Table 5.
Summary of statistical and machine learning methods and data sources for forecasting using Twitter data.
| Public Health Issue | Method | Complementary Data |
|---|---|---|
| Cancer | Simple Statistical Analysis [23], Linear Regression [99] | CDC |
| E Coli | Latent Dirichlet Allocation [31], Lexicon Analysis [31] | Robert Koch Institute |
| Vomiting | TSVM [22], ARIMA (Autoregressive Integrated Moving Average) [22] | Public Health England |
| Gastroenteritis | TSVM [22], Latent Dirichlet Allocation [31], Lexicon Analysis [31], ARIMA (Autoregressive Integrated Moving Average) [22] | Public Health England, Robert Koch Institute |
| Asthma | Decision Tree [95], Shallow MLP [95] | Children's Medical Center (CMC) |
| Influenze-like Illnesses (H1N1) | Support Vector Regression [100] | CDC |
| Influenze-like Illnesses | Deep Learning (RNN) [36], Deep Learning (MLP) [40], Fasttext [36], Deep Learning (CNN) [36], ARIMA (Autoregressive Integrated Moving Average) [22,97], GloVe [36], Temporal Topic Model [14], Dynamic Regression [96], TSVM [22], Partial Differential Equation [44], Simple Statistical Analysis [23], Autoregressive Moving Average (ARMA) [45] | Boston Public Health Commission, Public Health England, Pan American Health Organization (PAHO), Chinese CDC, CDC |
| General Healtha | Temporal Ailment Topic Aspect Model (TM-ATAM) [51] | CDC |
| Dengue | Simple Statistical Analysis [53] | Brazilian Official Dengue case data |
| Diarrhoea | TSVM [22], ARIMA (Autoregressive Integrated Moving Average) [22] | Public Health England |
a
Generic feelings of unwellness and non-specific illness.