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ZENODO
Article . 2023
License: CC BY NC
Data sources: ZENODO
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ZENODO
Article . 2023
License: CC BY NC
Data sources: Datacite
ZENODO
Article . 2023
License: CC BY NC
Data sources: Datacite
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The Analysis of Mortality Data, by Logistic and Compartmental Models, In Multiple Countries. Is It An Approach To Estimate How The Covid-19 Pandemic Could Last?

Authors: Boselli, Pietro Marco; Soriano del Castillo, José Miguel;

The Analysis of Mortality Data, by Logistic and Compartmental Models, In Multiple Countries. Is It An Approach To Estimate How The Covid-19 Pandemic Could Last?

Abstract

Currently, mathematical modeling plays a pivotal role in comprehending and examining the intricacies of the COVID-19 pandemic. This brief report uses official information from WHO to utilize a logistic and compartmental model in the COVID pandemic, applied across twelve countries, to infer the mortality asymptote, total deviance, and the moment from which the final period of the pandemic begins epidemic duration in order to estimate the duration of this pandemic. Our results based on the analysis of mortality data reflecting that can be conventionally 95% inferred to that the completion of the epidemic could ended in Spain (November 2022), South Africa (2023 February), Egypt (April 2023), France and Italy (June 2023), China (September 2023), Russia (November 2023), India (December 2023), USA (February 2024), Japan (July 2024), Israel (August 2024) and Germany (January 2025).

Country
Spain
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Keywords

COVID-19; end date of epidemic; logistic model; compartmental model, anàlisi matemàtica, medicina

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green
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