MODELLING ZERO-INFLATED OVER DISPERSED DENGUE DATA VIA ZERO-INFLATED POISSON INVERSE GAUSSIAN REGRESSION MODEL: A CASE STUDY OF BANGLADESH
Bangladesh has been noted for experiencing some of the most susceptible dengue outbreaks in Asia; the country’s location, dense population, and changing environment all play major roles in this. Determining the correlation between meteorological conditions and case count is critical for predicting a...
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Zibeline International
2024-04-01
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| Series: | Acta Scientifica Malaysia |
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| Online Access: | https://actascientificamalaysia.com/archives/ASM/1asm2024/1asm2024-11-14.pdf |
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| author | Sukanta Chakraborty Soma Chowdhury Biswas |
| author_facet | Sukanta Chakraborty Soma Chowdhury Biswas |
| author_sort | Sukanta Chakraborty |
| collection | DOAJ |
| description | Bangladesh has been noted for experiencing some of the most susceptible dengue outbreaks in Asia; the country’s location, dense population, and changing environment all play major roles in this. Determining the correlation between meteorological conditions and case count is critical for predicting about the characteristics of the DENV outbreak. Certain widely used models, such as the Poisson regression model or the negative binomial regression model, are insufficient to adequately predict dengue fever since many of these datasets are of the over-dispersed, long-tail variety, and zero-inflated. In this study, the Zero-inflated negative binomial regression model is compared with the Zero-inflated Poisson inverse Gaussian regression model. Depending on AIC and BIC criteria Zero-inflated Poisson inverse-Gaussian regression model is proposed. Then Zero-inflated Poisson inverse Gaussian regression model is used to model the dataset containing confirmed positive cases of dengue fever and seven meteorological variables. The proposed model shows that all the meteorological variables are significantly associated with the confirmed positive cases of dengue fever. That’s why modeling a dengue-fever dataset with a Zero-inflated Poisson-inverse Gaussian regression model is suggested in this study. |
| format | Article |
| id | doaj-art-0336ba08aabe46988a408551026276e6 |
| institution | Kabale University |
| issn | 2521-5051 2521-506X |
| language | English |
| publishDate | 2024-04-01 |
| publisher | Zibeline International |
| record_format | Article |
| series | Acta Scientifica Malaysia |
| spelling | doaj-art-0336ba08aabe46988a408551026276e62025-02-06T02:47:39ZengZibeline InternationalActa Scientifica Malaysia2521-50512521-506X2024-04-0181111410.26480/asm.01.2024.11.14MODELLING ZERO-INFLATED OVER DISPERSED DENGUE DATA VIA ZERO-INFLATED POISSON INVERSE GAUSSIAN REGRESSION MODEL: A CASE STUDY OF BANGLADESHSukanta Chakraborty0Soma Chowdhury Biswas1Department of Statistics, University of ChittagongDepartment of Statistics, University of ChittagongBangladesh has been noted for experiencing some of the most susceptible dengue outbreaks in Asia; the country’s location, dense population, and changing environment all play major roles in this. Determining the correlation between meteorological conditions and case count is critical for predicting about the characteristics of the DENV outbreak. Certain widely used models, such as the Poisson regression model or the negative binomial regression model, are insufficient to adequately predict dengue fever since many of these datasets are of the over-dispersed, long-tail variety, and zero-inflated. In this study, the Zero-inflated negative binomial regression model is compared with the Zero-inflated Poisson inverse Gaussian regression model. Depending on AIC and BIC criteria Zero-inflated Poisson inverse-Gaussian regression model is proposed. Then Zero-inflated Poisson inverse Gaussian regression model is used to model the dataset containing confirmed positive cases of dengue fever and seven meteorological variables. The proposed model shows that all the meteorological variables are significantly associated with the confirmed positive cases of dengue fever. That’s why modeling a dengue-fever dataset with a Zero-inflated Poisson-inverse Gaussian regression model is suggested in this study.https://actascientificamalaysia.com/archives/ASM/1asm2024/1asm2024-11-14.pdfdengue feverzero-inflated poisson regression modelzero-inflated negative binomial regression modelzero-inflated poisson inverse gaussian regression modelmaximum likelihood estimation. |
| spellingShingle | Sukanta Chakraborty Soma Chowdhury Biswas MODELLING ZERO-INFLATED OVER DISPERSED DENGUE DATA VIA ZERO-INFLATED POISSON INVERSE GAUSSIAN REGRESSION MODEL: A CASE STUDY OF BANGLADESH Acta Scientifica Malaysia dengue fever zero-inflated poisson regression model zero-inflated negative binomial regression model zero-inflated poisson inverse gaussian regression model maximum likelihood estimation. |
| title | MODELLING ZERO-INFLATED OVER DISPERSED DENGUE DATA VIA ZERO-INFLATED POISSON INVERSE GAUSSIAN REGRESSION MODEL: A CASE STUDY OF BANGLADESH |
| title_full | MODELLING ZERO-INFLATED OVER DISPERSED DENGUE DATA VIA ZERO-INFLATED POISSON INVERSE GAUSSIAN REGRESSION MODEL: A CASE STUDY OF BANGLADESH |
| title_fullStr | MODELLING ZERO-INFLATED OVER DISPERSED DENGUE DATA VIA ZERO-INFLATED POISSON INVERSE GAUSSIAN REGRESSION MODEL: A CASE STUDY OF BANGLADESH |
| title_full_unstemmed | MODELLING ZERO-INFLATED OVER DISPERSED DENGUE DATA VIA ZERO-INFLATED POISSON INVERSE GAUSSIAN REGRESSION MODEL: A CASE STUDY OF BANGLADESH |
| title_short | MODELLING ZERO-INFLATED OVER DISPERSED DENGUE DATA VIA ZERO-INFLATED POISSON INVERSE GAUSSIAN REGRESSION MODEL: A CASE STUDY OF BANGLADESH |
| title_sort | modelling zero inflated over dispersed dengue data via zero inflated poisson inverse gaussian regression model a case study of bangladesh |
| topic | dengue fever zero-inflated poisson regression model zero-inflated negative binomial regression model zero-inflated poisson inverse gaussian regression model maximum likelihood estimation. |
| url | https://actascientificamalaysia.com/archives/ASM/1asm2024/1asm2024-11-14.pdf |
| work_keys_str_mv | AT sukantachakraborty modellingzeroinflatedoverdisperseddenguedataviazeroinflatedpoissoninversegaussianregressionmodelacasestudyofbangladesh AT somachowdhurybiswas modellingzeroinflatedoverdisperseddenguedataviazeroinflatedpoissoninversegaussianregressionmodelacasestudyofbangladesh |