About: FORECASTING COVID-19 PANDEMIC: A DATA-DRIVEN ANALYSIS

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Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	FORECASTING COVID-19 PANDEMIC: A DATA-DRIVEN ANALYSIS
Creator	Khondoker, Nazmoon Nabi,
Source	Elsevier; Medline; PMC
abstract	In this paper, a new Susceptible-Exposed-Symptomatic Infectious-Asymptomatic Infectious-Quarantined-Hospitalized-Recovered-Dead (SEI(D)I(U)QHRD) deterministic compartmental model has been proposed and calibrated for interpreting the transmission dynamics of the novel coronavirus disease (COVID-19). The purpose of this study is to give a tentative prediction of the epidemic peak for Russia, Brazil, India and Bangladesh which could become the next COVID-19 hotspots in no time by using a Trust-region-reflective (TRR) algorithm which one of the well-known real data fitting techniques. Based on the publicly available epidemiological data from late January until 10 May, it has been estimated that the number of daily new symptomatic infectious cases for the above mentioned countries could reach the peak around the beginning of June with the peak size of ∼ 15, 774 (95% CI, 12,814-16,734) symptomatic infectious cases in Russia, ∼ 26, 449 (95% CI, 25,489-31,409) cases in Brazil, ∼ 9, 504 (95% CI, 8,378-13,630) cases in India and ∼ 2, 209 (95% CI, 2,078-2,840) cases in Bangladesh. As of May 11, 2020, incorporating the infectiousness capability of asymptomatic carriers, our analysis estimates the value of the basic reproduction number (R(0)) as of May 11, 2020 was found to be ∼ 4.234 (95% CI, 3.764-4.7) in Russia, ∼ 5.347 (95% CI, 4.737-5.95) in Brazil, ∼ 5.218 (95% CI, 4.56-5.81)in India, ∼ 4.649 (95% CI, 4.17-5.12) in the United Kingdom and ∼ 3.53 (95% CI, 3.12-3.94) in Bangladesh. Moreover, Latin hypercube sampling-partial rank correlation coefficient (LHS-PRCC) which is a global sensitivity analysis (GSA) method is applied to quantify the uncertainty of our model mechanisms, which elucidates that for Russia, the recovery rate of undetected asymptomatic carriers, the rate of getting home-quarantined or self-quarantined and the transition rate from quarantined class to susceptible class are the most influential parameters, whereas the rate of getting home-quarantined or self-quarantined and the inverse of the COVID-19 incubation period are highly sensitive parameters in Brazil, India, Bangladesh and the United Kingdom which could significantly affect the transmission dynamics of the novel coronavirus. Our analysis also suggests that relaxing social distancing restrictions too quickly could exacerbate the epidemic outbreak in the above-mentioned countries.
has issue date	2020-06-25(xsd:dateTime)
bibo:doi	10.1016/j.chaos.2020.110046
bibo:pmid	32834601
has license	no-cc
sha1sum (hex)	579c5a79277c68903c3ccbfb9b85b04300025359
schema:url	https://doi.org/10.1016/j.chaos.2020.110046
resource representing a document's title	FORECASTING COVID-19 PANDEMIC: A DATA-DRIVEN ANALYSIS
has PubMed Central identifier	PMC7315964
has PubMed identifier	32834601
schema:publication	Chaos Solitons Fractals
resource representing a document's body	covid:579c5a79277c68903c3ccbfb9b85b04300025359#body_text
is schema:about of	named entity 'May 11' named entity 'Latin hypercube' named entity 'basic reproduction number' named entity 'social distancing' named entity 'Brazil' named entity 'peak' named entity '95% CI' named entity 'asymptomatic carriers' named entity 'peak' named entity 'Brazil' named entity 'epidemic' named entity 'hotspots' named entity 'Bangladesh' named entity '95% CI' named entity 'India' named entity 'affect' named entity 'COVID-19' named entity 'model' named entity 'reach' named entity '95% CI' named entity 'estimates' named entity 'Brazil' named entity 'quarantined' named entity 'compartmental model' named entity 'January' named entity 'correlation coefficient' named entity 'estimated' named entity 'Russia' named entity 'deterministic' named entity 'Latin hypercube sampling' named entity '95% CI' named entity 'Brazil' named entity 'Quarantined' named entity 'Brazil' named entity 'India' named entity '95% CI' named entity 'coronavirus' named entity 'United Kingdom' named entity 'epidemiological data' named entity 'social distancing restrictions' named entity 'Wuhan' named entity 'Asymptomatic' named entity 'contact tracing' named entity 'mathematical models' named entity 'Peter Turchin' named entity 'basic reproduction number' named entity 'virus' named entity 'asymptomatic' named entity 'sensitivity analysis' named entity 'algorithm' named entity 'metapopulation' named entity 'May 12' named entity 'Russia' named entity 'incubation period' named entity 'deterministic' named entity 'Worldometer' named entity 'SEIR' named entity 'respiratory disease' named entity 'mathematical model' named entity 'COVID' named entity 'Bangladesh' named entity 'virus' named entity 'countries and territories' named entity 'China' named entity 'global public health' named entity 'asymptomatic' named entity 'nonlinear differential equations' named entity 'infection' named entity 'COVID-19' named entity 'COVID-19' named entity 'case-fatality rate' named entity 'coronavirus disease 2019' named entity 'algorithm' named entity 'trust-region' named entity 'COVID' named entity 'Bangladesh' named entity 'case-fatality rate' named entity 'spectral radius' named entity '95% CI' named entity 'COVID'

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