Transmission Dynamics of COVID-19 in Malaysia Prior to the Movement Control Order

Labadin, Jane; Hong, Boon Hao

doi:10.1101/2020.02.07.20021188

Cited by 17 publications

(23 citation statements)

References 6 publications

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“…In this article, using the SEIR model of Malaysia [15], we will discuss the creation of a control solution using mathematical tools: Figure (1): SEIR model based on reference [15] The transmission model is based on the epidemiological aspects of the disease [15].…”

Section: Modelingmentioning

confidence: 99%

“…(1) -(4)), which describe the dynamics of 2019-nCoV in the human population, are formulated based on a diagram first described in Figure (1). [15] (1)…”

Section: Modelingmentioning

confidence: 99%

“…Are transferred to the improved part. However, there are some contaminated items that may die due to disease with a certain rate (d) [15].…”

Section: Differential Equations (Equationsmentioning

confidence: 99%

“…: Right: SEIR modeling using reference[15], Left figure: Control system design to reduce patients and control them under a thousand people using PID controller…”

mentioning

confidence: 99%

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A study on control of novel corona-virus (2019- nCoV) disease process by using PID controller

rad

badi

2020

Preprint

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Background, In this paper, the SEIR dynamic model will be used to model the epidemic of coronvirus (2019-nCoV)disease. The SEIR model has been used to model infectious diseases in Malaysia.Then, the spread and control of the disease is simulated applying a PID controller. The results of this study show that the implementation of strict restrictions such as quarantine, social distancing and closure of gathering centers is effective in controlling the disease. Using the results and analyzing them, it was found that early and strict implementation of strict restrictions such as quarantine, social distance and closure of centers with a high percentage of community is very important to control this disease and prevent irreparable economic losses and depreciation of medical staff.Objective: Modeling the prevalence and control of corona-virus (2019-nCoV)and the impact of government actions using control engineering methods. Method:In this study, the SEIR dynamic model was used and the common data on the prevalence of the virus in Wuhan, China and Malaysia were used. As an example, the use of control target schemes is simulated in this paper. Results:The findings of this study use control methods and forecasting in control engineering to provide a clear picture of macro-decisions for different governments in the field of infectious diseases. Conclusion:Management and control schemes such as travel restrictions, quarantine, social distance and closure of offices, higher education institutions must be implemented immediately to prevent major economic and social losses. The implementation of these restrictions should not be delayed during the outbreak of corona-virus(2019-nCoV) infectious diseases.

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Section: Modelingmentioning

confidence: 99%

“…(1) -(4)), which describe the dynamics of 2019-nCoV in the human population, are formulated based on a diagram first described in Figure (1). [15] (1)…”

Section: Modelingmentioning

confidence: 99%

“…Are transferred to the improved part. However, there are some contaminated items that may die due to disease with a certain rate (d) [15].…”

Section: Differential Equations (Equationsmentioning

confidence: 99%

“…: Right: SEIR modeling using reference[15], Left figure: Control system design to reduce patients and control them under a thousand people using PID controller…”

mentioning

confidence: 99%

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A study on control of novel corona-virus (2019- nCoV) disease process by using PID controller

rad

badi

2020

Preprint

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“…Epidemiological models, e.g. SEIR model, are being evolved to suit the national conditions [4,10,17,20,24]. A model based on delay-differential equations considers the effects of past events [31].…”

Section: Introductionmentioning

confidence: 99%

Evolution of COVID-19 Pandemic in India

Ali

Srivastava

Verma

2020

Preprint

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A mathematical analysis of patterns for the evolution of COVID-19 cases is key to the development of reliable and robust predictive models potentially leading to efficient and effective governance against COVID-19. Towards this objective, we study and analyze the temporal growth pattern of COVID-19 infection and death counts in various states of India. Our analysis up to June 16, 2020 shows that several states (namely Maharashtra, Tamil Nadu, Delhi, Uttar Pradesh) have reached t-squared power-law growth, while some others like Gujarat, Rajasthan, and Madhya Pradesh have reached linear growth. Karnataka and Kerala are exhibiting a second wave. In addition, we report that the death counts exhibit similar behavior as the infection counts. These observations indicate that Indian states are far from flattening their epidemic curves.

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