Table 6.
Summary of statistical and machine learning methods and data sources for disease tracking using Twitter data.
| Public Health Issue | Method | Complementary Data |
|---|---|---|
| Measles | Semantic Network Analysis [101] | CDC |
| Influenze-like Illnesses (Hemophilus) | Bayesian Inference [15] | Genbank |
| Influenze-like Illnesses (H1N1) | Semi-Superviseddeep Learning (MLP) [104], Support Vector Regression [100] | CDC |
| Influenze-like Illnesses | Bayesian Inference [39], Bayesian Inference [41], Dynamic Regression [96], Maximum Entropy [41] | FluWatch, Boston Public Health Commission, Chinese CDC |
| General Healtha | Temporal Ailment Topic Aspect Model (TM-ATAM) [51] | CDC |
| Dengue | Time-Series Susceptible-Infected-Recovered Model [103], Simple Statistical Analysis [53] | Brazilian Official Dengue case data |
| Miscellaneous | Gaussian Mixture Regression (Gmr) [102] | Map data |
a
Generic feelings of unwellness and non-specific illness.