A seasonal infectious disease spread prediction method by using the singular-value decomposition


H. Hirose


The First BMIRC International Symposium on Frontiers in Computational Systems Biology and Bioengineering, February 28 - March 1, 2013, Fukuoka, Japan


Prediction methods for infectious disease spread have been dealt with from a variety of mathematical approaches. Among them are 1) the SIR/SEIR model (ordinary/stochastic differential equations), 2) statistical model (likelihood approach with conditional probability), 3) agent-based model, and 4) the internet-used model. Here, we propose a new method for the seasonal infectious disease spread prediction method by using the singular-value decomposition (SVD).
The SVD 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 presentation, we apply the SVD 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. Comparing the root mean squared error between the predicted and observed data, we have found that the proposed method shows the superiority over the conventional methods using the method of artificial neural networks. To demonstrate the advantageous point and effectiveness of the SVD 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
SIR; stochastic differential equation; pandemic; SARS;



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