The Comparison between Standardized Mortality Ratio, Poisson-Gamma and Stochastic Sic Model for Pneumonia Disease Mapping in Malaysia
Pneumonia is one of the primary causes of death from infectious diseases. Traditionally, its spread has been tracked based on the total number of cases reported, with no concern for geographical distribution. Disease mapping is among the ways public health and the government can monitor diseases a...
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| Format: | Article |
| Language: | English |
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Universiti Utara Malaysia Press
2022
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| Online Access: | https://repo.uum.edu.my/id/eprint/29110/1/JICT%2021%2004%202022%20549-570.pdf https://doi.org/10.32890/jict2022.21.4.4 |
| _version_ | 1803629584835411968 |
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| author | Mohd Diah, Ijlal Aziz, Nazrina |
| author_facet | Mohd Diah, Ijlal Aziz, Nazrina |
| author_sort | Mohd Diah, Ijlal |
| collection | UUM |
| description | Pneumonia is one of the primary causes of death from infectious diseases. Traditionally, its spread has been tracked based on the
total number of cases reported, with no concern for geographical distribution. Disease mapping is among the ways public health and
the government can monitor diseases as a preventative strategy. Clear pictures of the risk areas can be seen using this method. Relative risk estimation is a significant part of disease mapping that needs to be considered when studying disease occurrence. This paper aimed to estimate the relative risk values for pneumonia based on three models and compare the results. The approaches used in this study were Standardized Morbidity Ratio (SMR), Poisson-gamma, and discrete time-space stochastic Susceptible-Infected-Carriers (SIC) models, which were applied in estimating the relative risk values. Results showed that Kuala Lumpur was classified as a very low-risk area for pneumonia incidence when using the SMR and Poisson-gamma models. In contrast, Selangor was identified as a very low-risk area when using the discrete time-space stochastic SIC model. Putrajaya was categorised as a very high-risk area in the results of all three types of methods. In conclusion, this stochastic SIC model demonstrated better performance than the conventional models. |
| first_indexed | 2024-07-04T06:40:11Z |
| format | Article |
| id | uum-29110 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T06:40:11Z |
| publishDate | 2022 |
| publisher | Universiti Utara Malaysia Press |
| record_format | dspace |
| spelling | uum-291102023-02-09T03:25:58Z https://repo.uum.edu.my/id/eprint/29110/ The Comparison between Standardized Mortality Ratio, Poisson-Gamma and Stochastic Sic Model for Pneumonia Disease Mapping in Malaysia Mohd Diah, Ijlal Aziz, Nazrina QA Mathematics Pneumonia is one of the primary causes of death from infectious diseases. Traditionally, its spread has been tracked based on the total number of cases reported, with no concern for geographical distribution. Disease mapping is among the ways public health and the government can monitor diseases as a preventative strategy. Clear pictures of the risk areas can be seen using this method. Relative risk estimation is a significant part of disease mapping that needs to be considered when studying disease occurrence. This paper aimed to estimate the relative risk values for pneumonia based on three models and compare the results. The approaches used in this study were Standardized Morbidity Ratio (SMR), Poisson-gamma, and discrete time-space stochastic Susceptible-Infected-Carriers (SIC) models, which were applied in estimating the relative risk values. Results showed that Kuala Lumpur was classified as a very low-risk area for pneumonia incidence when using the SMR and Poisson-gamma models. In contrast, Selangor was identified as a very low-risk area when using the discrete time-space stochastic SIC model. Putrajaya was categorised as a very high-risk area in the results of all three types of methods. In conclusion, this stochastic SIC model demonstrated better performance than the conventional models. Universiti Utara Malaysia Press 2022 Article PeerReviewed application/pdf en cc4_by https://repo.uum.edu.my/id/eprint/29110/1/JICT%2021%2004%202022%20549-570.pdf Mohd Diah, Ijlal and Aziz, Nazrina (2022) The Comparison between Standardized Mortality Ratio, Poisson-Gamma and Stochastic Sic Model for Pneumonia Disease Mapping in Malaysia. Journal of Information and Communication Technology, 21 (4). pp. 549-570. ISSN 2180-3862 https://e-journal.uum.edu.my/index.php/jict/article/view/14423 https://doi.org/10.32890/jict2022.21.4.4 https://doi.org/10.32890/jict2022.21.4.4 |
| spellingShingle | QA Mathematics Mohd Diah, Ijlal Aziz, Nazrina The Comparison between Standardized Mortality Ratio, Poisson-Gamma and Stochastic Sic Model for Pneumonia Disease Mapping in Malaysia |
| title | The Comparison between Standardized Mortality Ratio, Poisson-Gamma and Stochastic Sic Model for Pneumonia Disease Mapping in Malaysia |
| title_full | The Comparison between Standardized Mortality Ratio, Poisson-Gamma and Stochastic Sic Model for Pneumonia Disease Mapping in Malaysia |
| title_fullStr | The Comparison between Standardized Mortality Ratio, Poisson-Gamma and Stochastic Sic Model for Pneumonia Disease Mapping in Malaysia |
| title_full_unstemmed | The Comparison between Standardized Mortality Ratio, Poisson-Gamma and Stochastic Sic Model for Pneumonia Disease Mapping in Malaysia |
| title_short | The Comparison between Standardized Mortality Ratio, Poisson-Gamma and Stochastic Sic Model for Pneumonia Disease Mapping in Malaysia |
| title_sort | comparison between standardized mortality ratio poisson gamma and stochastic sic model for pneumonia disease mapping in malaysia |
| topic | QA Mathematics |
| url | https://repo.uum.edu.my/id/eprint/29110/1/JICT%2021%2004%202022%20549-570.pdf https://doi.org/10.32890/jict2022.21.4.4 |
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