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Early Risk Detection Analysis using the PoP,
Prediction on Predictions
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Yuki, Koyanagi, 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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We
deal with hear the risk analysis mainly regarding to the infectious
disease spread. In observing the widely spread of patients caused
by infectious diseases or the increase of the number of failures
of equipment, it is crucial to predict the final number of infected
patients or failures at earlier stages. To estimate the number
of infected patients, the SIR model, the ordinary differential
equation model, statistical truncated model are useful.
The predicted value for the final number of patients using data until
truncation time T becomes a function (trend) of T. To grasp the prediction
trend with truncation time, the L-plot is developed here, which is
to plot the predicted final value at the truncation time. We consider
the use of the L-plot to predict the final number of patients. For
example, we have shown to use the decay function. Applying the multiple
methodologies to the same data, we can expect better predicted values.
This is called the PoP, the prediction on predictions. As one of
the PoP method, we propose to use the ensemble method. By applying
these methods to the SARS case, we have found that the ensemble method
works well as a PoP method. |
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Times Cited in Web of Science:
Cited in Books:
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