Seasonal Infectious Disease Spread Prediction via the Large Scale Matrix Approach


Takeru Kiyosue, Hideo Hirose


2nd International Symposium on Applied Engineering and Sciences (SAES2014), Big Data Session 1, December 20-21, 2014, Fukuoka, Japan

Prediction of the seasonal infectious disease spread is traditionally dealt with as a function of time. Typical methods are time series analysis such as ARIMA (autoregressive, integrated, and moving average) or ANN (artificial neural networks). However, if we regard the time series data as the matrix form, e.g., consisting of yearly magnitude in row and weekly trend in column, we may expect to use a different method (matrix approach) to predict the disease spread when seasonality is dominant. The MD (matrix decomposition) method is one of the methods used in recommendation systems. The other is the IRT (item response theory) used in ability evaluation systems.
In this paper, we apply these two methods to predict the disease spread in the case of infectious gastroenteritis caused by Norovirus in Japan and others, and compare the results obtained by using the ARIMA and the ANN.
We have found that the matrix approach is simple and useful in prediction for the seasonal infectious disease spread.

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