In observing the widely spread of patients caused by infectious
diseases or the increase of the number of failures of equipments,
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 Lplot is developed here, which is
to plot the predicted final value at the truncation time. We
consider the use of the Lplot 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.





PoP, Pandemic, SIR model, ordinary differ
ential equation model, statistical truncated model, generalized
logistic distribution, ensemble method, Lplot, decay function,
restricted RMSE, pandemic, SIR model, ordinary differential
equation model, statistical truncated model, ensemble method.


