Making up the Complete Matrix from the Incomplete Matrix Using the EM-type IRT and Its Application
Sakumura, Takenori - Chuo University
Tokunaga, Masakazu - KIS Co. Ltd.
Hirose, Hideo - Hiroshima Institute of Technology
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.
item response theory, incomplete matrix, EM-type IRT, adaptive test, matrix decomposition method, calibration
The Information Processing Society of Japan: Transactions on Mathematical Modeling and its Applications (TOM), Volume 7, Number 2, Pages 17–26, November 2014.