From: A review of influenza detection and prediction through social networking sites
Method category | Method name | Study reference | Performance metric | Metric value |
---|---|---|---|---|
Graph data mining | Graph data Mining | [23] | Pearson correlation | r = 0.545 |
Text mining | Historical patterns | [45] | The precision for 1-day prediction is 0.8 (with mean of 0.52) and 0.6 (with mean of 0.46) for 7-days prediction. | |
Co-occurrences | [44] | |||
Topic models | ATAM | [46] | Pearson correlation | r = 0.934 |
ATAM+ | [47] | Pearson correlation | r = 0.958 | |
HFSTM | [48] | Mean square error (MSE) | MSE = 40.67 | |
Machine learning | Neural network | [61] | ACC (Eq. 8) | ACC = 0.9532 |
SVM | [57] | Pearson correlation | r = 0.93 | |
[56] | Pearson correlation | r = 0.89 | ||
[59] | Pearson correlation | r = 0.89 | ||
[58] | ||||
[60] | Pearson correlation | r = 0.9897 | ||
[55] | ||||
Prediction Market using SVR | [64] | |||
Naive Bayes | [63] | Sentiment polarity is used to determine the accuracy of the used method (Naive Bayes polarity is 70%) | ||
Math/Statistical based models | SNEFT | [67] | Pearson correlation | r = 0.9846 |
ACF | [65] | Pearson correlation | r = 0.767 | |
Numerical-based analysis (SEHA using BOW) | [68] | RMSE | Avg (RMSE) = 1.1 | |
Mechanistic disease models | Metpopulation model | [70] | Pearson correlation | r = 0.98 |
Compartmental model | [35] | |||
Agent-based model | [73] | |||
Keys/Documents filtration | Keys/Documents filtration | [74] |