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Revista Cubana de Investigaciones Biomedicas ; 40(2), 2021.
Article in Spanish | Scopus | ID: covidwho-1391225


Introduction: Since March 2020, Cuba has been affected by SARS-CoV-2, a highly infectious coronavirus that causes COVID-19. In COVID-19 a set of associated symptoms is presented and its evolution can be influenced by the presence of certain personal pathological antecedents in the host. Objective: To identify through principal components the grouping of clinical variables in cases with COVID-19 in Santiago de Cuba province, Cuba. Methods: We conducted an observational, descriptive and transversal study. The study population consisted of the 49 confirmed cases with COVID-19 in the province of Santiago de Cuba. Ten clinical variables were selected: nine related to symptoms and personal pathological history, and one to the state “deceased”. Principal component analysis was applied as a statistical technique. Results: Variables were represented at the level of the first two principal components. The first component was associated to symptoms and the second component to personal pathological antecedents not associated to the respiratory system. This representation revealed that variables leading to an unfavorable evolution of cases were located in the first and fourth quadrants of the plane, being remarkable for those located in the fourth quadrant. The second and third quadrants were indicators of the favorable evolution, being marked in the second quadrant. Conclusions: The principal component analysis groups the clinical variables and corroborates that personal pathological antecedents have an essential role in the unfavorable evolution of patients with COVID-19. © 2021, Editorial Ciencias Medicas. All rights reserved.

Revista Mexicana De Fisica ; 67(1):123-136, 2021.
Article in English | Web of Science | ID: covidwho-1059946


In the province of Santiago de Cuba, Cuba, the COVID-19 epidemic has a limited progression that shows an early small-number peak of infections. Most published mathematical models fit data with high numbers of confirmed cases. In contrast, small numbers of cases make it difficult to predict the course of the epidemic. We present two known models adapted to capture the noisy dynamics of COVID-19 in the Santiago de Cuba province. Parameters of both models were estimated using the approximate-Bayesian-computation framework with dedicated error laws. One parameter of each model was updated on key dates of travel restrictions. Both models approximately predicted the infection peak and the end of the COVID-19 epidemic in Santiago de Cuba. The first model predicted 57 reported cases and 16 unreported cases. Additionally, it estimated six initially exposed persons. The second model forecasted 51 confirmed cases at the end of the epidemic. In conclusion, an opportune epidemiological investigation, along with the low number of initially exposed individuals, might partly explain the favorable evolution of the COVID-19 epidemic in Santiago de Cuba. With the available data, the simplest model predicted the epidemic evolution with greater precision, and the more complex model helped to explain the epidemic phenomenology.