ABSTRACT
RESUMEN Fundamento: ante la pandemia provocada por el nuevo coronavirus SARS-CoV-2, resulta importante la estimación del crecimiento de casos infestados y decesos de la población cubana. Objetivo: obtener predicciones para el pico de casos confirmados y fallecidos en Cuba por la COVID- 19, haciendo uso de herramientas estadísticas e informáticas. Métodos: el método de los mínimos cuadrados fue utilizado para la obtención de los parámetros utilizando modelos lineales (MCL) y no lineales (MCNL). Los modelos logísticos y exponenciales, como la curva de crecimiento logístico, utilizada para modelar el crecimiento poblacional (modelos de crecimiento de Gompertz), se aplicaron en el pronóstico del crecimiento de casos infectados y/o decesos respectivamente. Resultados: existe una adecuación de los modelos presentados con respecto a los valores pronosticados y los reales lo cual permite una confiabilidad de los mismos para los pronósticos efectuados para Cuba. Conclusiones: los modelos estadísticos de predicciones obtenidos dan resultados muy significativos para el estudio de la pandemia COVID-19 en Cuba. ABSTRACT Foundation: on the pandemic caused by the new SARS-CoV-2 coronavirus, it is important to estimate the growth of infested cases and deaths of the Cuban population. Objective: to obtain predictions for the peak of confirmed and deceased cases in Cuba by COVID-19, using statistical and computer tools. Methods: the least squares method was used to obtain the parameters using linear (MCL) and nonlinear (MCNL) models. Logistic and exponential models, such as the logistic growth curve, used to model population growth (Gompertz growth models), were applied to the growth prediction of infected cases and / or deaths, respectively. Results: there is an adequacy of the presented models with respect to the predicted and the real values which allow their reliability for the predictions made for Cuba. Conclusions: statistical prediction models obtained give very significant results for the COVID-19 pandemic study in Cuba.
ABSTRACT
RESUMEN Introducción: Cuba ha sido afectada por la COVID-19. Todas las provincias del país han presentado casos confirmados de la enfermedad. Se han llevado a cabo medidas por parte del gobierno y el sistema de salud, para contrarrestar el contagio de persona a persona. Es de gran ayuda contar con estimaciones de casos confirmados para las decisiones. Objetivos: Obtener predicciones para los picos de casos confirmados y cantidad total de estos para algunas provincias de Cuba y para todo el país. Material y Métodos: Estudio de tipo predictivo de curvas de crecimiento poblacional. Se analizan los datos correspondientes a los primeros 52 días de afectación de la enfermedad en el país para estimar los modelos y aplicar el método de los mínimos cuadrados para modelos no lineales con respecto a los parámetros. Se utilizan el coeficiente de determinación ajustado, el criterio de información de Akaike y el error estándar de los residuos para medir la bondad del ajuste de los modelos. Se estudian las provincias del país que presentan una tasa de infectados por cien mil habitantes mayor que 14,71 y el país en su conjunto. Resultados: La bondad de ajuste de los modelos utilizados en las localidades estudiadas y en el país es alta, lo cual permite su confiabilidad para los pronósticos efectuados. Conclusiones: Las predicciones plantean que las cinco localidades analizadas presentan su pico de contagio en abril al igual que Cuba. ABSTRACT Introduction: Cuba and all its provinces have been affected by COVID-19 disease. The government and the health system have taken measures to avoid contagion from person to person. To take these measures it is important to have estimates of the rate of infection. Objectives: To obtain predictions for the peak of infected cases and the total number for some Cuban provinces and the whole country. Material and Methods: Predictive study of population growth curves. Data from the first 52 days of the disease in the country are processed to estimate the models and to apply the method of least squares estimation of nonlinear parameters. The adjusted coefficient of determination, the Akaike information criterion and the standard error of the residuals are used to measure the goodness of fit of the models. The provinces that present a rate of infection per 100,000 inhabitants greater than 14,71 and the country as a whole are studied. Results: The goodness of fit of the models used in the provinces studied and the country is high, which allows them to be reliable for predictions. Conclusions: The predictions suggest that the five provinces analyzed and Cuba show their peak of contagion in April.
ABSTRACT
INTRODUCTION On March 11, 2020, WHO declared COVID-19 a pandemic and called on governments to impose drastic measures to fi ght it. It is vitally important for government health authorities and leaders to have reliable estimates of infected cases and deaths in order to apply the necessary measures with the resources at their disposal. OBJECTIVE Test the validity of the logistic regression and Gompertz curve to forecast peaks of confi rmed cases and deaths in Cuba, as well as total number of cases. METHODS An inferential, predictive study was conducted using lo-gistic and Gompertz growth curves, adjusted with the least squares method and informatics tools for analysis and prediction of growth in COVID-19 cases and deaths. Italy and Spain-countries that have passed the initial peak of infection rates-were studied, and it was inferred from the results of these countries that their models were ap-plicable to Cuba. This hypothesis was tested by applying goodness-of-fi t and signifi cance tests on its parameters.RESULTS Both models showed good fi t, low mean square errors, and all parameters were highly signifi cant. CONCLUSIONS The validity of models was confi rmed based on logis-tic regression and the Gompertz curve to forecast the dates of peak infections and deaths, as well as total number of cases in Cuba. KEYWORDS COVID-19, SARS-CoV-2, logistic models, pandemic, mortality, Cuba.