Spatio-temporal model to forecast COVID-19 confirmed cases in high-density areas of Malaysia
The coronavirus 2019 disease has spread across the world. The number ofcoronaviruses 2019 (COVID-19) cases throughout Malaysia is high in the densely populated state of Selangor. In assisting the early preventive measures, this study utilises time series methods to model and forecast the number of d...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Penerbit UTM Press
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/114409/ http://psasir.upm.edu.my/id/eprint/114409/1/114409.pdf |
| _version_ | 1848866485127086080 |
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| author | Abd Rahman, Nur Haizum Muhammad Yusof, Saidatul Nurfarahin Che Ilias, Iszuanie Syafidza Gopal, Kathiresan Yaacob, Hannuun Mohammad Sham, Noraishah |
| author_facet | Abd Rahman, Nur Haizum Muhammad Yusof, Saidatul Nurfarahin Che Ilias, Iszuanie Syafidza Gopal, Kathiresan Yaacob, Hannuun Mohammad Sham, Noraishah |
| author_sort | Abd Rahman, Nur Haizum |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The coronavirus 2019 disease has spread across the world. The number ofcoronaviruses 2019 (COVID-19) cases throughout Malaysia is high in the densely populated state of Selangor. In assisting the early preventive measures, this study utilises time series methods to model and forecast the number of daily positive cases in three Selangor districts: Petaling, Hulu Langat, and Klang. Specifically, the study compares the effectiveness of the Autoregressive Integrated Moving Average (ARIMA), a univariate model and the Generalized Space-Time autoregressive integrated (GSTARI), a multivariate model. For the GSTARI model, uniform and inverse distance weights represent the relationship between locations. The analysed data are from January to August 2021, and the lowest root mean square error (RMSE) is chosen as the best model. The results show GSTARI (1,1) with both spatial weights outperformed ARIMA (0,1,1) in Petaling and Klang but not in Hulu Langat. However, the average RMSE values show that the most accurate and effective for forecasting the number of daily confirmed positive cases in Selangor is using GSTARI. In conclusion, by utilising advanced time series methods such as spatial analysis, this study provides important insights into forecasting trends of infectiousdiseases like COVID-19 and can help in early preventive measures. |
| first_indexed | 2025-11-15T14:21:21Z |
| format | Article |
| id | upm-114409 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:21:21Z |
| publishDate | 2024 |
| publisher | Penerbit UTM Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1144092025-01-20T02:51:23Z http://psasir.upm.edu.my/id/eprint/114409/ Spatio-temporal model to forecast COVID-19 confirmed cases in high-density areas of Malaysia Abd Rahman, Nur Haizum Muhammad Yusof, Saidatul Nurfarahin Che Ilias, Iszuanie Syafidza Gopal, Kathiresan Yaacob, Hannuun Mohammad Sham, Noraishah The coronavirus 2019 disease has spread across the world. The number ofcoronaviruses 2019 (COVID-19) cases throughout Malaysia is high in the densely populated state of Selangor. In assisting the early preventive measures, this study utilises time series methods to model and forecast the number of daily positive cases in three Selangor districts: Petaling, Hulu Langat, and Klang. Specifically, the study compares the effectiveness of the Autoregressive Integrated Moving Average (ARIMA), a univariate model and the Generalized Space-Time autoregressive integrated (GSTARI), a multivariate model. For the GSTARI model, uniform and inverse distance weights represent the relationship between locations. The analysed data are from January to August 2021, and the lowest root mean square error (RMSE) is chosen as the best model. The results show GSTARI (1,1) with both spatial weights outperformed ARIMA (0,1,1) in Petaling and Klang but not in Hulu Langat. However, the average RMSE values show that the most accurate and effective for forecasting the number of daily confirmed positive cases in Selangor is using GSTARI. In conclusion, by utilising advanced time series methods such as spatial analysis, this study provides important insights into forecasting trends of infectiousdiseases like COVID-19 and can help in early preventive measures. Penerbit UTM Press 2024-10-15 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/114409/1/114409.pdf Abd Rahman, Nur Haizum and Muhammad Yusof, Saidatul Nurfarahin and Che Ilias, Iszuanie Syafidza and Gopal, Kathiresan and Yaacob, Hannuun and Mohammad Sham, Noraishah (2024) Spatio-temporal model to forecast COVID-19 confirmed cases in high-density areas of Malaysia. Malaysian Journal of Fundamental and Applied Sciences, 20 (5). pp. 972-984. ISSN 2289-599X; https://mjfas.utm.my/index.php/mjfas/article/view/3389 10.11113/mjfas.v20n5.3389 |
| spellingShingle | Abd Rahman, Nur Haizum Muhammad Yusof, Saidatul Nurfarahin Che Ilias, Iszuanie Syafidza Gopal, Kathiresan Yaacob, Hannuun Mohammad Sham, Noraishah Spatio-temporal model to forecast COVID-19 confirmed cases in high-density areas of Malaysia |
| title | Spatio-temporal model to forecast COVID-19 confirmed cases in high-density areas of Malaysia |
| title_full | Spatio-temporal model to forecast COVID-19 confirmed cases in high-density areas of Malaysia |
| title_fullStr | Spatio-temporal model to forecast COVID-19 confirmed cases in high-density areas of Malaysia |
| title_full_unstemmed | Spatio-temporal model to forecast COVID-19 confirmed cases in high-density areas of Malaysia |
| title_short | Spatio-temporal model to forecast COVID-19 confirmed cases in high-density areas of Malaysia |
| title_sort | spatio-temporal model to forecast covid-19 confirmed cases in high-density areas of malaysia |
| url | http://psasir.upm.edu.my/id/eprint/114409/ http://psasir.upm.edu.my/id/eprint/114409/ http://psasir.upm.edu.my/id/eprint/114409/ http://psasir.upm.edu.my/id/eprint/114409/1/114409.pdf |