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) agentbased
model, and 4) the internetused model. Here, we propose a new method
for the seasonal infectious disease spread prediction method by
using the singularvalue 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. 




SIR;
stochastic differential equation; pandemic; SARS; 

