Closed monitoring of Malaysia COVID-19 using SEIR compartmental model for first wave trajectory

Malaysia COVID-19 trend trajectory has shown significant improvement since Malaysia employed Movement Control Order (MCO) on 18th Mar 2020 Since then a modified compartmental Susceptible, Exposed, Infectious and Removed (SEIR) model has been developed to monitor closely the development on the epidemic The model introduced early detection factor in order to measure the reliability of the strategy carried out The closed monitoring is to estimate the current projection of Reproduction Number (R0), a number which a single infected person could outspread the virus to other people, and to forecast in the near future of the number of active cases for several days This is to show that the strategy carried out by the Malaysian government in order to contain the outbreak whether or not has taken into effect within the period of the first wave of the outbreak The observation carried out has found out that under the Enhanced MCO (EMCO) or the second MCO showed significant reduction in the number of active cases as well as R0 Not only that the early detection strategy carried out has shown significant improvement of 2 5 to 3 6 times higher than the first MCO © 2021 Siti Daleela Mohd Wahid et al

[1]  J. Vincent,et al.  Understanding pathways to death in patients with COVID-19 , 2020, The Lancet Respiratory Medicine.

[2]  L. Yang,et al.  Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak , 2020, International Journal of Infectious Diseases.

[3]  Yongli Cai,et al.  A conceptual model for the coronavirus disease 2019 (COVID-19) outbreak in Wuhan, China with individual reaction and governmental action , 2020, International Journal of Infectious Diseases.

[4]  Etienne Pardoux,et al.  NUMERICAL METHODS IN THE CONTEXT OF COMPARTMENTAL MODELS IN EPIDEMIOLOGY , 2015 .

[5]  P.L. Lewin,et al.  Modeling and Parameter Estimation of High Voltage Transformer Using Rational Transfer Function State Space Approach , 2008, 2008 Annual Report Conference on Electrical Insulation and Dielectric Phenomena.

[6]  Kenneth D Mandl,et al.  Early Transmissibility Assessment of a Novel Coronavirus in Wuhan, China. , 2020, SSRN.

[7]  Ramizi Mohamed,et al.  Frequency Domain Modeling of High Voltage Transformers Using a Nonlinear Least-Square Estimation Technique , 2009 .

[8]  S. Syafruddin,et al.  Lyapunov function of SIR and SEIR model for transmission of dengue fever disease , 2013, Int. J. Simul. Process. Model..

[9]  G. Leung,et al.  First-wave COVID-19 transmissibility and severity in China outside Hubei after control measures, and second-wave scenario planning: a modelling impact assessment , 2020, The Lancet.

[10]  Cheng-Shang Chang,et al.  A Time-dependent SIR model for COVID-19 , 2020, ArXiv.

[11]  J. Dushoff,et al.  Inferring the causes of the three waves of the 1918 influenza pandemic in England and Wales , 2013, Proceedings of the Royal Society B: Biological Sciences.

[12]  M. Noorani,et al.  SEIR MODEL FOR TRANSMISSION OF DENGUE FEVER IN SELANGOR MALAYSIA , 2012 .

[13]  G. Pandey,et al.  SEIR and Regression Model based COVID-19 outbreak predictions in India , 2020, medRxiv.

[14]  A. Vespignani,et al.  Evolving epidemiology and transmission dynamics of coronavirus disease 2019 outside Hubei province, China: a descriptive and modelling study , 2020, The Lancet Infectious Diseases.

[15]  Graham Williams,et al.  Conference on Electrical Insulation and Dielectric Phenomena , 1982, IEEE Transactions on Electrical Insulation.

[16]  Malik Peiris,et al.  Viral dynamics in mild and severe cases of COVID-19 , 2020, The Lancet Infectious Diseases.

[17]  G. Leung,et al.  Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study , 2020, The Lancet.

[18]  Liangrong Peng,et al.  Epidemic analysis of COVID-19 in China by dynamical modeling , 2020, medRxiv.

[19]  Antone dos Santos Benedito,et al.  A Novel Technique to Estimate Biological Parameters in an Epidemiology Problem , 2017, IWANN.

[20]  D. Cummings,et al.  Novel coronavirus 2019-nCoV: early estimation of epidemiological parameters and epidemic predictions , 2020, medRxiv.

[21]  Hannah R. Meredith,et al.  The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application , 2020, Annals of Internal Medicine.

[22]  Carl A. B. Pearson,et al.  The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study , 2020, The Lancet Public Health.

[23]  C. Anastassopoulou,et al.  Data-based analysis, modelling and forecasting of the COVID-19 outbreak , 2020, medRxiv.

[24]  C. Althaus,et al.  Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020 , 2020, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[25]  Mohd. Salmi Md. Noorani,et al.  SEIR Model for Transmission of Dengue Fever , 2012 .

[26]  D. Murdoch,et al.  Clinical course and mortality risk of severe COVID-19 , 2020, The Lancet.

[27]  X. Rodó,et al.  A modified SEIR model to predict the COVID-19 outbreak in Spain and Italy: Simulating control scenarios and multi-scale epidemics , 2020, Results in Physics.

[28]  M. Baguelin,et al.  Report 3: Transmissibility of 2019-nCoV , 2020 .

[29]  W. Liang,et al.  Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions , 2020, Journal of thoracic disease.