About: A model based study on the dynamics of COVID-19: Prediction and control

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About: A model based study on the dynamics of COVID-19: Prediction and control 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	A model based study on the dynamics of COVID-19: Prediction and control
Creator	Adak, Sayani Jana, Soovoojeet Kar, T Khatua, Anupam Mandal, Manotosh Nandi, Swapan
Source	Elsevier; Medline; PMC
abstract	As there is no vaccination and proper medicine for treatment, the recent pandemic caused by COVID-19 has drawn attention to the strategies of quarantine and other governmental measures, like lockdown, media coverage on social isolation, and improvement of public hygiene, etc to control the disease. The mathematical model can help when these intervention measures are the best strategies for disease control as well as how they might affect the disease dynamics. Motivated by this, in this article, we have formulated a mathematical model introducing a quarantine class and governmental intervention measures to mitigate disease transmission. We study a thorough dynamical behavior of the model in terms of the basic reproduction number. Further, we perform the sensitivity analysis of the essential reproduction number and found that reducing the contact of exposed and susceptible humans is the most critical factor in achieving disease control. To lessen the infected individuals as well as to minimize the cost of implementing government control measures, we formulate an optimal control problem, and optimal control is determined. Finally, we forecast a short-term trend of COVID-19 for the three highly affected states, Maharashtra, Delhi, and Tamil Nadu, in India, and it suggests that the first two states need further monitoring of control measures to reduce the contact of exposed and susceptible humans.
has issue date	2020-05-13(xsd:dateTime)
bibo:doi	10.1016/j.chaos.2020.109889
bibo:pmid	32406395
has license	no-cc
sha1sum (hex)	6c808957fb64cb1e22530a3aa2ab5a3da641b2f0
schema:url	https://doi.org/10.1016/j.chaos.2020.109889
resource representing a document's title	A model based study on the dynamics of COVID-19: Prediction and control
has PubMed Central identifier	PMC7218394
has PubMed identifier	32406395
schema:publication	Chaos Solitons Fractals
resource representing a document's body	covid:6c808957fb64cb1e22530a3aa2ab5a3da641b2f0#body_text
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