovery through Bayesian methods. Issue 6 (27th April 2021)

• Record Type: Journal Article
• Authors: Aghababaei, Mohammad; Koliou, Maria; Watson, Maria; Xiao, Yu
• Is Part Of: Structure and infrastructure engineering. Volume 17:Issue 6(2021)
• Page Start: 838
• Page End: 856
• Publication Date: 2021-04-27
• Subjects: Bayesian linear regression -- business recovery -- hurricanes -- resilience -- risk analysis -- socioeconomic recovery; Structural analysis (Engineering) -- Periodicals; Structural engineering -- Periodicals; Buildings -- Performance -- Periodicals; 620.005
• DOI: 10.1080/15732479.2020.1777569
• Languages: English
• ISSNs: 1573-2479

Abstract

Abstract: Business recovery after a disaster plays an important role in the socioeconomic recovery of a community. This study focuses on the development of a probabilistic modelling approach for quantifying and predicting business recovery through Bayesian linear regression. The proposed modelling approach consists of three steps including data collection, development of model forms, and model selection through rigorous evaluation and elimination steps. Four attributes, namely business cease operation days, revenue recovery, customer retention, and employee retention, which describe the post-disaster recovery state of a business, are considered. One of the main contributions of this study is incorporating the interplay between household and businesses in a community in developing predictive business recovery models. Towards that direction, different methods to account for the effect of household recovery into the customer retention rate of a business are investigated and proposed. As an application, the proposed modelling approach is applied on the results of a longitudinal field study at the community of Lumberton, NC, which was heavily impacted by the 2016 Hurricane Matthew, focusing on business recovery. The predictive models proposed in this study may be further applicable in risk-based resilience assessment of communities following disastrous events.