Modeling Covid-19 Pandemic Responses in Malaysia for the First 145 Days Duration (Jan. 25 – June 17, 2020)

Abstract Background At 145 days from Jan. 25, the Covid-19 recovery rate was 92.5% of the total cumulative cases of 8,515 with 1.4% fatalities were recorded in Malaysia. On top of the provided medical services and facilities, the recoveries could be mainly resulted from the Movement Control Order (MCO) enforcements in six phases for mitigating the pandemic nationwide. To understand the Covid-19 pandemic trend and dynamic mathematically to the MCO phases, the applicability of the proposed models ( tri-logistic growth model) on covid-19 related responses was studied by using the ‘matured’ 145-day data duration. The study also aimed at, (1) identifying information/parameters obtained through modeling technique that could be used in explaining the pattern of dynamic subjected to MCO enforcements, and (2) forecasting the quantitative information for postdate days beyond the used data duration. Methods Each serial layered-logistic growth of cumulative cases, recoveries, fatalities, and active cases were linked among them via the asymptotic cumulative cases and the nature of the recovery-fatality complementarity. The used data of Jan. 25 – Jun. 17, 2020 were quite ‘matured’ trend as observed in the ‘plateauing’ of cumulative cases. The appropriate R2 was used for model fitness analysis. The innovative SAS program writing of SAS9.4 version was used for the analysis. Conclusion Predicted cumulative cases, recoveries, fatalities, and active cases vs. days were significantly fitted to the observed values (R2 > 99 %), indicating the validity of the tri-logistic growth proposed model. Via the derivative of cumulative cases function, the daily cases or dynamic model was significant (R2=70%) with the fluctuating daily cases moved around the predicted curve. The authority has been classified the pandemic as of two waves (Jan. 25 – Feb. 26, 2020, and Feb. 27, 2020 onwards), and these were regrouped as wave 1 (primary wave) in this study. Within this one wave, the study shows that covid-19 in Malaysia has three cycles: cycle 1(1o cycle) [Jan. 25 – Apr.27], cycle 2 [Apr. 28 – May 14] and cycle 3 [May 15 and onwards] with the aggressiveness order of wave 1 > wave 3 > wave 2 (wave 3 had mainly caused by local foreigners). The observed cumulative cases were slightly higher than the predicted (by 0.07% -0.49%) during Jun 13 – 17 suggesting small room of resources still available for covid-19 infection, thus, maintaining the current MCO is crucial. Beyond Jun.17, the forecasts of cumulative cases, recoveries, fatalities, and active cases at June 30 would be 8500, 8168, 128, and 202 cases, respectively, with daily cases of 1 case day-1 . If no any further odd-extraordinary wave occurrence, the country would probably be enjoying a non-significant covid or ‘covid-free’ pandemic by the July end or mid-August, 2020. The model had validated the correctness and appropriateness of the MCO phases’ enforcements. The active cases model was the resultant of linkages of all models via asymptotic cumulative cases. Based on above resultant and the logistic growth function applicability in various fields, the modeling approach involving serial tri-logistic functions could probably be used in analyzing covid or covid-related epidemic elsewhere and in the future.

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Evaluation of the Effectiveness of Movement Control Order to Limit the Spread of COVID-19

Annals of Emerging Technologies in Computing ◽

10.33166/aetic.2020.04.001 ◽

2020 ◽

Vol 4 (4) ◽

pp. 1-9

Author(s):

Md Amiruzzaman ◽

M. Abdullah-Al-Wadud ◽

Rizal Mohd Nor ◽

Normaziah A. Aziz

Keyword(s):

Prediction Model ◽

Growth Curve ◽

Growth Models ◽

Movement Control ◽

Logistic Growth ◽

Model Based ◽

Model Match ◽

Partial Data ◽

Control Order ◽

Three Phases

This study presents a prediction model based on Logistic Growth Curve (LGC) to evaluate the effectiveness of Movement Control Order (MCO) on COVID-19 pandemic spread. The evaluation assesses and predicts the growth models. The estimated model is a forecast-based model that depends on partial data from the COVID-19 cases in Malaysia. The model is studied on the effectiveness of the three phases of MCO implemented in Malaysia, where the model perfectly fits with the R2 value 0.989. Evidence from this study suggests that results of the prediction model match with the progress and effectiveness of the MCO to flatten the curve, and thus is helpful to control the spike in number of active COVID-19 cases and spread of COVID-19 infection growth.

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Evaluation of the Effectiveness of Movement Control Order to Limit the Spread of COVID-19

10.20944/preprints202005.0247.v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Md Amiruzzaman ◽

M. Abdullah-Al-Wadud ◽

Rizal Mohd Nor ◽

Normaziah A. Aziz

Keyword(s):

Prediction Model ◽

Growth Curve ◽

Growth Models ◽

Movement Control ◽

Logistic Growth ◽

Model Based ◽

Model Match ◽

Partial Data ◽

Control Order ◽

Three Phases

This study presents a prediction model based on Logistic Growth Curve to evaluate the effectiveness of Movement Control Order (MCO) on COVID-19 pandemic spread. The evaluation assesses and predicts the growth models. The estimated model is a forecast-based model that depended on partial data from the COVID-19 cases in Malaysia. The model is then studied together with the effectiveness of the three phases of MCO implemented in Malaysia. Evidence from this study suggests that results of the LGC prediction model match with the progress and effectiveness of the MCO to flatten the curve, thus helped to control the spike in number of active COVID-19 cases and spread of COVID-19 infection growth.

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A bi-logistic growth model for conference registration with an early bird deadline

Open Physics ◽

10.2478/s11534-013-0275-4 ◽

2013 ◽

Vol 11 (7) ◽

Author(s):

Trevor Fenner ◽

Mark Levene ◽

George Loizou

Keyword(s):

Growth Function ◽

Logistic Growth ◽

Human Response ◽

Social Mechanism ◽

Data Set ◽

Early Bird ◽

Registration Data ◽

Recent Interest ◽

Logistic Growth Model ◽

Conference Registration

AbstractThe recent interest in human dynamics has led researchers to investigate the processes that explain human behaviour within different contexts. Here we are concerned in modelling the human response to a deadline, and in particular we look at the process of conference registration with an early bird deadline. We provide empirical evidence from a six-year conference registration data set that the bi-logistic growth function, with the interpretation as registration with an early bird deadline, can be viewed as a social mechanism.

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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 ◽

10.1016/j.idm.2021.07.004 ◽

2021 ◽

Author(s):

Nicholas Pang Tze Ping ◽

Assis Kamu ◽

Mohd Amiruddin Mohd Kassim ◽

Ho Chong Mun

Keyword(s):

Growth Modeling ◽

Movement Control ◽

Logistic Growth ◽

Modeling Approach ◽

Control Order ◽

The Impact

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A Susceptible-Infected-Removed (SIR) model of COVID-19 epidemic trend in Malaysia under Movement Control Order (MCO) using a data fitting approach

10.1101/2020.05.01.20084384 ◽

2020 ◽

Cited By ~ 1

Author(s):

Wan Nor Arifin ◽

Weng Howe Chan ◽

Safiya Amaran ◽

Kamarul Imran Musa

Keyword(s):

Public Health ◽

Movement Control ◽

Data Fitting ◽

Sir Model ◽

Decision Makers ◽

Susceptible Population ◽

The Public ◽

Proposed Model ◽

Control Order ◽

Parameter Values

AbstractBackgroundIn 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.MethodsThe 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.ResultsThe 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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Public knowledge, perception and communication behavior surrounding COVID-19 in Malaysia

10.31124/advance.12102816 ◽

2020 ◽

Author(s):

Khayriyyah Mohd Hanafiah ◽

Chang Da Wan

Keyword(s):

Logistic Regression ◽

Employment Status ◽

Multinomial Logistic Regression ◽

Movement Control ◽

Education Level ◽

Public Knowledge ◽

Communication Behavior ◽

Positive Communication ◽

The Face ◽

Control Order

The COVID-19 pandemic is the first to occur in an age of hyperconnectivity. This paper presents results from an online anonymous survey conducted in Malay, English, and Chinese, during the first week of the Movement Control Order in Malaysia (n=1075), which aimed to examine public knowledge, perception and communication behavior in the Malaysian society in the face of a sudden outbreak and social distancing measures. Although the level of public knowledge, risk perception and positive communication behavior surrounding COVID-19 was high, a majority of respondents reported receiving a lot of questionable information. Multinomial logistic regression further identified that responses to different items varied significantly across respondent survey language, gender, age, education level and employment status.

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Forecast of COVID-19 Trend Following Countrywide Movement Control Order in Malaysia: SEIR Model Approach (Preprint)

10.2196/preprints.22304 ◽

2020 ◽

Author(s):

Aidalina Mahmud ◽

Poh Ying Lim ◽

Hayati Kadir Shahar

Keyword(s):

Mathematical Model ◽

Incidence Rate ◽

Movement Control ◽

Seir Model ◽

Infection Incidence ◽

Contact Rates ◽

Exposed Population ◽

Control Order ◽

Future Work ◽

Model Approach

BACKGROUND On March 18, 2020, the Malaysian government implemented Movement Control Order (MCO) to limit the contact rates among the population and infected individuals. OBJECTIVE The objective of this study was to forecast the trend of the COVID-19 epidemic in Malaysia in terms of its magnitude and duration. METHODS Data for this analysis was obtained from publicly available databases, from March 17 until March 27, 2020. By applying the Susceptible, Exposed, Infectious and Removed (SEIR) mathematical model and several predetermined assumptions, two analyses were carried out: without and with MCO implementation. RESULTS Without MCO, it is forecasted that it would take 18 days to reach the peak of infection incidence. The incidence rate would plateau at day 80 and end by day 94, with 43% of the exposed population infected. With the implementation of the MCO, it is forecasted that new cases of infection would peak at day 25, plateau at day 90 and end by day 100. At its peak, the infection could affect up to about 40% of the exposed population. CONCLUSIONS It is forecasted that the COVID-19 epidemic in Malaysia will subside soon after the mid-year of 2020. Although the implementation of MCO can flatten the epidemiological curve, it also prolongs the duration of the epidemic. The MCO can result in several unfavorable consequences in economic and psychosocial aspects. A future work of an exit plan for the MCO should also be devised and implemented gradually. The exit plan raises several timely issues of re-infection resurgence after MCO are lifted.

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