A New Zero-Inflated Negative Binomial Multilevel Model for Forecasting the Demand of Disaster Relief Supplies in the State of Sao Paulo, Brazil

Abstract

This article presents the results of the implementation of a forecasting model, to predict the relief materials needed for assisting in decisions prior to natural disasters, thus filling a gap in the exploration of Generalized Linear Mixed Models (GLMM) in a humanitarian context. Demand information from the State of Sao Paulo, Brazil was used to develop the Zero Inflated Negative Binomial Multilevel (ZINBM) model, which gets to handle the excess of zeros in the count data and considers the nested structure of the data set. Strategies for selecting predictor variables were based on the understanding of the needs for relief supplies; consequently, they were derived from vulnerability indicators, demographic factors, and occurrences of climatic anomalies. The model presents coefficients that are statistically significant, and the results show the importance of considering the nested structure of the data and the zero-inflated nature of the outcome variable. To validate the fitness of the ZINBM model, it was compared against the Poisson, Negative Binomial (NB), Zero Inflated Poisson (ZIP), and Zero Inflated Negative Binomial (ZINB) models.