Seasonal Infectious Disease Spread Prediction Using Matrix Decomposition Method


H. Hirose, T. Nakazono, M. Tokunaga, T. Sakumura, S.M. Sumi, J. Sulaiman


the 4th International Conference on Intelligent Systems, Modelling and Simulation (ISMS 2013), pp.121-126, January 29-31, 2013, Bangkok, Thailand


The matrix decomposition is one of the most powerful methods in recommendation systems. In the recommendation system, we can assume an incomplete matrix consisted of observed evaluation values by users and items, then we predict the vacant elements of the matrix using the observed values. This method is applied to a variety of the fields, e.g., for movie recommendations, music recommendations, book recommendations, etc. In this paper, we apply the matrix decomposition to predict the seasonal infectious disease spread. Applying the method to the case of infectious gastroenteritis caused by Norovirus in Japan, we have found that the early detection and prediction for the prevalence of the disease spread can be expected accurately. The infectious disease spread prediction using the matrix decomposition is new.
To demonstrate the advantageous point and effectiveness of the matrix decomposition method, we applied the method to the influenza spread prediction in Japan, where missing observations are admitted for computation unlike other prediction methods.

Key Words
matrix decomposition; recommendation system; disease spread; Norovirus; influenza; early detection; artificial neural networks; ensemble;



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