| Ayyoubzadeh et al. (2020) |
LSTM & Linear regression |
LSTM: RMSE 27.187 |
Predict COVID-19 positive cases in Iran |
The algorithm can predict the trend of the COVID-19 pandemic in Iran, which can help policymakers to plan for the allocation of medical resources |
| Linear Regression: RMSE 7.562 |
| Chimmula & Zhang (2020) |
LSTM |
RMSE: 34.83 |
Forecast COVID-19 transmission in Canada |
Help decision-makers in monitoring and curtailing future transmission of COVID-19 in Canada |
| Accuracy: 92.6% |
| Liu et al. (2020b) |
ANN |
Not Applicable |
Estimated the trend of COVID-19 in China |
Help policymakers and health officials attend to the need of other diseases during the COVID-19 pandemic |
| Ribeiro et al. (2020) |
Support vector regression |
MAE: 79.17 |
Provide future COVID-19 confirmed cases in brazil |
monitoring COVID-19 cases in Brazil and help decision-makers in taken critical decision about COVID-19 |
| Tiwari, Kumar & Guleria (2020) |
Machine learning algorithm (not specified) |
MAE & RSME—graphical |
The predicted peak period of COVID-19 in India |
Help India policymakers decide on COVID-19 to mitigate its spread |
| Tuli et al. (2020) |
Machine learning algorithm (not specified) |
MSE: 9.32E+06 |
Provide real life COVID-19 predictions |
Government and citizens can use the results for proactive measures to fight COVID-19 |
| Vaid, Cakan & Bhandari (2020) |
Machine learning algorithm (not specified) |
Not reported |
Predict Potential COVID-19 infections |
Policymakers in North America can use the projection to curtail the effect of COVID-19 pandemic |
| Yang et al. (2020b) |
LSTM |
Confidence Interval: 95% |
Predict COVID-19 trend in China |
Authorities in China to decide to control the COVID-19 pandemic |
| Pirouz et al. (2020) |
Group method of data handling neural network |
Accuracy: 85.7% |
Predict COVID-19 pandemic based on weather condition |
Help in managing COVID-19 pandemic |
