




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.121126, January
2931, 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. 




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








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