In
observing the gradual increase of patients caused by infectious
diseases or the increase of the number of failures of equipment,
we anticipate the disease spread widely, and 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 is commonly
used even when the size of observed data is small. Other methods,
such as the ordinary differential equation model (ODE), statistical
truncated model are also useful to estimate the final number of
infected patients. These methods are also applicable to find the
increase of the number of failures.
The predicted value for the final number of patients using data until
time T becomes a function (trend) of T. We call this Lplot. We,
here, consider the use of the Lplot to predict the final number
of patients. So far, we have been discussing about the better predictor
in the sense that the newly proposed method is superior to other
conventional methods. However, in this paper, we try to use all the
methods already proposed, and to make a better result than that by
using a single method. That is, we will make a prediction using the
predicted values already obtained. We call this methodology the PoP,
the prediction on predictions.
We here define the decay function using the Lplot. We also propose
to use the ensemble method, accepting the majority vote. The PoP
in this presentation includes the simple mean value, the decay function,
and the ensemble method. By applying the method to the SARS case,
we have found that it worked well for early prediction of infectious
disease spread. 





