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Seasonal Infectious Disease Spread Prediction
via the Large Scale Matrix Approach
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Takeru Kiyosue, Hideo Hirose
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2nd International Symposium on Applied Engineering and Sciences
(SAES2014), Big Data Session 1, December 20-21, 2014, Fukuoka,
Japan
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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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Times Cited in Web of Science:
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