# Short-Term Prediction of COVID-19 Cases Using Machine Learning Models

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## Abstract

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## 1. Introduction

- 1
- The number of COVID-19 infected people is currently still increasing inBangladesh but there are only incomplete data on the number and location of cases. There are many strategies for reducing the spread that could be implemented by the local authorities but a lack of understanding of the spreading pattern hinders their design and implementation. We therefore designed software based modeling that can be trained to perform short-term forecasting.
- 2
- Many previous studies have considered many epidemiological models where several pandemic parameters depended on the rate of social mixing of people. In the current circumstances in Bangladesh, we cannot determine or measure such parameters precisely. Therefore, various machine learning models can be a useful approach to forecast infectious cases without needing such parameter precision. However, it is important to note that predictions of infection levels are sensitive to non-linear changes of parameters so that long term prediction tends to give poor results. For this reason, we have focused on implementing short-term forecasting models where accuracy is more likely to be achieved.
- 3
- The analysis with different sliding windows (rounds) helps to estimate the prediction capability of individual machine learning models and assist in the exploration of the best models that provide the most accurate predictions. This model will assist governmental authorities to take more effective steps against COVID-19 spread and fatalities.
- 4
- Cloud based web mining makes it possible to achieve fast and feasible to get real-time outcomes.

## 2. Materials and Methods

#### 2.1. Data Description

#### 2.2. Regression Methods

#### Hyperparameter Tuning

#### 2.3. Cloud Based Services

#### 2.4. Evaluation Metrics

#### 2.4.1. Root Mean Square Error (RMSE)

#### 2.4.2. Mean Absolute Error (MAE)

#### 2.4.3. R-Squared

#### 2.5. Cloud Based Short Term Forecasting Model: Epidemic Analysis

- Firstly, this web tool gathers the daily cumulative instances of confirmed infection and fatality cases of Bangladesh at the Github repository of the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (see Section 2.1).
- In this work, we gathered the daily cumulative instances from 8 March 2020 to 28 November 2020 where the confirmed infection and fatality cases were investigated to assess the severity of the pandemic in Bangladesh. This whole period was split into several windows using sliding window techniques [40,41]. The size of sliding window was fixed (32 days) and each of them is called round. Instances were identified from the last 25 days where 85% of the data were used as the training set and 15% of the data were used for the the test set. Thus, it analyzes the confirmed infection and fatality cases and forecasts them for the next couple of days as a requirement. For instance, we predicted the next 7 days cases from the training period in this work. Besides this, we considered 35 rounds where the first 10 and last 5 rounds were presented for the next 7 days of future forecasting.
- The primary web mining model was executed on the Google Colab platform. Raw data were loaded and applied using different machine learning regression models which are described in Section 2.2. To explore the best results, parameters need to be estimated and the highest outcomes from them need to be found [27]. However, choosing the optimal parameters is a challenging task for any machine learning procedure. In this work, we manually trained models with various parameters and identified the best model from them.
- To evaluate the performance of different regression models, we used several metrics such as MAE, RMSE and ${R}^{2}$ values (see details in Section 2.4) for evaluating the test set and identifying the best model for predicting cumulative confirmed infection and fatality cases with the lowest error rate.
- All actual and predicted trajectories have been placed in our web tool which is uploaded by the local cloud host via plotly Chart Studio.

## 3. Experiment Result

#### 3.1. Cloud Based Short Term Forecasting

#### 3.1.1. 1st Round (8 March 2020–8 April 2020)

#### 3.1.2. 2nd Round (15 March 2020–15 April 2020)

#### 3.1.3. 3rd Round (22 March 2020–22 April 2020)

#### 3.1.4. 4th Round (29 March 2020–29 April 2020)

#### 3.1.5. 5th Round (5 April 2020–6 May 2020)

#### 3.1.6. 6th Round (12 April 2020–13 May 2020)

#### 3.1.7. 7th Round (19 April 2020–20 May 2020)

#### 3.1.8. 8th Round (26 April 2020–7 May 2020)

#### 3.1.9. 9th Round (3 May 2020–3 June 2020)

#### 3.1.10. 10th Round (10 May 2020–10 June 2020)

#### 3.1.11. 31st Round (4 October 2020–4 November 2020)

#### 3.1.12. 32nd Round (11 October 2020–11 November 2020)

#### 3.1.13. 33rd Round (18 October 2020–18 November 2020)

#### 3.1.14. 34th Round (25 October 2020–25 November 2020)

#### 3.1.15. 35th Round (1 November 2020–28 November 2020)

## 4. Discussion

#### Implications

## 5. Conclusions and Future Work

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Appendix A

#### Appendix A.1. Linear Regression (LR)

#### Appendix A.2. Polynomial Regression (PR)

#### Appendix A.3. Support Vector Machine-Regression (SVR)

#### Appendix A.4. Multi-Layer Perception (MLP)

#### Appendix A.5. Polynomial Multi-Layer Perception (Poly-MLP)

#### Appendix A.6. Prophet Model

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**Figure 2.**Future Forecasting of Confirmed Cases estimating (

**a**) 1st round by LR (

**b**) 2nd round by SVM (

**c**) 3rd round by PR (

**d**) 4th round by Poly-MLP including (

**e**) 5th (

**f**) 6th (

**g**) 7th (

**h**) 8th (

**i**) 9th (

**j**) 10th (

**k**) 31th (

**l**) 32th (

**m**) 33th (

**n**) 34th round by Prophet Algorithm and (

**o**) 35th round by PR.

**Figure 3.**Future Forecasting of Death Cases estimating (

**a**) 1st round by LR (

**b**) 2nd round by SVM (

**c**) 3rd round by Poly-MLP (

**d**) 4th round by MLP including (

**e**) 5th (

**f**) 6th (

**g**) 7th (

**h**) 8th (

**i**) 9th (

**j**) 10th (

**k**) 31th (

**l**) 32th (

**m**) 33th (

**n**) 34th round by Prophet Algorithm and (

**o**) 35th round by PR.

Round | Method | Hyperparameters | Method | Hyperparameters |
---|---|---|---|---|

1st Round | Prophet | default | Prophet | default |

2nd Round | SVR | degree = 5 | SVR | degree = 5 |

3rd Round | PR | degree = 4 | Poly-MLP | degree = 2, neuron = 100 |

4th Round | PR | degree = 3 | Poly-MLP | degree = 1, neuron = 25,13,5 |

5th Round | Prophet | default | Prophet | default |

6th Round | Prophet | default | Prophet | default |

7th Round | Prophet | default | Prophet | default |

8th Round | Prophet | default | Prophet | default |

9th Round | Prophet | default | Prophet | default |

10th Round | Prophet | default | Prophet | default |

31st Round | Prophet | default | Prophet | default |

32nd Round | Prophet | default | Prophet | default |

33rd Round | Prophet | default | Prophet | default |

34th Round | Prophet | default | Prophet | default |

35th Round | Prophet | default | Prophet | default |

Round | Date | Highest Factor | Date | Highest Factor | |
---|---|---|---|---|---|

Infected Cases | Fatality Cases | ||||

1st Round | 15-March | 1.6670 | 21-March | 2.0000 | |

2nd Round | 16-March | 1.6000 | 21-March | 2.0000 | |

3rd Round | 9-April | 1.5138 | 23-March | 1.5000 | |

4th Round | 9-April | 1.5138 | 7-April | 1.4167 | |

5th Round | 9-April | 1.5138 | 7-April | 1.4167 | |

6th Round | 13-April | 1.2930 | 17-April | 1.2500 | |

7th Round | 20-April | 1.2003 | 20-April | 1.1099 | |

8th Round | 29-April | 1.0992 | 13-May | 1.0760 | |

9th Round | 5-May | 1.0775 | 13-May | 1.0760 | |

10th Round | 18-May | 1.0772 | 13-May | 1.0760 | |

31st Round | 07-October | 1.0064 | 14-October | 1.0044 | |

32nd Round | 12-October | 1.0061 | 14-October | 1.0044 | |

33th Round | 05-November | 1.0044 | 29-October | 1.0054 | |

34th Round | 17-November | 1.0051 | 17-November | 1.0063 | |

35th Round | 19-November | 1.0054 | 17-November | 1.0063 |

**Table 3.**Experimental Results for Both Infected and Fatality Cases from Different Regression Models.

Round | Method | RMSE | MAE | R^{2} | Method | RMSE | MAE | R^{2} | |
---|---|---|---|---|---|---|---|---|---|

Infected Cases | Fatality Cases | ||||||||

1st Round | LR | 1.0950 | 1.0489 | 0.7716 | LR | 0.2766 | 0.2500 | 0.5921 | |

2nd Round | SVR | 19.5371 | 17.4783 | 0.8372 | SVR | 2.0445 | 1.6569 | 0.7710 | |

3rd Round | PR | 13.2350 | 10.4837 | 0.9966 | Poly-MLP | 0.9218 | 0.8057 | 0.9777 | |

4th Round | Poly-MLP | 53.2986 | 43.5731 | 0.9882 | MLP | 0.7134 | 0.6773 | 0.9956 | |

5th Round | Prophet | 43.9722 | 39.9999 | 0.9951 | Prophet | 1.0051 | 0.8383 | 0.9758 | |

6th Round | Prophet | 13.0955 | 10.9061 | 0.9998 | Prophet | 0.5113 | 0.4621 | 0.9751 | |

7th Round | Prophet | 38.8117 | 32.6514 | 0.9989 | Prophet | 1.7241 | 1.5615 | 0.9870 | |

8th Round | Prophet | 59.5438 | 56.5706 | 0.9987 | Prophet | 1.8866 | 1.6031 | 0.9925 | |

9th Round | Prophet | 125.7346 | 105.7328 | 0.9946 | Prophet | 0.8403 | 0.5109 | 0.9988 | |

10th Round | Prophet | 151.9238 | 132.1162 | 0.9974 | Prophet | 2.8667 | 2.5618 | 0.9939 | |

31th Round | Prophet | 7.13 $\times {10}^{-11}$ | 5.82$\times {10}^{-11}$ | 1.0000 | Prophet | 9.25$\times {10}^{-12}$ | 8.41$\times {10}^{-12}$ | 1.0000 | |

32th Round | Prophet | 2.10$\times {10}^{-10}$ | 1.46$\times {10}^{-10}$ | 1.0000 | Prophet | 4.55$\times {10}^{-13}$ | 2.27$\times {10}^{-13}$ | 1.0000 | |

33th Round | Prophet | 7.70$\times {10}^{-11}$ | 7.28$\times {10}^{-10}$ | 1.0000 | Prophet | 4.55$\times {10}^{-13}$ | 2.27$\times {10}^{-13}$ | 1.0000 | |

34th Round | Prophet | 2.93$\times {10}^{-09}$ | 2.49$\times {10}^{-09}$ | 1.0000 | Prophet | 1.36$\times {10}^{-12}$ | 1.14$\times {10}^{-12}$ | 1.0000 | |

35th Round | Prophet | 11.7169 | 9.0584 | 0.8144 | Prophet | 213.5905 | 201.4884 | 0.9923 |

Round | LR | PR | SVR | MLP | Poly-MLP | Prophet |
---|---|---|---|---|---|---|

1st Round | 0.467 | 0.603 | 0.666 | 1.673 | 1.314 | 0.450 |

2nd Round | 0.468 | 0.431 | 0.483 | 1.201 | 1.201 | 0.433 |

3rd Round | 0.452 | 0.444 | 0.451 | 3.557 | 2.609 | 4.012 |

4th Round | 0.459 | 0.454 | 0.457 | 6.173 | 7.149 | 4.189 |

5th Round | 0.471 | 0.432 | 0.465 | 6.842 | 8.127 | 4.613 |

6th Round | 0.724 | 0.457 | 0.466 | 11.836 | 9.134 | 4.560 |

7th Round | 0.522 | 0.456 | 0.437 | 13.691 | 10.809 | 4.501 |

8th Round | 0.466 | 0.703 | 0.470 | 12.593 | 12.404 | 4.693 |

9th Round | 0.445 | 0.460 | 0.476 | 8.051 | 10.579 | 4.520 |

10th Round | 0.471 | 0.458 | 0.452 | 8.013 | 8.801 | 4.737 |

31st Round | 0.515 | 0.656 | 0.665 | 8.532 | 7.510 | 35.499 |

32nd Round | 0.471 | 0.483 | 0.459 | 8.657 | 8.002 | 30.286 |

33rd Round | 0.493 | 0.723 | 0.512 | 8.764 | 7.590 | 23.994 |

34th Round | 0.485 | 0.475 | 0.751 | 8.727 | 7.762 | 28.945 |

35th Round | 0.500 | 0.463 | 0.477 | 9.043 | 7.848 | 0.459 |

Round | Method | RMSE | MAE | R^{2} | Method | RMSE | MAE | R^{2} |
---|---|---|---|---|---|---|---|---|

Confirmed Cases | Death Cases | |||||||

1st Round | Prophet | 3.5808 | 2.7740 | −0.5786 | Prophet | 0.5837 | 0.4524 | −1.7821 |

2nd Round | SVR | 24.8737 | 22.8440 | 0.8043 | SVR | 2.4589 | 2.2902 | 0.7660 |

3rd Round | PR | 55.1576 | 49.7568 | 0.9676 | Poly-MLP | 2.3675 | 2.1407 | 0.9394 |

4th Round | PR | 63.7025 | 57.3959 | 0.9928 | Poly-MLP | 5.8317 | 4.9376 | 0.9072 |

5th Round | Prophet | 36.9007 | 31.2751 | 0.9985 | Prophet | 1.3350 | 1.0758 | 0.9877 |

6th Round | Prophet | 10.9600 | 9.0326 | 0.9999 | Prophet | 0.7154 | 0.6148 | 0.9870 |

7th Round | Prophet | 38.8742 | 34.5744 | 0.9995 | Prophet | 1.5454 | 1.4078 | 0.9956 |

8th Round | Prophet | 51.7929 | 46.3280 | 0.9996 | Prophet | 1.4812 | 1.1691 | 0.9982 |

9th Round | Prophet | 117.2432 | 98.1062 | 0.9987 | Prophet | 0.8894 | 0.6813 | 0.9996 |

10th Round | Prophet | 155.8378 | 131.5769 | 0.9990 | Prophet | 2.2835 | 1.8957 | 0.9987 |

31st Round | Prophet | 6.22$\times {10}^{-11}$ | 4.99$\times {10}^{-11}$ | 1.0000 | Prophet | 9.27$\times {10}^{-12}$ | 8.71$\times {10}^{-12}$ | 1.0000 |

32nd Round | Prophet | 1.89$\times {10}^{-10}$ | 1.50$\times {10}^{-10}$ | 1.0000 | Prophet | 4.86$\times {10}^{-13}$ | 2.60$\times {10}^{-13}$ | 1.0000 |

33rd Round | Prophet | 7.93$\times {10}^{-11}$ | 7.48$\times {10}^{-11}$ | 1.0000 | Prophet | 4.86$\times {10}^{-13}$ | 2.60$\times {10}^{-13}$ | 1.0000 |

34th Round | Prophet | 3.11$\times {10}^{-09}$ | 2.63$\times {10}^{-09}$ | 1.0000 | Prophet | 1.33$\times {10}^{-12}$ | 1.17$\times {10}^{-12}$ | 1.0000 |

35th Round | Prophet | 4.40$\times {10}^{-11}$ | 3.33$\times {10}^{-11}$ | 1.0000 | Prophet | 8.21$\times {10}^{-11}$ | 7.04$\times {10}^{-11}$ | 1.0000 |

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**MDPI and ACS Style**

Satu, M.S.; Howlader, K.C.; Mahmud, M.; Kaiser, M.S.; Shariful Islam, S.M.; Quinn, J.M.W.; Alyami, S.A.; Moni, M.A.
Short-Term Prediction of COVID-19 Cases Using Machine Learning Models. *Appl. Sci.* **2021**, *11*, 4266.
https://doi.org/10.3390/app11094266

**AMA Style**

Satu MS, Howlader KC, Mahmud M, Kaiser MS, Shariful Islam SM, Quinn JMW, Alyami SA, Moni MA.
Short-Term Prediction of COVID-19 Cases Using Machine Learning Models. *Applied Sciences*. 2021; 11(9):4266.
https://doi.org/10.3390/app11094266

**Chicago/Turabian Style**

Satu, Md. Shahriare, Koushik Chandra Howlader, Mufti Mahmud, M. Shamim Kaiser, Sheikh Mohammad Shariful Islam, Julian M. W. Quinn, Salem A. Alyami, and Mohammad Ali Moni.
2021. "Short-Term Prediction of COVID-19 Cases Using Machine Learning Models" *Applied Sciences* 11, no. 9: 4266.
https://doi.org/10.3390/app11094266