Estimating public demand following disasters through Bayesian-based information integration. (January 2022)

• Record Type: Journal Article
• Authors: Chen, Yudi; Ji, Wenying
• Is Part Of: International journal of disaster risk reduction. Volume 68(2022)
• Publication Date: 2022-01
• Subjects: Disaster response -- Bayesian inference -- Uncertainty modeling -- Social media -- COVID-19; Emergency management -- Periodicals; Risk management -- Periodicals; Disaster relief -- Periodicals; Hazard mitigation -- Periodicals; 363.34
• DOI: 10.1016/j.ijdrr.2021.102713
• Languages: English
• ISSNs: 2212-4209

Abstract

Abstract: Public demand estimation is essential to effective relief resource distribution following disasters. However, previous studies are incapable of deriving a reliable estimation mainly due to the complexity, dynamicity, and nonlinearity of public demand. This research proposes an innovative data-driven approach to estimate public demand by leveraging sample information, such as social media and surveys. Twitter-based demand percentage (TDP) is designed as the predictor of actual demand percentage, while survey-based demand percentage (SDP) is developed as the ground truth of actual demand percentage. Sampling bias of social media users is removed through a systematic process that comprises the prediction of social media user races/ethnicities and the aggregation of demand percentages. Sampling uncertainty of TDP and SDP is modeled through a Bayesian-based approach that integrates prior knowledge as well as new observations from social media and surveys. The relationship between TDP and SDP is learned through a polynomial model, which facilitates the estimation of future actual demand percentage. To illustrate the feasibility and applicability of the proposed approach, public demand for COVID-19 vaccines in the US is estimated. Results demonstrate that the TDP is a strong predictor of actual demand percentage. This research novelly takes the advantages of sample information—the near-real-time nature of social media and the high reliability of surveys—to achieve a reliable and rapid estimation of public demand following disasters.