About: COVID-19 epidemic in Malaysia: Impact of lock-down on infection dynamics

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About: COVID-19 epidemic in Malaysia: Impact of lock-down on infection dynamics Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	COVID-19 epidemic in Malaysia: Impact of lock-down on infection dynamics
Creator	Chan, Weng Amaran, Safiya Athif, Ahmad Bazin, Nazira Faudzi, Mohd Hooi, Jiun Huspi, Sharin Khoo, Eric Mansor, Shuhaimi Salim, Naomie Shithil, Shaekh Zainal, Anazida
Source	MedRxiv
abstract	COVID-19 epidemic in Malaysia started as a small wave of 22 cases in January 2020 through imported cases. It was followed by a bigger wave mainly from local transmissions resulting in 651 cases. The following wave saw unexpectedly three digit number of daily cases following a mass gathering urged the government to choose a more stringent measure. A limited lock-down approach called Movement Control Order (MCO) was immediately initiated to the whole country as a way to suppress the epidemic trajectory. The lock-down causes a major socio-economic disruption thus the ability to forecast the infection dynamic is urgently required to assist the government on timely decisions. Limited testing capacity and limited epidemiological data complicate the understanding of the future infection dynamic of the COVID-19 epidemic. Three different epidemic forecasting models was used to generate forecasts of COVID-19 cases in Malaysia using daily reported cumulative case data up until 1st April 2020 from the Malaysia Ministry of Health. The forecasts were generated using a Curve Fitting Model with Probability Density Function and Skewness Effect, the SIR Model, and a System Dynamic Model. Method one based on curve fitting with probability density function estimated that the peak will be on 19th April 2020 with an estimation of 5,637 infected persons. Method two based on SIR Model estimated that the peak will be on 20th - 31st May 2020 if Movement Contro (MCO) is in place with an estimation of 630,000 to 800,000 infected persons. Method three based on System Dynamic Model estimated that the peak will be on 17th May 2020 with an estimation of 22,421 infected persons. Forecasts from each of model suggested the epidemic may peak between middle of April to end of May 2020. Keywords: COVID-19, Infection dynamic, Prediction Modeling, SIR, System Learning, Lock-down
has issue date	2020-04-11(xsd:dateTime)
bibo:doi	10.1101/2020.04.08.20057463
has license	medrxiv
sha1sum (hex)	39826490e205c227816986a7bacc0f8db8998f0b
schema:url	https://doi.org/10.1101/2020.04.08.20057463
resource representing a document's title	COVID-19 epidemic in Malaysia: Impact of lock-down on infection dynamics
resource representing a document's body	covid:39826490e205c227816986a7bacc0f8db8998f0b#body_text
is schema:about of	named entity 'The' named entity 'government' named entity 'COVID-19' named entity 'called' named entity 'epidemic' named entity 'EPIDEMIC' named entity 'CHOOSE' named entity 'digit' named entity 'measure' named entity 'Order' named entity 'disruption' named entity 'wave' named entity 'wave' named entity 'COVID-19' named entity 'Malaysia' named entity 'epidemiological data' named entity 'infection' named entity 'epidemic' named entity 'lockdown' named entity 'infection' named entity 'Infected person' named entity 'social distancing' named entity 'COVID-19' named entity 'MCO' named entity 'asymptomatic' named entity 'MCO' named entity 'control order' named entity 'MCO' named entity '28 days' named entity 'social distancing' named entity 'MCO' named entity 'COVID' named entity 'Coronavirus' named entity 'COVID-19' named entity '2.6 million' named entity 'logistic growth model' named entity 'COVID' named entity 'epidemic curve' named entity 'MCO' named entity 'Malaysia' named entity 'coughing' named entity 'regarded as people' named entity 'Malaysia' named entity 'medRxiv' named entity 'lockdown' named entity 'COVID-19' named entity 'MCO' named entity 'MCO' named entity 'travel restrictions' named entity 'normal distribution' named entity 'SIR Model' named entity 'virus' named entity 'Applied Physics Lab' named entity '2.1' named entity 'MCO' named entity 'peer-reviewed' named entity 'peer-reviewed' named entity 'World Health Organization' named entity 'Malaysia' named entity '1918 influenza pandemic' named entity 'death rate' named entity 'epidemic' named entity 'infected person' named entity 'Malaysia' named entity 'machine learning' named entity 'MCO' named entity 'Malaysia' named entity 'case fatality rate' named entity 'SIR models' named entity 'infected person' named entity 'secondary infection' named entity 'MCO' named entity 'Malaysia' named entity 'COVID'

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