A phenomenological model for COVID-19 data taking into account neighboring-provinces effect and random noise.
Stat Neerl
; 2022 Oct 05.
Article
in English
| MEDLINE | ID: covidwho-2253518
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 structure. To couple the provinces, we relate the daily new infections 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 likelihood estimation. The calculated paths of the stochastic response and the probabilistic regions are in good agreement with the data.
Full text:
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Collection:
International databases
Database:
MEDLINE
Type of study:
Experimental Studies
/
Observational study
/
Qualitative research
/
Randomized controlled trials
Language:
English
Year:
2022
Document Type:
Article
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