A phenomenological model for COVID-19 data taking into account neighboring-provinces effect and random noise
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comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
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INVESTIGACIONMetadata
Title
A phenomenological model for COVID-19 data taking into account neighboring-provinces effect and random noiseDate
2022Publisher
WileyISSN
0039-0402; 1467-9574Bibliographic citation
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. 146-155Type
info:eu-repo/semantics/articlePublisher version
https://onlinelibrary.wiley.com/doi/full/10.1111/stan.12278Version
info:eu-repo/semantics/submittedVersionSubject
Abstract
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. [-]
Is part of
Statistica Neerlandica, 2023, 77, 2Funder Name
Universitat Jaume I | Generalitat Valenciana | Ministerio de Ciencia e Innovación
Project code
PID2019‐107392RB‐I00 | AICO/2019/198
Rights
"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
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