A Susceptible-Infected-Removed (SIR) model of COVID-19 epidemic trend in Malaysia under Movement Control Order (MCO) using a data fitting approach
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Abstract
Background
In this work, we presented a Susceptible-Infected-Removed (SIR) epidemiological model of COVID-19 epidemic in Malaysia post- and pre-Movement Control Order (MCO). The proposed SIR model was fitted to confirmed COVID-19 cases from the official press statements to closely reflect the observed epidemic trend in Malaysia. The proposed model is aimed to provide an accurate predictive information for decision makers in assessing the public health and social measures related to COVID-19 epidemic.
Methods
The SIR model was fitted to the data by minimizing a weighted loss function; the sum of the residual sum of squares (RSS) of infected, removed and total cases. Optimized beta (β),), gamma (γ) parameter values) parameter values and the starting value of susceptible individuals ( N ) were obtained.
Results
The SIR model post-MCO indicates the peak of infection on 10 April 2020, less than 100 active cases by 8 July 2020, less than 10 active cases by 29 August 2020, and close to zero daily new case by 22 July 2020, with a total of 6562 infected cases. In the absence of MCO, the model predicts the peak of infection on 1 May 2020, less than 100 active cases by 14 February 2021, less than 10 active cases by 26 April 2021 and close to zero daily new case by 6 October 2020, with a total of 1.6 million infected cases. Conclusion : The results suggest that the present MCO has significantly reduced the number of susceptible population and the total number of infected cases. The method to fit the SIR model used in this study was found to be accurate in reflecting the observed data. The method can be used to predict the epidemic trend of COVID-19 in other countries.
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SciScore for 10.1101/2020.05.01.20084384: (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
Software and Algorithms Sentences Resources Using the data, the SIR model was plotted using ggplot2 R package [12]. ggplot2suggested: (ggplot2, RRID:SCR_014601)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Limitations: The model was unable to consider imported cases because the size of susceptible population was assumed fixed at the start of each prediction period. Hence, another approach must be considered whenever imported cases are present. The performance measures provided in form of R2 and MAPE can be …
SciScore for 10.1101/2020.05.01.20084384: (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
Software and Algorithms Sentences Resources Using the data, the SIR model was plotted using ggplot2 R package [12]. ggplot2suggested: (ggplot2, RRID:SCR_014601)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Limitations: The model was unable to consider imported cases because the size of susceptible population was assumed fixed at the start of each prediction period. Hence, another approach must be considered whenever imported cases are present. The performance measures provided in form of R2 and MAPE can be viewed as the goodness of fit and error measures for the training set, which may not be generalizable to future observations and there could be an issue with model overfitting [19]. However, given the limited number of available data points, all available data points were used for the model optimization. Given the urgency of the present situation, it is not reasonable to wait for more data points to be used as the test set to estimate the test goodness of fit and error measures. In our opinion, cross-validation method is also not reasonable in this situation given the small number of data points. Finally, our model used the available incidence data instead of the data based on date of onset [20]. Despite this limitation, we feel that our SIR model, to some extent, reflects the true COVID-19 epidemic trend.
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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