A phenomenological model for COVID-19 data taking into account neighboring-provinces effect and random noise
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INVESTIGACIONMetadatos
Título
A phenomenological model for COVID-19 data taking into account neighboring-provinces effect and random noiseFecha de publicación
2022Editor
WileyISSN
0039-0402; 1467-9574Cita bibliográfica
CALATAYUD, Julia; JORNET, Marc; MATEU, Jorge. A phenomenological model for COVID‐19 data taking into account neighboring‐provinces effect and random noise. Statistica Neerlandica, 2023, 77, 2, p. 146-155Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://onlinelibrary.wiley.com/doi/full/10.1111/stan.12278Versión
info:eu-repo/semantics/submittedVersionPalabras clave / Materias
Resumen
We model the incidence of the COVID-19 disease during the first wave of the epidemic
in Castilla-Leon (Spain). Within-province dynamics may be governed by a generalized logistic
map, but this lacks of spatial ... [+]
We model the incidence of the COVID-19 disease during the first wave of the epidemic
in Castilla-Leon (Spain). Within-province dynamics may be governed by a generalized logistic
map, but this lacks of spatial structure. To couple the provinces, we relate the daily new infec-
tions through a density-independent parameter that entails positive spatial correlation. Pointwise
values of the input parameters are fitted by an optimization procedure. To accommodate the
significant variability in the daily data, with abruptly increasing and decreasing magnitudes, a
random noise is incorporated into the model, whose parameters are calibrated by maximum like-
lihood estimation. The calculated paths of the stochastic response and the probabilistic regions
are in good agreement with the data. [-]
Publicado en
Statistica Neerlandica, 2023, 77, 2Entidad financiadora
Universitat Jaume I | Generalitat Valenciana | Ministerio de Ciencia e Innovación
Código del proyecto o subvención
PID2019‐107392RB‐I00 | AICO/2019/198
Derechos de acceso
"This is the pre-peer reviewed version of the following article: CALATAYUD, Julia; JORNET, Marc; MATEU, Jorge. A phenomenological model for COVID‐19 data taking into account neighboring‐provinces effect and random noise. Statistica Neerlandica, 2023, 77, 2, which has been published in final form at https://doi.org/10.1111/stan.12278. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions."
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info:eu-repo/semantics/openAccess
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/openAccess
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