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.
