About: Analogies between SARS-CoV-2 infection dynamics and batch chemical reactor behavior

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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	Analogies between SARS-CoV-2 infection dynamics and batch chemical reactor behavior
Creator	Ariatti, C Bisotti, F Dell'angelo, A Galeazzi, A Manenti, F Pretoro, A Prifti, K
Source	Elsevier; Medline; PMC
abstract	The pandemic infection of SARS-CoV-2 presents analogies with the behavior of chemical reactors. Susceptible population (A), active infected population (B), recovered cases (C) and deaths (D) can be assumed to be molecules of chemical compounds and their dynamics seem well aligned with those of composition and conversions in chemical syntheses. Thanks to these analogies, it is possible to generate pandemic predictive models based on chemical and physical considerations and regress their kinetic parameters, either globally or locally, to predict the peak time, entity and end of the infection with certain reliability. These predictions can strongly support the emergency plans decision making process. The model predictions have been validated with data from Chinese provinces that already underwent complete infection dynamics. For all the other countries, the evolution is re-regressed and re-predicted every day, updating a pandemic prediction database on Politecnico di Milano’s webpage based on the real-time available data.
has issue date	2020-06-20(xsd:dateTime)
bibo:doi	10.1016/j.ces.2020.115918
bibo:pmid	32834062
has license	no-cc
sha1sum (hex)	ecd598c82e43970cf1d25aa17ef207663e55584f
schema:url	https://doi.org/10.1016/j.ces.2020.115918
resource representing a document's title	Analogies between SARS-CoV-2 infection dynamics and batch chemical reactor behavior
has PubMed Central identifier	PMC7305736
has PubMed identifier	32834062
schema:publication	Chem Eng Sci
resource representing a document's body	covid:ecd598c82e43970cf1d25aa17ef207663e55584f#body_text
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