Simulation of COVID-19 outbreaks via Graphical User Interface (GUI)

Background: This research aimed to model the outbreak of COVID-19 in Malaysia and develop a GUI-based model. Design and Methods: The model is an improvement of the susceptible, infected, recovery, and death (SIRD) compartmental model. The epidemiological parameters of the infection, recovery, and death rates were formulated as time dependent piecewise functions by incorporating the control measures of lockdown, social distancing, quarantine, lockdown lifting time and the percentage of people who abide by the rules. An improved SIRD model was solved via the 4th order Runge-Kutta (RK4) method and 14 unknown parameters were estimated by using Nelder-Mead algorithm and pattern-search technique. The publicly available data for COVID-19 outbreak in Malaysia was used to validate the performance of the model. The GUI-based SIRD model was developed to simulate the number of active cases of COVID-19 over time by considering movement control order (MCO) lifted date and the percentage of people who abide the rules. Results: The simulator showed that the improved SIRD model adequately fitted Malaysia COVID-19 data indicated by low values of root mean square error (RMSE) as compared to other existing models. The higher the percentage of people following the SOP, the lower the spread of disease. Another key point is that the later the lifting time after the lockdown, the lower the spread of disease. Conclusion: These findings highlight the importance of the society to obey the intervention measures in preventing the spread of the COVID-19 disease.

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Modelling the Effectiveness of Epidemic Control Measures in Preventing the Transmission of COVID-19 in Malaysia

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17155509 ◽

2020 ◽

Vol 17 (15) ◽

pp. 5509 ◽

Cited By ~ 1

Author(s):

Balvinder Singh Gill ◽

Vivek Jason Jayaraj ◽

Sarbhan Singh ◽

Sumarni Mohd Ghazali ◽

Yoon Ling Cheong ◽

...

Keyword(s):

Mathematical Model ◽

Model Calibration ◽

Reproduction Number ◽

Close Contact ◽

Movement Control ◽

Control Measures ◽

Seir Model ◽

Epidemic Curve ◽

Reproduction Numbers ◽

Control Order

Malaysia is currently facing an outbreak of COVID-19. We aim to present the first study in Malaysia to report the reproduction numbers and develop a mathematical model forecasting COVID-19 transmission by including isolation, quarantine, and movement control measures. We utilized a susceptible, exposed, infectious, and recovered (SEIR) model by incorporating isolation, quarantine, and movement control order (MCO) taken in Malaysia. The simulations were fitted into the Malaysian COVID-19 active case numbers, allowing approximation of parameters consisting of probability of transmission per contact (β), average number of contacts per day per case (ζ), and proportion of close-contact traced per day (q). The effective reproduction number (Rt) was also determined through this model. Our model calibration estimated that (β), (ζ), and (q) were 0.052, 25 persons, and 0.23, respectively. The (Rt) was estimated to be 1.68. MCO measures reduce the peak number of active COVID-19 cases by 99.1% and reduce (ζ) from 25 (pre-MCO) to 7 (during MCO). The flattening of the epidemic curve was also observed with the implementation of these control measures. We conclude that isolation, quarantine, and MCO measures are essential to break the transmission of COVID-19 in Malaysia.

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Is Nigeria really on top of COVID-19? Message from effective reproduction number

10.1101/2020.05.16.20104471 ◽

2020 ◽

Author(s):

Adeshina Israel Adekunle ◽

Oyelola Adegboye ◽

Ezra Gayawan ◽

Emma McBryde

Keyword(s):

Public Health ◽

Compartmental Model ◽

Reproduction Number ◽

Transmission Rate ◽

Credible Interval ◽

Control Measures ◽

Maximum Value ◽

Intervention Measures ◽

Death Cases ◽

Epidemiological Parameters

Following the importation of Covid-19 into Nigeria on the 27 February 2020 and then the outbreak, the question is: how do we anticipate the progression of the ongoing epidemics following all the intervention measures put in place? This kind of question is appropriate for public health responses and it will depend on the early estimates of the key epidemiological parameters of the virus in a defined population. In this study, we combined a likelihood-based method using a Bayesian framework and compartmental model of the epidemic of Covid-19 in Nigeria to estimate the effective reproduction number (R(t)) and basic reproduction number (R_0). This also enables us to estimate the daily transmission rate (β) that determines the effect of social distancing. We further estimate the reported fraction of symptomatic cases. The models are applied to the NCDC data on Covid-19 symptomatic and death cases from 27 February 2020 and 7 May 2020. In this period, the effective reproduction number is estimated with a minimum value of 0.18 and a maximum value of 1.78. Most importantly, the R(t) is strictly greater than one from April 13 till 7 May 2020. The R_0 is estimated to be 2.42 with credible interval: (2.37, 2.47). Comparing this with the R(t) shows that control measures are working but not effective enough to keep R(t) below one. Also, the estimated fractional reported symptomatic cases are between 10 to 50%. Our analysis has shown evidence that the existing control measures are not enough to end the epidemic and more stringent measures are needed.

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Use of movement restrictions during an outbreak of COVID-19 in Selangor, Malaysia

Western Pacific Surveillance and Response ◽

10.5365/wpsar.2020.11.3.008 ◽

2021 ◽

Vol 12 (2) ◽

pp. 51-56

Author(s):

Anita Suleiman ◽

Shaari Ngadiman ◽

Mazliza Ramly ◽

Ahmad Faudzi Yusoff ◽

Mohamed Paid Yusof

Keyword(s):

Public Health ◽

Movement Control ◽

Health Department ◽

Control Measures ◽

Contact Tracing ◽

Epidemic Curve ◽

Imported Cases ◽

Control Order ◽

Control Orders ◽

Public Health Strategies

Objective: Various public health and social measures have been used during the COVID-19 outbreak, including lockdowns, contact-tracing, isolation and quarantine. The objective of this manuscript is to describe outbreaks of COVID-19 in Selangor, Malaysia, the public health strategies used and the observed impact of the measures on the epidemic curve. Methods: Information on all confirmed COVID-19 cases in Selangor between 25 January and 28 April 2020 was obtained. Clusters were identified, and cases were disaggregated into linked, unlinked and imported cases. Epidemic curves were constructed, and the timing of movement control orders was compared with the numbers of cases reported. Results: During the study period, 1395 confirmed COVID-19 cases were reported to the Selangor Health Department, of which 15.8% were imported, 79.5% were linked and 4.7% were unlinked cases. For two main clusters, the number of cases decreased after control measures were instituted, by contact-tracing followed by isolation and home quarantine for the first cluster (n = 126), and with the addition of the movement control order for the second, much larger cluster (n = 559). Discussion: The findings suggest that appropriate, timely public health interventions and movement control measures have a synergistic effect on controlling COVID-19 outbreaks.

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Forecasting Malaysia COVID-19 Incidence based on Movement Control Order using ARIMA and Expert Modeler

IIUM Medical Journal Malaysia ◽

10.31436/imjm.v19i2.1606 ◽

2020 ◽

Vol 19 (2) ◽

Author(s):

Edre MA ◽

Muhammad Adil ZA ◽

Jamalludin AR

Keyword(s):

Moving Average ◽

Arima Model ◽

Movement Control ◽

Time Frame ◽

Control Measures ◽

Model Fit ◽

Quality Of Data ◽

Auto Regressive ◽

Control Order ◽

Time Frames

INTRODUCTION: Coronavirus disease (COVID-19) is a novel pandemic that affects every other country in the world. Various countries have adopted control measures involving restriction of movement. Several studies have used mathematical modelling to predict the dynamic of this pandemic. Forecasting techniques can be used to predict the incidence cases for the short term. The study aims to forecast the COVID-19 incidence using the Auto Regressive Integrated Moving Average (ARIMA) method. MATERIALS AND METHODS: Using publicly available data, we performed a forecast of Malaysia COVID-19 new cases using Expert Modeler Method in SPSS and ARIMA model in R to predict COVID-19 cases in Malaysia. We compare 3 different time frames based on different Movement Control Order (MCO) period. We compare the model fit and prediction across models. RESULTS: All models show static cases for each MCO 7-day prediction. For prediction until 12 May, the third MCO time frame shows the best model fit for both techniques. Both software shows a stationary trend of cases of below 100. CONCLUSION: These MCO models have shown to stabilize the rate of new cases. Further sub analysis and quality of data is needed to improve the accuracy of the model.

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Is Nigeria really on top of COVID-19? Message from effective reproduction number

Epidemiology and Infection ◽

10.1017/s0950268820001740 ◽

2020 ◽

Vol 148 ◽

Cited By ~ 3

Author(s):

A. I. Adekunle ◽

O. A. Adegboye ◽

E. Gayawan ◽

E. S. McBryde

Keyword(s):

Public Health ◽

Compartmental Model ◽

Reproduction Number ◽

Transmission Rate ◽

Credible Interval ◽

Control Measures ◽

Maximum Value ◽

Intervention Measures ◽

Death Cases ◽

Epidemiological Parameters

Abstract Following the importation of coronavirus disease (COVID-19) into Nigeria on 27 February 2020 and then the outbreak, the question is: How do we anticipate the progression of the ongoing epidemic following all the intervention measures put in place? This kind of question is appropriate for public health responses and it will depend on the early estimates of the key epidemiological parameters of the virus in a defined population. In this study, we combined a likelihood-based method using a Bayesian framework and compartmental model of the epidemic of COVID-19 in Nigeria to estimate the effective reproduction number (R(t)) and basic reproduction number (R0) – this also enables us to estimate the initial daily transmission rate (β0). We further estimate the reported fraction of symptomatic cases. The models are applied to the NCDC data on COVID-19 symptomatic and death cases from 27 February 2020 and 7 May 2020. In this period, the effective reproduction number is estimated with a minimum value of 0.18 and a maximum value of 2.29. Most importantly, the R(t) is strictly greater than one from 13 April till 7 May 2020. The R0 is estimated to be 2.42 with credible interval: (2.37–2.47). Comparing this with the R(t) shows that control measures are working but not effective enough to keep R(t) below 1. Also, the estimated fraction of reported symptomatic cases is between 10 and 50%. Our analysis has shown evidence that the existing control measures are not enough to end the epidemic and more stringent measures are needed.

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Mathematical epidemiologic and simulation modelling of first wave COVID-19 in Malaysia

Scientific Reports ◽

10.1038/s41598-021-99541-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Muhammad Rezal Kamel Ariffin ◽

Kathiresan Gopal ◽

Isthrinayagy Krishnarajah ◽

Iszuanie Syafidza Che Ilias ◽

Mohd Bakri Adam ◽

...

Keyword(s):

Transmission Rate ◽

Mainland China ◽

Movement Control ◽

Simulation Modelling ◽

Sir Model ◽

Control Measures ◽

Cumulative Number ◽

Severity Of The Disease ◽

Control Order ◽

Second Wave

AbstractSince the first coronavirus disease 2019 (COVID-19) outbreak appeared in Wuhan, mainland China on December 31, 2019, the geographical spread of the epidemic was swift. Malaysia is one of the countries that were hit substantially by the outbreak, particularly in the second wave. This study aims to simulate the infectious trend and trajectory of COVID-19 to understand the severity of the disease and determine the approximate number of days required for the trend to decline. The number of confirmed positive infectious cases [as reported by Ministry of Health, Malaysia (MOH)] were used from January 25, 2020 to March 31, 2020. This study simulated the infectious count for the same duration to assess the predictive capability of the Susceptible-Infectious-Recovered (SIR) model. The same model was used to project the simulation trajectory of confirmed positive infectious cases for 80 days from the beginning of the outbreak and extended the trajectory for another 30 days to obtain an overall picture of the severity of the disease in Malaysia. The transmission rate, β also been utilized to predict the cumulative number of infectious individuals. Using the SIR model, the simulated infectious cases count obtained was not far from the actual count. The simulated trend was able to mimic the actual count and capture the actual spikes approximately. The infectious trajectory simulation for 80 days and the extended trajectory for 110 days depicts that the inclining trend has peaked and ended and will decline towards late April 2020. Furthermore, the predicted cumulative number of infectious individuals tallies with the preparations undertaken by the MOH. The simulation indicates the severity of COVID-19 disease in Malaysia, suggesting a peak of infectiousness in mid-March 2020 and a probable decline in late April 2020. Overall, the study findings indicate that outbreak control measures such as the Movement Control Order (MCO), social distancing and increased hygienic awareness is needed to control the transmission of the outbreak in Malaysia.

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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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