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dc.contributor.authorMarín-Machuca, Olegario
dc.contributor.authorHumala-Caycho, Yuri Esquilo
dc.contributor.authorChinchay-Barragán, Carlos Enrique
dc.contributor.authorYataco-Velásquez, Luis Andrés
dc.contributor.authorRojas Rueda, María del Pilar
dc.contributor.authorBonilla-Ferreyra, Jorge Luis
dc.contributor.authorPerez-Ton, Luis Adolfo
dc.contributor.authorMarín-Sánchez, Obert
dc.date.accessioned2025-02-27T22:54:01Z
dc.date.available2025-02-27T22:54:01Z
dc.date.issued2025-02-26
dc.identifier.urihttps://hdl.handle.net/20.500.13067/3694
dc.description.abstractObjective. Determine was mathematically modeled using the expression𝑁=𝑀(1+𝑄×𝑒−𝑘×𝑡)⁄, which is a predictive equation. Using this model, the number of deaths due to COVID-19 worldwide was estimated.Design. Correlational, prospective, predictive and transversal study. Participans.The data on deceased individuals due to the COVID-19 disease up to November 5, 2022, was considered. Main measurement.This data was used to analyze the pandemic dispersion, which was determined to exhibit logistic sigmoidal behavior. By deriving Equation 3, the rate of deaths due to COVID-19 worldwide was calculated, obtaining the predictive model represented in Figure 3.Results. Using Equation (5), the critical time𝑡𝑐=447𝑑𝑎𝑦𝑠and the maximum speed (𝑑𝑁̂𝑑𝑡)𝑚á𝑥=1525028,553𝑝𝑒𝑟𝑠𝑜𝑛𝑠/𝑑𝑎𝑦and the date when the global death rate due to COVID-19 reached its maximum was July 6, 2021. The Pearson correlation coefficient between the elapsed time (𝑡) and the number of deceased individuals (𝑁) worldwide, based on 33 cases, was𝑟=−0,9365. Conclusions.This indicates that the relationship between elapsed time and the number of deceased individuals is real, with no significant difference, showing that the predictive model provides a high estimation of the correlated data.There is a "very strong correlation" between elapsed time (𝑡)and the number of deceased individuals (𝑁)with 87,7 % of the variance in 𝑁explained by 𝑡, ue to the COVID-19 disease. These models help us predict the behavior of disease like COVID-19.es_PE
dc.formatapplication/pdfes_PE
dc.language.isoenges_PE
dc.publisherLearning Gatees_PE
dc.rightsinfo:eu-repo/semantics/openAccesses_PE
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/es_PE
dc.subjectCOVID-19 diseasees_PE
dc.subjectEstimationes_PE
dc.subjectGlobal fatalitieses_PE
dc.subjectLogistic modelinges_PE
dc.subjectValidationes_PE
dc.titleMathematical modeling of global covid-19 fatalitieses_PE
dc.typeinfo:eu-repo/semantics/articlees_PE
dc.identifier.journalEdelweiss Applied Science and Technologyes_PE
dc.identifier.doihttps://doi.org/10.55214/25768484.v8i6.3687
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#2.02.00es_PE
dc.publisher.countryPEes_PE
dc.source.volume8es_PE
dc.source.issue6es_PE
dc.source.beginpage7782es_PE
dc.source.endpage7790es_PE


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