Monitoring the impact of Movement Control Order (MCO) in flattening the cummulative daily cases curve of Covid-19 in Malaysia: a generalized logistic growth modeling approach
Introduction: COVID-19 has affected almost every country in the world, which causing many negative implications in terms of education, economy and mental health. Worryingly, the trend of second or third wave of the pandemic has been noted in multiple regions despite early success of flattening the c...
| _version_ | 1796910910580719616 |
|---|---|
| author | Pang, Nicholas Tze Ping Assis Kamu Mohd Amiruddin Mohd Kassim Ho, Chong Mun |
| author_facet | Pang, Nicholas Tze Ping Assis Kamu Mohd Amiruddin Mohd Kassim Ho, Chong Mun |
| author_sort | Pang, Nicholas Tze Ping |
| collection | UMS |
| description | Introduction: COVID-19 has affected almost every country in the world, which causing many negative implications in terms of education, economy and mental health. Worryingly, the trend of second or third wave of the pandemic has been noted in multiple regions despite early success of flattening the curve, such as in the case of Malaysia, post Sabah state election in September 2020. Hence, it is imperative to predict ongoing trend of COVID-19 to assist crucial policymaking in curbing the transmission. Method: Generalized logistic growth modelling (GLM) approach was adopted to make prediction of growth of cases according to each state in Malaysia. The data was obtained from official Ministry of Health Malaysia daily report, starting from 26 September 2020 until 1 January 2021.Result: Sabah, Johor, Selangor and Kuala Lumpur are predicted to exceed 10,000 cumulative cases by 2 February 2021. Nationally, the growth factor has been shown to range between 0.25 to a peak of 3.1 throughout the current Movement Control Order (MCO). The growth factor range for Sabah ranged from 1.00 to 1.25, while Selangor, the state which has the highest case, has a mean growth factor ranging from 1.22 to 1.52. The highest growth rates reported were in WP Labuan for the time periods of 22 Nov - 5 Dec 2020 with growth rates of 4.77. States with higher population densities were predicted to have higher cases of COVID-19. Conclusion: GLM is helpful to provide governments and policymakers with accurate and helpful forecasts on magnitude of epidemic and peak time. This forecast could assist government in devising short- and long-term plan to tackle the ongoing pandemic. |
| first_indexed | 2024-03-06T03:10:56Z |
| format | Article |
| id | ums.eprints-30572 |
| institution | Universiti Malaysia Sabah |
| language | English English |
| last_indexed | 2024-03-06T03:10:56Z |
| publishDate | 2021 |
| publisher | KeAi Publishing Communications Ltd. |
| record_format | dspace |
| spelling | ums.eprints-305722021-10-22T03:06:09Z https://eprints.ums.edu.my/id/eprint/30572/ Monitoring the impact of Movement Control Order (MCO) in flattening the cummulative daily cases curve of Covid-19 in Malaysia: a generalized logistic growth modeling approach Pang, Nicholas Tze Ping Assis Kamu Mohd Amiruddin Mohd Kassim Ho, Chong Mun RA643-645 Disease (Communicable and noninfectious) and public health Introduction: COVID-19 has affected almost every country in the world, which causing many negative implications in terms of education, economy and mental health. Worryingly, the trend of second or third wave of the pandemic has been noted in multiple regions despite early success of flattening the curve, such as in the case of Malaysia, post Sabah state election in September 2020. Hence, it is imperative to predict ongoing trend of COVID-19 to assist crucial policymaking in curbing the transmission. Method: Generalized logistic growth modelling (GLM) approach was adopted to make prediction of growth of cases according to each state in Malaysia. The data was obtained from official Ministry of Health Malaysia daily report, starting from 26 September 2020 until 1 January 2021.Result: Sabah, Johor, Selangor and Kuala Lumpur are predicted to exceed 10,000 cumulative cases by 2 February 2021. Nationally, the growth factor has been shown to range between 0.25 to a peak of 3.1 throughout the current Movement Control Order (MCO). The growth factor range for Sabah ranged from 1.00 to 1.25, while Selangor, the state which has the highest case, has a mean growth factor ranging from 1.22 to 1.52. The highest growth rates reported were in WP Labuan for the time periods of 22 Nov - 5 Dec 2020 with growth rates of 4.77. States with higher population densities were predicted to have higher cases of COVID-19. Conclusion: GLM is helpful to provide governments and policymakers with accurate and helpful forecasts on magnitude of epidemic and peak time. This forecast could assist government in devising short- and long-term plan to tackle the ongoing pandemic. KeAi Publishing Communications Ltd. 2021 Article PeerReviewed text en https://eprints.ums.edu.my/id/eprint/30572/1/Monitoring%20the%20impact%20of%20Movement%20Control%20Order%20%28MCO%29%20in%20flattening%20the%20cummulative%20daily%20cases%20curve%20of%20Covid-19%20in%20Malaysia%20a%20generalized%20logistic%20growth%20modeling%20approach_FULL%20TEXT.pdf text en https://eprints.ums.edu.my/id/eprint/30572/3/Monitoring%20the%20impact%20of%20Movement%20Control%20Order%20%28MCO%29%20in%20flattening%20the%20cummulative%20daily%20cases%20curve%20of%20Covid-19%20in%20Malaysia%20a%20generalized%20logistic%20growth%20modeling%20approach%20ABSTRACT.pdf Pang, Nicholas Tze Ping and Assis Kamu and Mohd Amiruddin Mohd Kassim and Ho, Chong Mun (2021) Monitoring the impact of Movement Control Order (MCO) in flattening the cummulative daily cases curve of Covid-19 in Malaysia: a generalized logistic growth modeling approach. Infectious Disease Modelling, 6. pp. 898-908. ISSN 2468-0427 https://www.sciencedirect.com/science/article/pii/S2468042721000506#! https://doi.org/10.1016/j.idm.2021.07.004 https://doi.org/10.1016/j.idm.2021.07.004 |
| spellingShingle | RA643-645 Disease (Communicable and noninfectious) and public health Pang, Nicholas Tze Ping Assis Kamu Mohd Amiruddin Mohd Kassim Ho, Chong Mun Monitoring the impact of Movement Control Order (MCO) in flattening the cummulative daily cases curve of Covid-19 in Malaysia: a generalized logistic growth modeling approach |
| title | Monitoring the impact of Movement Control Order (MCO) in flattening the cummulative daily cases curve of Covid-19 in Malaysia: a generalized logistic growth modeling approach |
| title_full | Monitoring the impact of Movement Control Order (MCO) in flattening the cummulative daily cases curve of Covid-19 in Malaysia: a generalized logistic growth modeling approach |
| title_fullStr | Monitoring the impact of Movement Control Order (MCO) in flattening the cummulative daily cases curve of Covid-19 in Malaysia: a generalized logistic growth modeling approach |
| title_full_unstemmed | Monitoring the impact of Movement Control Order (MCO) in flattening the cummulative daily cases curve of Covid-19 in Malaysia: a generalized logistic growth modeling approach |
| title_short | Monitoring the impact of Movement Control Order (MCO) in flattening the cummulative daily cases curve of Covid-19 in Malaysia: a generalized logistic growth modeling approach |
| title_sort | monitoring the impact of movement control order mco in flattening the cummulative daily cases curve of covid 19 in malaysia a generalized logistic growth modeling approach |
| topic | RA643-645 Disease (Communicable and noninfectious) and public health |
| url | https://eprints.ums.edu.my/id/eprint/30572/1/Monitoring%20the%20impact%20of%20Movement%20Control%20Order%20%28MCO%29%20in%20flattening%20the%20cummulative%20daily%20cases%20curve%20of%20Covid-19%20in%20Malaysia%20a%20generalized%20logistic%20growth%20modeling%20approach_FULL%20TEXT.pdf https://eprints.ums.edu.my/id/eprint/30572/3/Monitoring%20the%20impact%20of%20Movement%20Control%20Order%20%28MCO%29%20in%20flattening%20the%20cummulative%20daily%20cases%20curve%20of%20Covid-19%20in%20Malaysia%20a%20generalized%20logistic%20growth%20modeling%20approach%20ABSTRACT.pdf |
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