Explainable influenza forecasting scheme using DCC-based feature selection

As influenza is easily converted to another type of virus and spreads very quickly from person to person, it is more likely to develop into a pandemic. Even though vaccines are the most effective way to prevent influenza, it takes a lot of time to produce them. Due to this, there has been an imbalance in the supply and demand of influenza vaccines every year. For a smooth vaccine supply, it is necessary to accurately forecast vaccine demand at least three to six months in advance. So far, many machine learning-based predictive models have shown excellent performance. However, their use was limited due to performance deterioration due to inappropriate training data and inability to explain the results. To solve these problems, in this paper, we propose an explainable influenza forecasting model. In particular, the model selects highly related data based on the distance correlation coefficient for effective training and explains the prediction results using shapley additive explanations. We evaluated its performance through extensive experiments. We report some of the results.