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

Author

Listed:
  • Camila Pareja Yale

    (Production Engineering Department, Polytechnic School, University of São Paulo—USP, São Paulo 05508-010, Brazil
    These authors contributed equally to this work.)

  • Hugo Tsugunobu Yoshida Yoshizaki

    (Production Engineering Department, Polytechnic School, University of São Paulo—USP, São Paulo 05508-010, Brazil
    These authors contributed equally to this work.)

  • Luiz Paulo Fávero

    (Accounting Department, School of Economics, Business and Accounting, University of São Paulo—USP, São Paulo 05508-010, Brazil
    These authors contributed equally to this work.)

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.

Suggested Citation

  • Camila Pareja Yale & Hugo Tsugunobu Yoshida Yoshizaki & Luiz Paulo Fávero, 2022. "A New Zero-Inflated Negative Binomial Multilevel Model for Forecasting the Demand of Disaster Relief Supplies in the State of Sao Paulo, Brazil," Mathematics, MDPI, vol. 10(22), pages 1-11, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:22:p:4352-:d:978039
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    References listed on IDEAS

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    1. Xiaoxin Zhu & Guanghai Zhang & Baiqing Sun, 2019. "A comprehensive literature review of the demand forecasting methods of emergency resources from the perspective of artificial intelligence," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 97(1), pages 65-82, May.
    2. Mullahy, John, 1986. "Specification and testing of some modified count data models," Journal of Econometrics, Elsevier, vol. 33(3), pages 341-365, December.
    3. Luiz Paulo Fávero & Joseph F. Hair & Rafael de Freitas Souza & Matheus Albergaria & Talles V. Brugni, 2021. "Zero-Inflated Generalized Linear Mixed Models: A Better Way to Understand Data Relationships," Mathematics, MDPI, vol. 9(10), pages 1-28, May.
    4. Cindy Xin Feng, 2021. "A comparison of zero-inflated and hurdle models for modeling zero-inflated count data," Journal of Statistical Distributions and Applications, Springer, vol. 8(1), pages 1-19, December.
    5. Daniel B. Hall, 2000. "Zero-Inflated Poisson and Binomial Regression with Random Effects: A Case Study," Biometrics, The International Biometric Society, vol. 56(4), pages 1030-1039, December.
    6. Davis, Lauren B. & Jiang, Steven X. & Morgan, Shona D. & Nuamah, Isaac A. & Terry, Jessica R., 2016. "Analysis and prediction of food donation behavior for a domestic hunger relief organization," International Journal of Production Economics, Elsevier, vol. 182(C), pages 26-37.
    7. Mude, Andrew G. & Barrett, Christopher B. & McPeak, John G. & Kaitho, Robert & Kristjanson, Patti, 2009. "Empirical forecasting of slow-onset disasters for improved emergency response: An application to Kenya's arid north," Food Policy, Elsevier, vol. 34(4), pages 329-339, August.
    8. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    9. Huang Xing & Zhou Zhonglin & Wang Shaoyu, 2015. "The prediction model of earthquake casuailty based on robust wavelet v-SVM," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 77(2), pages 717-732, June.
    10. Bruce A. Desmarais & Jeffrey J. Harden, 2013. "Testing for zero inflation in count models: Bias correction for the Vuong test," Stata Journal, StataCorp LP, vol. 13(4), pages 810-835, December.
    11. Cameron, A. Colin & Trivedi, Pravin K., 1990. "Regression-based tests for overdispersion in the Poisson model," Journal of Econometrics, Elsevier, vol. 46(3), pages 347-364, December.
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