Early detection of disease outbreaks and non-outbreaks using incidence data: A framework using feature-based time series classification and machine learning

doi:10.1371/journal.pcbi.1012782

Early detection of disease outbreaks and non-outbreaks using incidence data: A framework using feature-based time series classification and machine learning

Fig 5

Illustration in Expanding window experiment settings and AUC score at different reiterations.

A: Expanding window settings on and . We fix the length of D as 30, then expand L until reaching the left boundary of sliced data. The initial length of L is 5. B-I: AUC scores depicting the classification performance of the classifier trained from changing gaps D between the subsequence and transition points. Two feature extraction methods (22SF, 22 statistical features; 5EWSI, 5 early warning signal indicators) are put in two rows and four predictive models (GBM, gradient boosting machine; LRM, logistic regression model; KNN, k-nearest neighbor; SVM, support vector machine) are presented in four columns.

doi: https://doi.org/10.1371/journal.pcbi.1012782.g005