Matrix Approach for the Seasonal Infectious Disease Spread Prediction

Hideo HIROSE  Masakazu TOKUNAGA  Takenori SAKUMURA  Junaida SULAIMAN  Herdianti DARWIS  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E98-A   No.10   pp.2010-2017
Publication Date: 2015/10/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E98.A.2010
Type of Manuscript: Special Section PAPER (Special Section on Recent Developments on Reliability, Maintainability and Dependability)
seasonal infectious disease,  matrix decomposition,  item response theory,  ARIMA,  artificial neural network,  

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Prediction of 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 the one method which is 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 compare the results obtained by using two conventional methods in forecasting, ARIMA and ANN. We have found that the matrix approach is simple and useful in prediction for the seasonal infectious disease spread.