Table 2 Summary of the reviewed methods and techniques
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] |