About: Epidemic Trend Analysis of SARS-CoV-2 in SAARC Countries Using Modified SIR (M-SIR) Predictive Model

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About: Epidemic Trend Analysis of SARS-CoV-2 in SAARC Countries Using Modified SIR (M-SIR) Predictive Model Goto Sponge NotDistinct Permalink

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Epidemic Trend Analysis of SARS-CoV-2 in SAARC Countries Using Modified SIR (M-SIR) Predictive Model
Creator	Rahman, Howlader, Arpita Kumar Dey, Samrat Shibly, Kabid Siddiqi, Raihan
Source	MedRxiv
abstract	A novel coronavirus causing the severe and fatal respiratory syndrome was identified in China, is now producing outbreaks in more than two hundred countries around the world, and became pandemic by the time. In this article, a modified version of the well known mathematical epidemic model Susceptible (S)- Infected (I)- Recovered (R) is used to analyze the epidemic's course of COVID-19 in eight different countries of the South Asian Association for Regional Cooperation (SAARC). To achieve this goal, the parameters of the SIR model are identified by using publicly available data for the corresponding countries: Afghanistan, Bangladesh, Bhutan, India, the Maldives, Nepal, Pakistan and Sri Lanka. Based on the prediction model we estimated the epidemic trend of COVID-19 outbreak in SAARC countries for 20 days, 90 days, and 180 days respectively. An SML (short-mid-long) term prediction model has been designed to understand the early dynamics of COVID-19 Epidemic in the south-east Asian region. The maximum and minimum basic reproduction number (R0 = 1.33 and 1.07) for SAARC countries are predicted to be in Pakistan and Bhutan. We equate simulation results with real data in the SAARC countries on the COVID-19 outbreak, and model potential countermeasure implementation scenarios. Our results should provide policymakers with a method for evaluating the impacts of possible interventions, including lockdown and social distancing, as well as testing and contact tracking.
has issue date	2020-06-30(xsd:dateTime)
bibo:doi	10.1101/2020.06.29.20142513
has license	medrxiv
sha1sum (hex)	9326446a84fa1a002d0cedb5b9de87b69a6e5e9d
schema:url	https://doi.org/10.1101/2020.06.29.20142513
resource representing a document's title	Epidemic Trend Analysis of SARS-CoV-2 in SAARC Countries Using Modified SIR (M-SIR) Predictive Model
resource representing a document's body	covid:9326446a84fa1a002d0cedb5b9de87b69a6e5e9d#body_text
is schema:about of	named entity 'India' named entity 'Bhutan' named entity 'countermeasure' named entity 'AVAILABLE' named entity 'AFGHANISTAN' named entity 'PAKISTAN' named entity 'SRI LANKA' named entity 'USED' named entity 'IDENTIFIED' named entity 'COVID-19 EPIDEMIC' named entity 'GOAL' named entity 'INDIA' named entity 'SIR MODEL' named entity '28S' named entity 'PANDEMIC' named entity 'PREDICTED' named entity 'DIFFERENT' named entity 'COUNTRIES' named entity '28I' named entity 'EPIDEMIC MODEL' named entity 'tracking' named entity 'data' named entity 'SAARC' named entity 'predicted' named entity 'basic reproduction number' named entity 'simulation' named entity 'South Asian' named entity 'SAARC' named entity 'fatal' named entity 'epidemic' named entity 'provide' named entity 'social distancing' named entity 'epidemic model' named entity 'evaluating' named entity 'Pakistan' named entity 'time' named entity 'severe' named entity 'early' named entity 'SAARC' named entity 'Modified' named entity 'COVID' named entity 'Nepal' named entity 'SIR model' named entity 'Afghanistan' named entity 'China' named entity 'Pakistan' named entity 'SAARC' named entity 'Bhutan' named entity 'social distancing' named entity 'epidemic' named entity 'Pakistan' named entity 'basic reproduction number' named entity 'Trend Analysis' named entity 'SAARC' named entity 'SAARC' named entity 'COVID-19' named entity 'COVID-19' named entity 'preprint' named entity 'preprint' named entity 'peer review' named entity 'preprint' named entity 'geopolitical' named entity 'copyright holder' named entity 'medRxiv' named entity 'Maldives' named entity 'medRxiv' named entity 'SAARC' named entity 'India' named entity 'CC-BY-NC-ND 4.0' named entity 'SIR model' named entity 'research studies' named entity 'coronavirus' named entity 'copyright holder' named entity 'data analysis' named entity 'South Asia' named entity 'Johns Hopkins University' named entity 'medRxiv' named entity 'epidemic model' named entity 'Nepal' named entity 'epidemiological' named entity 'SAARC'

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