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 of coronaviruses 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...
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| Format: | Article |
| Language: | English |
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UTM Press
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/42848/1/2024%20Spatio-Temporal%20Model%20to%20Forecast%20COVID-19%20Confirmed%20Cases%20in%20High-Density%20Areas%20of%20Malaysia.pdf |
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| author | Nur Haizum, Abd Rahman Saidatul Nurfarahin, Muhammad Yusof Iszuanie Syafidza, Che Ilias Gopal, Kathiresan Hannuun, Yaacob Noraishah, Mohammad Sham |
| author_facet | Nur Haizum, Abd Rahman Saidatul Nurfarahin, Muhammad Yusof Iszuanie Syafidza, Che Ilias Gopal, Kathiresan Hannuun, Yaacob Noraishah, Mohammad Sham |
| author_sort | Nur Haizum, Abd Rahman |
| collection | UMP |
| description | The coronavirus 2019 disease has spread across the world. The number of coronaviruses 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 infectious diseases like COVID-19 and can help in early preventive measures. |
| first_indexed | 2024-12-09T02:30:48Z |
| format | Article |
| id | UMPir42848 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-12-09T02:30:48Z |
| publishDate | 2024 |
| publisher | UTM Press |
| record_format | dspace |
| spelling | UMPir428482024-10-24T00:29:53Z http://umpir.ump.edu.my/id/eprint/42848/ Spatio-temporal model to forecast covid-19 confirmed cases in high-density areas of Malaysia Nur Haizum, Abd Rahman Saidatul Nurfarahin, Muhammad Yusof Iszuanie Syafidza, Che Ilias Gopal, Kathiresan Hannuun, Yaacob Noraishah, Mohammad Sham QA Mathematics The coronavirus 2019 disease has spread across the world. The number of coronaviruses 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 infectious diseases like COVID-19 and can help in early preventive measures. UTM Press 2024 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/42848/1/2024%20Spatio-Temporal%20Model%20to%20Forecast%20COVID-19%20Confirmed%20Cases%20in%20High-Density%20Areas%20of%20Malaysia.pdf Nur Haizum, Abd Rahman and Saidatul Nurfarahin, Muhammad Yusof and Iszuanie Syafidza, Che Ilias and Gopal, Kathiresan and Hannuun, Yaacob and Noraishah, Mohammad Sham (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 2289-5981. (Published) https://doi.org/10.11113/mjfas.v20n5.3389 10.11113/mjfas.v20n5.3389 |
| spellingShingle | QA Mathematics Nur Haizum, Abd Rahman Saidatul Nurfarahin, Muhammad Yusof Iszuanie Syafidza, Che Ilias Gopal, Kathiresan Hannuun, Yaacob Noraishah, Mohammad Sham 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 |
| topic | QA Mathematics |
| url | http://umpir.ump.edu.my/id/eprint/42848/1/2024%20Spatio-Temporal%20Model%20to%20Forecast%20COVID-19%20Confirmed%20Cases%20in%20High-Density%20Areas%20of%20Malaysia.pdf |
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