| Popis: |
From a public health perspective, tobacco use is addictive by nature and triggers several cancers, cardiovascular and respiratory diseases, reproductive disorders, and many other adverse health effects leading to many deaths. In this context, the need to eradicate tobacco-related health problems and the increasingly complex environments of tobacco research require sophisticated analytical methods to handle large amounts of data and perform highly specialized tasks. In this study, time series models are used: autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) to forecast the impact of COVID-19 on sales of cigarette in Spanish provinces. To find the optimal solution, initial combinations of model parameters automatically selected the ARIMA model, followed by finding the optimized model parameters based on the best fit between the predictions and the test data. The analytical tools Autocorrelation Function (ACF), Partial Autocorrelation Function (PACF), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) were used to assess the reliability of the models. The evaluation metrics that are used as criteria to select the best model are: mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), mean percentage error (MPE), mean error (ME) and mean absolute standardized error (MASE). The results show that the national average impact is slight. However, in border provinces with France or with a high influx of tourists, a strong impact of COVID-19 on tobacco sales has been observed. In addition, the least impact has been observed in border provinces with Gibraltar. Policymakers need to make the right decisions about the tobacco price differentials that are observed between neighboring European countries when there is constant and abundant cross-border human transit. To keep smoking under control, all countries must make harmonized decisions. |
