Random resampling numerical simulations applied to a SEIR compartmental model
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Random resampling numerical simulations applied to a SEIR compartmental model

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Random resampling numerical simulations applied to a SEIR compartmental model

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dc.contributor.author Morillas Jurado, Francisco Gabriel
dc.contributor.author Valero, José
dc.date.accessioned 2022-05-02T13:40:08Z
dc.date.available 2022-05-02T13:40:08Z
dc.date.issued 2021
dc.identifier.uri https://hdl.handle.net/10550/82500
dc.description.abstract In this paper, we apply resampling techniques to a modified compartmental SEIR model which takes into account the existence of undetected infected people in an epidemic. In particular, we implement numerical simulations for the evolution of the first wave of the COVID-19 pandemic in Spain in 2020. We show, by using suitable measures of goodness, that the point estimates obtained by the bootstrap samples improve the ones of the original data. For example, the relative error of detected currently infected people is equal to 0.061 for the initial estimates, while it is reduced to 0.0538 for the mean over all bootstrap estimated series.
dc.relation.ispartof European Physical Journal Plus, 2021, vol. 136, num. 10, p. 1067
dc.rights.uri info:eu-repo/semantics/openAccess
dc.source Morillas Jurado, Francisco Gabriel Valero, José 2021 Random resampling numerical simulations applied to a SEIR compartmental model European Physical Journal Plus 136 10 1067
dc.subject Economia de la salut
dc.subject Salut pública
dc.title Random resampling numerical simulations applied to a SEIR compartmental model
dc.type info:eu-repo/semantics/article
dc.date.updated 2022-05-02T13:40:09Z
dc.identifier.doi https://doi.org/10.1140/epjp/s13360-021-02003-9
dc.identifier.idgrec 150207

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