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In this article, we focus on the analysis of the potential factors drivingthe spread of influenza, and possible policies to mitigate the adverse effectsof the disease. To be precise, we first invoke discrete Fourier transform (DFT)to conclude a yearly periodic regional structure in the influenza activity,thus safely restricting ourselves to the analysis of the yearly influenzabehavior. Then we collect a massive number of possible region-wise indicatorscontributing to the influenza mortality, such as consumption, immunization,sanitation, water quality, and other indicators from external data, with $1170$dimensions in total. We extract significant features from the high dimensionalindicators using a combination of data analysis techniques, including matrixcompletion, support vector machines (SVM), autoencoders, and principalcomponent analysis (PCA). Furthermore, we model the international flow ofmigration and trade as a convolution on regional influenza activity, and solvethe deconvolution problem as higher-order perturbations to the linearregression, thus separating regional and international factors related to theinfluenza mortality. Finally, both the original model and the perturbed modelare tested on regional examples, as validations of our models. Pertaining tothe policy, we make a proposal based on the connectivity data along with thepreviously extracted significant features to alleviate the impact of influenza,as well as efficiently propagate and carry out the policies. We conclude thatenvironmental features and economic features are of significance to theinfluenza mortality. The model can be easily adapted to model other types ofinfectious diseases.

Influenza Modeling Based on Massive Feature Engineering and International Flow Deconvolution