  Epidemic is a rapid and wide spread of infectious disease threatening many
lives and economy damages. It is important to fore-tell the epidemic lifetime
so to decide on timely and remedic actions. These measures include closing
borders, schools, suspending community services and commuters. Resuming such
curfews depends on the momentum of the outbreak and its rate of decay. Being
able to accurately forecast the fate of an epidemic is an extremely important
but difficult task. Due to limited knowledge of the novel disease, the high
uncertainty involved and the complex societal-political factors that influence
the widespread of the new virus, any forecast is anything but reliable. Another
factor is the insufficient amount of available data. Data samples are often
scarce when an epidemic just started. With only few training samples on hand,
finding a forecasting model which offers forecast at the best efforts is a big
challenge in machine learning. In the past, three popular methods have been
proposed, they include 1) augmenting the existing little data, 2) using a panel
selection to pick the best forecasting model from several models, and 3)
fine-tuning the parameters of an individual forecastingmodel for the highest
possible accuracy. In this paper, a methodology that embraces these three
virtues of data mining from a small dataset is proposed...
