  In this article, we focus on the analysis of the potential factors driving
the spread of influenza, and possible policies to mitigate the adverse effects
of 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 influenza
behavior. Then we collect a massive number of possible region-wise indicators
contributing 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 dimensional
indicators using a combination of data analysis techniques, including matrix
completion, support vector machines (SVM), autoencoders, and principal
component analysis (PCA). Furthermore, we model the international flow of
migration and trade as a convolution on regional influenza activity, and solve
the deconvolution problem as higher-order perturbations to the linear
regression, thus separating regional and international factors related to the
influenza mortality. Finally, both the original model and the perturbed model
are tested on regional examples, as validations of our models. Pertaining to
the policy, we make a proposal based on the connectivity data along with the
previously extracted significant features to alleviate the impact of influenza,
as well as efficiently propagate and carry out the policies. We conclude that
environmental features and economic features are of significance to the
influenza mortality. The model can be easily adapted to model other types of
infectious diseases.
