Application of zero-truncated count data regression models to air-pollution disease
Count data consist of non-negative integers that have many applications in various fields of studies. To handle count data, there are various statistical models that can be employed corresponding to the properties of the count data studied. Poisson regression model (PRM) is mostly used to model data...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
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2021
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| Online Access: | http://eprints.utm.my/98089/1/FadhilahYusof2021_ApplicationOfZeroTruncatedCountDataRegression.pdf |
| _version_ | 1796866551410851840 |
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| author | Zulki Alwani, Z. I. Ibrahim, A. I. N. Yunus, R. M. Yusof, F. |
| author_facet | Zulki Alwani, Z. I. Ibrahim, A. I. N. Yunus, R. M. Yusof, F. |
| author_sort | Zulki Alwani, Z. I. |
| collection | ePrints |
| description | Count data consist of non-negative integers that have many applications in various fields of studies. To handle count data, there are various statistical models that can be employed corresponding to the properties of the count data studied. Poisson regression model (PRM) is mostly used to model data with equidispersion, while negative binomial regression model (NBRM) is a model that is regularly employed to model over-dispersed count data. On the other hand, the usual count data regression models may not able to handle strictly positive counts. In this case, the appropriate model for the analysis of such data would be models truncated at zero. We are interested to study the relationship between pollution related disease with influential factors such as air pollution and climate variables in Johor Bahru, Malaysia, using these zero-truncated models, where the number of disease cases are strictly positive. In particular, the zero-truncated PRM and NBRM are used to determine the association between the number of dengue patients and their influential factors. From the study, zero-truncated NBRM is found to be the best model amongst the two models to model the relationship between the number dengue cases and air pollution and climate. Air pollution factors that significantly affect the number of cases for dengue are particulate matter (PM10) and sulfur dioxide. Also, humidity and temperature are the climate factors that significantly affect the number of dengue cases. |
| first_indexed | 2024-03-05T21:13:51Z |
| format | Conference or Workshop Item |
| id | utm.eprints-98089 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T21:13:51Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | utm.eprints-980892022-11-30T04:14:05Z http://eprints.utm.my/98089/ Application of zero-truncated count data regression models to air-pollution disease Zulki Alwani, Z. I. Ibrahim, A. I. N. Yunus, R. M. Yusof, F. QA Mathematics Count data consist of non-negative integers that have many applications in various fields of studies. To handle count data, there are various statistical models that can be employed corresponding to the properties of the count data studied. Poisson regression model (PRM) is mostly used to model data with equidispersion, while negative binomial regression model (NBRM) is a model that is regularly employed to model over-dispersed count data. On the other hand, the usual count data regression models may not able to handle strictly positive counts. In this case, the appropriate model for the analysis of such data would be models truncated at zero. We are interested to study the relationship between pollution related disease with influential factors such as air pollution and climate variables in Johor Bahru, Malaysia, using these zero-truncated models, where the number of disease cases are strictly positive. In particular, the zero-truncated PRM and NBRM are used to determine the association between the number of dengue patients and their influential factors. From the study, zero-truncated NBRM is found to be the best model amongst the two models to model the relationship between the number dengue cases and air pollution and climate. Air pollution factors that significantly affect the number of cases for dengue are particulate matter (PM10) and sulfur dioxide. Also, humidity and temperature are the climate factors that significantly affect the number of dengue cases. 2021 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/98089/1/FadhilahYusof2021_ApplicationOfZeroTruncatedCountDataRegression.pdf Zulki Alwani, Z. I. and Ibrahim, A. I. N. and Yunus, R. M. and Yusof, F. (2021) Application of zero-truncated count data regression models to air-pollution disease. In: 28th Simposium Kebangsaan Sains Matematik, SKSM 2021, 28 - 29 July 2021, Kuantan, Pahang, Virtual. http://dx.doi.org/10.1088/1742-6596/1988/1/012096 |
| spellingShingle | QA Mathematics Zulki Alwani, Z. I. Ibrahim, A. I. N. Yunus, R. M. Yusof, F. Application of zero-truncated count data regression models to air-pollution disease |
| title | Application of zero-truncated count data regression models to air-pollution disease |
| title_full | Application of zero-truncated count data regression models to air-pollution disease |
| title_fullStr | Application of zero-truncated count data regression models to air-pollution disease |
| title_full_unstemmed | Application of zero-truncated count data regression models to air-pollution disease |
| title_short | Application of zero-truncated count data regression models to air-pollution disease |
| title_sort | application of zero truncated count data regression models to air pollution disease |
| topic | QA Mathematics |
| url | http://eprints.utm.my/98089/1/FadhilahYusof2021_ApplicationOfZeroTruncatedCountDataRegression.pdf |
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