About: A predictive model and country risk assessment for COVID-19: An application of the Limited Failure Population concept

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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	A predictive model and country risk assessment for COVID-19: An application of the Limited Failure Population concept
Creator	Koutsellis, Themistoklis Nikas, Alexandros
Source	Elsevier; PMC
abstract	This article provides predictions for the spread of the SARS-CoV-2 virus for a number of European countries and the United States of America, drawing from their different profiles, both socioeconomically and in terms of outbreak and response to the 2019–2020 coronavirus pandemic, from an engineering and data science perspective. Each country is separately analysed, due to their differences in populations density, cultural habits, health care systems, protective measures, etc. The probabilistic analysis is based on actual data, as provided by the World Health Organization (WHO), as of May 1, 2020. The deployed predictive model provides analytical expressions for the cumulative density function of COVID-19 curve and estimations of the proportion of infected subpopulation for each country. The latter is used to define a Risk Index, towards assessing the level of risk for a country to exhibit high rates of COVID-19 cases after a given interval of observation and given the plans of lifting lockdown measures.
has issue date	2020-08-24(xsd:dateTime)
bibo:doi	10.1016/j.chaos.2020.110240
has license	no-cc
sha1sum (hex)	5373819e161215a9e8d362afd34e86c6b6c50e21
schema:url	https://doi.org/10.1016/j.chaos.2020.110240
resource representing a document's title	A predictive model and country risk assessment for COVID-19: An application of the Limited Failure Population concept
has PubMed Central identifier	PMC7444907
schema:publication	Chaos Solitons Fractals
resource representing a document's body	covid:5373819e161215a9e8d362afd34e86c6b6c50e21#body_text
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