Forecast accuracy of COVID-19 spread dynamics in Peru - Córdova Sotomayor, Daniel Angel (2020)
Objective: To analyze the forecast accuracy of Brown's exponential smoothing model to predict the spread of COVID-19 in Peru from March 6 to May 30, 2020. Materials and methods: A descriptive study based on a time series analysis conducted from March 6 to May 30, 2020 in Peru. The information on the number of positive cases of COVID-19 (155,671 people) was used. The prediction method was Brown's exponential smoothing model, which consists in carrying out two exponential smoothings from which the forecast is calculated: the time series values were used in the first smoothing, and the first attenuation series was used in the second one. Accuracy measures used in the research were: mean forecast error (MFE), mean squared error (MSE), mean absolute deviation (MAD) and mean absolute percentage error (MAPE). The coefficient of determination (R2) was used to establish if the data fits the evaluated model. Results: MFE was 156.7, MSE was 506461.3, MAD was 450.6 and MAPE was 9.03 %. R2 accounted for 0.8078. Conclusions: Accuracy error or MAPE was 9.03 % and R2 was 0.8078, which indicates that the data fits by 80.78 % to the evaluated model.