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RESUMEN Introducción: Los estudios basados en modelos estadísticos juegan un papel importante para las predicciones sobre la COVID-19. Objetivo: Realizar un análisis de modelación estadística combinando 6 modelos de pronósticos para predecir la aparición de casos positivos diarios, activos y fallecidos por COVID-19 en Cuba. Métodos: Se utilizaron los datos reportados diariamente del 11 de marzo al 25 de mayo publicados en el sitio web CUBADEBATE. A los modelos propuestos se les calculó el desempeño mediante los estadísticos: MAE, RMSE, MAPE y ME así como el análisis de residuales. Resultados: Los modelos A y B dan una tendencia constante de 8 y 9 casos positivos respectivamente para el día 22 de julio. El modelo C indica una ligera disminución de los casos con 4 ese mismo día y el modelo D una tendencia al aumento con 19 casos. . El modelo E refleja un mínimo de 126 casos el día 3 de junio y luego un aumento de los casos hasta alcanzar el 22 de julio un valor de 374 casos activos hospitalizados. En el modelo F se apreció una tendencia a mantenerse constante el número de fallecidos por encima de 80 casos en la primera quincena de julio. Conclusiones: Los 6 modelos estudiados cumplen con las pruebas estadísticas, de desempeño y residuales. Sus datos proporcionan un pronóstico para la COVID-2019, representando una herramienta válida.
ABSTRACT Introduction: Studies based on statistical models play an important role for predictions about COVID-19. Objective: To carry out a statistical modeling analysis combining 6 forecast models to predict the appearance of daily positive cases, active and deceased by COVID-19 in Cuba. Method: Data reported daily from March 11 to May 25 from the CUBADEBATE website were used, which were processed and analyzed. The performance of the models was calculated: Mean absolute error (MAE), root of the mean square error (RMSE), percent of mean absolute error (MAPE) and the mean error (ME) as well as the residual analysis. Results: Models A and B gave a constant trend between 8 and 9 cases of until July 22. Model C indicated a decrease in cases with 4 that same day and model D indicated a raise to 19 cases. Model E indicated a minimum of 126 cases on June E and then a raise to 374 hospitalized cases. Deceases cases had a constant tendency in deceases numbers above of 80 cases in first 15 days of July. Conclusions: The 6 models studied meet the statistical , performance and residual tests. Their data provides a forecast for COVID2019, representing a valid tool.
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Aims: The forecast is a topical subject, which aid in decision making and its performance. The aim of this study is to predict the disease between 1995 and 2010. Place and Duration of Study: The choice of the disease is of after its appearance in our survey in the region of Gharb. Time series were illustrated between 1988-1994. Regional cholera annual data reported from ministry of health of Morocco. Methods: The comparison of four models by the analysis of the series of cholera cases includes examining graphic series by using EVIEWS software, the consideration of the autocorrelation and partial autocorrelation functions, define the model that suits, estimate it, diagnose, the residue analysis and compare the four models to choose the best for use in the forecasting process. Except the stationary series, we used IBMSPSS V22 for the other steps. Results: Throughout this work, it is assumed that the underlying structure of the series follows an autoregressive integrated moving average (ARIMA) process. It is presumed that observations of the disease follow an autoregressive moving average process of order AR (1) and therefore ARIMA (1, 1, 0). The comparison of models of time series is extended away by using the statistics fit of the model: MAPE, BIC and R-squared, in addition to the sig. of the parameters and the analysis of residues by Ljung-Box and Durbinwatson statistic. The validation of the series is estimated by the calculation of the Mean Absolute Percentage Error (MAPE) and the signification of the parameter with P =0,05. Conclusion: Brown model is the model of choice for the prediction of cholera cases.