ABSTRACT:
Using a lagged polynomial regression model, which used COVID-19 data from 2020 with no vaccines, the prediction of COVID-19 was performed in a scenario with vaccine administration for Tucumán in 2021. The modeling included the identification of a contagion breaking point between both series with the best correlation. Previously, the lag that served to obtain the smallest error between the expected and observed values was indicated by means of cross correlation. The validation of the model was carried out with real data. In 21 days, 18,640 COVID-19 cases out of 20,400 reported cases were predicted. The maximum peak of COVID-19 was estimated 21 days in advance with the expected intensity.
Keywords:
Forecasting; Model; COVID-19; Vaccines
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The red line indicates probability. The lag 10 was significant with non-null model residuals.
The R and Polynomial (R) blue lines correspond to real data from COVID-19. The red line P corresponds to COVID-19 data predicted with MRPR. The green line is an Index of Personnel Movement in Supermarkets and Pharmacies (Índice de Movimiento de Personas en Supermercados y Farmacias – IMPSF) in percentage for Tucumán according to the same reference point. All broken lines correspond to grade three polynomials.