Algorithm 1.

Classification

Inputs: epidemic curves on day j

Outputs: predicted cluster on day j

Read in all epidemic curves in training set

Loop over days

for (each day j) do

Train each supervised learning method using all curves in the training set for (each epidemic in the test set) do

Predict the epidemic cluster using each method

end for

Estimate error εi,j: mean misclassification error on day j for method i

end for

For weighted classifiers

Using validation data set estimate weights: 2εi,j−1, 2εi,j−3, and exp (2εi,j−1)

for (each day j) do

for (each epidemic in the test set) do

Predict the epidemic cluster using each method and consider each prediction as a single vote

Assign appropriate weights to each method

Sum the weighted votes assigned to each cluster

Predict the epidemic cluster based on the majority weighted vote

end for

end for