About: SEIR Modeling of the Italian Epidemic of SARS-CoV-2 Using Computational Swarm Intelligence

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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	SEIR Modeling of the Italian Epidemic of SARS-CoV-2 Using Computational Swarm Intelligence
Creator	Godio, Alberto Pace, Francesca Vergnano, Andrea
Source	Medline; PMC
abstract	We applied a generalized SEIR epidemiological model to the recent SARS-CoV-2 outbreak in the world, with a focus on Italy and its Lombardy, Piedmont, and Veneto regions. We focused on the application of a stochastic approach in fitting the model parameters using a Particle Swarm Optimization (PSO) solver, to improve the reliability of predictions in the medium term (30 days). We analyzed the official data and the predicted evolution of the epidemic in the Italian regions, and we compared the results with the data and predictions of Spain and South Korea. We linked the model equations to the changes in people’s mobility, with reference to Google’s COVID-19 Community Mobility Reports. We discussed the effectiveness of policies taken by different regions and countries and how they have an impact on past and future infection scenarios.
has issue date	2020-05-18(xsd:dateTime)
bibo:doi	10.3390/ijerph17103535
bibo:pmid	32443640
has license	cc-by
sha1sum (hex)	d65444c03b7c179056ab0cffb2317cd9badc16eb
schema:url	https://doi.org/10.3390/ijerph17103535
resource representing a document's title	SEIR Modeling of the Italian Epidemic of SARS-CoV-2 Using Computational Swarm Intelligence
has PubMed Central identifier	PMC7277829
has PubMed identifier	32443640
schema:publication	Int J Environ Res Public Health
resource representing a document's body	covid:d65444c03b7c179056ab0cffb2317cd9badc16eb#body_text
is schema:about of	named entity 'data' named entity 'Piedmont' named entity 'variation' named entity 'official' named entity 'Google' named entity 'lockdown' named entity 'infection' named entity 'medium' named entity 'predictions' named entity 'infection' named entity 'QUARANTINE' named entity 'PIEDMONT' named entity 'DEVELOPMENT' named entity 'CONSTANT' named entity 'ANALYZED' named entity 'REGIONS' named entity 'REPRESENT' named entity '5B4' named entity 'DECREASES' named entity 'GOOGLE' named entity 'POLICIES' named entity 'DATA' named entity 'RECOVERED' named entity 'ACCOUNT' named entity 'introduces' named entity 'policies' named entity 'dead' named entity 'infected' named entity 'equations' named entity 'fitting' named entity 'epidemic' named entity 'Google' named entity 'infection' named entity 'infection rate' named entity 'COVID-19' named entity 'SARS-CoV-2 outbreak' named entity 'Italy' named entity 'Epidemic' named entity 'Italian' named entity 'good health' named entity 'PSO' named entity 'SARS-CoV-2 outbreak' named entity 'Particle Swarm Optimization' named entity 'algorithm' named entity 'deterministic' named entity 'deterministic' named entity 'Piedmont' named entity 'Italian' named entity 'deterministic' named entity 'developed countries' named entity 'WHO' named entity 'deterministic' named entity 'data set' named entity 'data fitting' named entity 'deterministic' named entity 'death rate' named entity 'SEIR' named entity 'SEIR' named entity 'PSO' named entity 'Italian' named entity 'South Korea' named entity 'PSO' named entity 'Italy' named entity 'Piedmont' named entity 'infection' named entity 'evolution' named entity 'search-space' named entity 'SEIR' named entity 'synergic' named entity 'Google' named entity 'epidemiological studies' named entity 'PSO' named entity 'data fitting' named entity 'quarantine' named entity 'deterministic' named entity 'epidemiological data' named entity 'SEIR' named entity 'population density' named entity 'PSO'

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