Applying the SEIR Model in Forecasting The COVID-19 Trend in Malaysia: A Preliminary Study
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Abstract
On March 18, 2020 the Malaysian government implemented a 14-day Movement Control Order (MCO) as part of the mitigation plan in controlling the COVID-19 epidemic in the country. The MCO aims to limit the contact rates among the population and hence prevent the surge of infected individuals. However, the trend of the epidemic before and after the MCO was not apparent. By applying the Susceptible, Exposed, Infectious and Removed (SEIR) mathematical model, we aimed to forecast the trend of COVID-19 epidemic in Malaysia using data from March 17 to 27, 2020. Based on several predetermined assumptions, the results of the analyses showed that after the implementation of the 14-day MCO from March 18 to 31, 2020, it is forecasted that the epidemic in Malaysia will peak approximately in the end of April 2020 and will subside by about the first week of July 2020. The MCO will “flatten the epidemic curve” but will prolong the duration of the epidemic. Decision to extend the duration of the MCO should depend on the consideration of socioeconomic factors as well.
Author summary
Dr. Aidalina Mahmud is a Public Health Specialist and a medical lecturer in the Department of Community Health, Universiti Putra Malaysia. Dr. Lim Poh Ying is a Biostatistician and is a senior lecturer in the Department of Community Health, Universiti Putra Malaysia.
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SciScore for 10.1101/2020.04.14.20065607: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:The limitations of this forecast lie mainly in the assumptions used and because the data used were from publicly available platforms. We used the data available on the day the data was announced. As there was a backlog of specimens to be analyzed of about 1,000 during the time of this analysis, we could not be certain whether the data on …
SciScore for 10.1101/2020.04.14.20065607: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:The limitations of this forecast lie mainly in the assumptions used and because the data used were from publicly available platforms. We used the data available on the day the data was announced. As there was a backlog of specimens to be analyzed of about 1,000 during the time of this analysis, we could not be certain whether the data on the new number of cases announced was entirely same-day data or it included data from specimens taken several days prior to the announcement. Apart from the number of new cases for each day, the nature of this data affected another parameter, namely the recovery period. For the recovery period, we considered the date of removal (discharged or death) and subtract that from the date of confirmation of diagnosis, instead of the actual dates of the start of illness to the day of discharge or death. This calculation may slightly under-estimate the duration of illness. Also, we did not consider the more frequent active case detection activities which have just been started during the production of this article. We also assumed that the laboratory capacity remains the same in the next few months, as when this analysis was conducted. Nonetheless, the results of this model could give an idea of the possible time span of the epidemic, until more detailed data is available. Additionally, the model could demonstrate that flattening the epidemic curve prolongs the duration of epidemic.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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