The Covid-19 epidemic poses a serious public health threat to the world, where people with little or no pre-existing human immunity can be more vulnerable to its effects. Thus, developing surveillance systems for predicting the Covid-19 pandemic at an early stage could save millions of lives. In this study, a deep learning algorithm and a Holt-trend model are proposed to predict the coronavirus. The Long-Short Term Memory (LSTM) and Holt-trend algorithms were applied to predict confirmed numbers and death cases. The real time data used has been collected from the World Health Organization (WHO). In the proposed research, we have considered three countries to test the proposed model, namely Saudi Arabia, Spain and Italy. The results suggest that the LSTM models show better performance in predicting the cases of coronavirus patients. Standard measure performance Mean squared Error (MSE), Root Mean Squared Error (RMSE), Mean error and correlation are employed to estimate the results of the proposed models. The empirical results of the LSTM, using the correlation metrics, are 99.94%, 99.94% and 99.91% in predicting the number of confirmed cases in the three countries. As far as the results of the LSTM model in predicting the number of death of Covid-19, they are 99.86%, 98.876% and 99.16% with respect to Saudi Arabia, Italy and Spain respectively. Similarly, the experiment’s results of the Holt-Trend model in predicting the number of confirmed cases of Covid-19, using the correlation metrics, are 99.06%, 99.96% and 99.94%, whereas the results of the Holt-Trend model in predicting the number of death cases are 99.80%, 99.96% and 99.94% with respect to the Saudi Arabia, Italy and Spain respectively. The empirical results indicate the efficient performance of the presented model in predicting the number of confirmed and death cases of Covid-19 in these countries. Such findings provide better insights regarding the future of Covid-19 this pandemic in general. The results were obtained by applying time series models, which need to be considered for the sake of saving the lives of many people.

The Covid-19 pandemic is currently regarded as a threat to global health. Coronaviruses comprise a large number of virus species that may cause diseases in animals and humans. A number of coronaviruses are known to cause respiratory infections in humans, ranging from common colds to more severe diseases such as Middle East Respiratory Syndrome (MERS) and other Severe Acute Respiratory Syndromes (SARS). The recently discovered disease known as Covid-19 is an infectious disease that has spread throughout the world [

This health crisis has led to significant economic repercussions due to shocks to supply and demand that differ from previous crises. Policies are needed to help economies overcome the pandemic while maintaining the integrity of the network of economic and financial relations between workers and businesses, lenders and borrowers, and suppliers and end-users so that activity can recover once the outbreak ends. The aim is to prevent such a temporary crisis from causing permanent harm to people and companies due to job loss and bankruptcy. Deaths caused by the outbreak of Covid-19 have increased at an alarming rate, while the disease continues to spread throughout many large countries. The highest priority should be to maintain people’s health and safety as much as possible. Countries can help by increasing spending to fight the virus and improve their health care systems, including spending on personal protective equipment, testing, diagnostic tests, and increasing the number of beds in hospitals. Since a vaccine has not yet been found, countries have taken actions to curb its spread. The economic impact was considerable in the countries that were most affected by the outbreak. For example, in China, activity in the manufacturing and service sectors fell sharply in February. While the fall in activity in the manufacturing sector is comparable to the beginning of the global financial crisis, the decline in the service sector appears to be greater due to the significant impact of social distancing [

Machine learning, deep learning, and traditional statistical models can be used to model and forecast Covid-19. In this study, we developed models that can predict the spread of Covid-19 with a high degree of accuracy. The main contribution of this paper is the use of the LSTM and Holt-Trend models to effectively predict the numbers of confirmed cases and deaths in Saudi Arabia, Italy, and Spain. The results of time series models to predict the spread of Covid-19 based on real-time data gathered from the WHO were more satisfactory. The main contributions of the present research are as follows:

To present the advanced time series model, namely, the LSTM deep learning model to predict the spread of Covid-19 in Saudi Arabia, Italy, and Spain.

To validate the proposed system and examine the reliability of the LSTM model for predicting the spread of Covid-19.

Researchers have used search queries to monitor health care systems [

This section presents the proposed method for predicting the spread of Covid-19.

Saudi Arabia, Italy, and Spain are the three countries we examined. Data for 85 days (between 21 January 2020 and 15 April 2020) were used. Confirmed cases were used to predict the future spread of Covid-19.

Country | Numbers of days | Number of simple confirmed cases | Number of simple deaths |
---|---|---|---|

Saudi Arabia | 55 | 42 | 22 |

Italy | 55 | 75 | 53 |

Spain | 55 | 72 | 42 |

The min-max method was employed in MATLAB to scale the data. This method transformed data within a range of 0 to 1 scales.

where _{min}_{max}_{minx} is the minimum number 0, and _{maxx} is the maximum number 1.

This section presents the models used to predict the numbers of confirmed cases and deaths in Saudi Arabia, Italy, and Spain.

Recurrent neural networks (RNNs) were designed in 1980 [

The hidden layer is represented by _{t}_{t}_{t}_{t}

_{t}_{t}_{t}

_{t}

where _{t}_{t −1} is the hidden state of the RNN, _{t}_{t}_{t}_{t}_{t}_{t}_{t}

where _{t}_{t −1} is the previous hidden layer in the long short-term memory network. In order to transfer the data from input to output by the logistic sigmoid function. The hyperbolic tangent function is based on the _{t}_{t}

Parameters of the LSTM algorithm | |
---|---|

Input hidden layer | 1 2 3 |

Num hidden | 250 |

Shallow hidden layer size | [30 50]; |

Max epochs | 500 |

Mini batch size | 120 |

Max iterations | 200 |

Shallow hidden layer size | [30 50] |

Exponential smoothing models are among the most important prediction approaches and are widely used in industry and commerce. The exponential smoothing method is a generalization of the moving average technique. Exponential smoothing models use stationary time-series data. The idea behind exponential smoothing is to smooth original time-series data to forecast future values. Holt-Trend Exponential Smoothing (HTES) model is similar to weighted exponential smoothing. However, it uses a trend estimator that changes over time:

where _{t+m} is a forecast future value; _{t}_{t −1}; _{t −1} refers to the estimate of the growth rate of the time series constructed at time _{t −1} (this is typically called the trend component). _{i}_{i}

To evaluate the performance of the LSTM and the Holt-Trend model, mean squared error (MSE), root-mean-square error (RMSE), and mean error metrics were applied. These standard metrics have the capability to find prediction errors made by the LSTM and Holt-Trend models.

where _{t}

where _{t}

where _{t}

where

Our analyses used WHO data for 55 days (21 January 2020 to 15 April 2020). The min–max method was applied for normalization purposes. Saudi Arabia, Spain, and Italy were used to test and evaluate the proposed model. Two advanced time series models were applied to predict confirmed cases and deaths. Two experiments, which are described below, were conducted in a specific environment in MATLAB 2018 to obtain the prediction results.

In this section, we evaluate the performance of the LSTM deep learning approach. The deep learning algorithm is proposed, and the real dataset was divided into 80% training and 20% testing. Whereas the training data are considered self-similar predictions, the testing predicted and validated the proposed model. Evaluation metrics (MSE, RMSE, mean error, and R-values) were employed to examine and evaluate the LSTM model.

MSE | RMSE | Mean error | SD | R (%) | |
---|---|---|---|---|---|

Training data | |||||

Saudi Arabia | 000132 | 0.0363 | 0.00023 | 0.0370 | 99.74 |

Italy | 0.0028 | 0.01676 | 2.0558e −05 | 0.0169 | 99.94 |

Spain | 0.00020 | 0.01426 | 3.5212e −05 | 0.01441 | 99.91 |

Testing data | |||||

Saudi Arabia | 0.8111 | 0.9013 | 0.250 | 0.3900 | 99.86 |

Italy | 0.14161 | 0.6450 | 0.5158 | 0.4045 | 98.86 |

Spain | 0.1096 | 0.3310 | 0.2067 | 0.2700 | 99.16 |

As shown in

MSE | RMSE | Mean error | SD | R (%) | |
---|---|---|---|---|---|

Training data | |||||

Saudi Arabia | 0.00605 | 0.0778 | 0.00023 | 0.0825 | 98.40 |

Italy | 0.00037 | 0.0192 | 9.5161e −05 | 0.01956 | 99.91 |

Spain | 0.00047 | 0.02178 | 0.000170 | 0.0222 | 99.95 |

Testing data | |||||

Saudi Arabia | 1.0767 | 1.0376 | 0.970 | 0.5020 | 99.77 |

Italy | 0.4253 | 0.6521 | 0.4725 | 0.4805 | 97.90 |

Spain | 0.0399 | 0.1998 | 0.1350 | 0.1613 | 99.57 |

The prediction results obtained from the LSTM when predicting deaths in Saudi Arabia were 0.00605, 0.0778, 0.00023, and 0.082 with respect to MSE, RMSE, mean error, and standard deviation, respectively; in training, the performance of the proposed model for forecasting the number of deaths was as follows:

The Holt-Trend model is an exponential smoothing model used to predict trend data. The Holt-Trend model has two smoothing constants, one for the level and one for the trend. In these experiments, we took different parameters for the level and the trend to obtain higher predictions. The level (alpha) parameter values were 0.1, 0.5, and 0.15; the trend (beta) parameter value was 0.20. The MSE metric was used to measure the best parameters; these were

MSE | RMSE | Mean error | R (%) | |
---|---|---|---|---|

Saudi Arabia | 0.0074 | 0.085 | 0.0377 | 99.06 |

Italy | 3.8378e −04 | 0.0196 | 0.0082 | 99.96 |

Spain | 5.1711e −04 | 0.0227 | 0.0085 | 99.94 |

In this section, the Holt-Trend model is applied to predict the number of deaths in the three countries. The Holt-Trend model depends on two constant values for the level and the trend.

MSE | RMSE | Mean Error | R (%) | |
---|---|---|---|---|

Saudi Arabia | 0.0030 | 0.0546 | 0.0248 | 99.80 |

Italy | 5.6995e −04 | 0.0239 | 0.0117 | 99.94 |

Spain | 9.3129e −04 | 0.0304 | 0.0145 | 99.94 |

In this section, we focused on predicting the future values of the number of deaths in the three countries. We forecasted future values at a time interval of one month from 15-4-2020 to 15-05-2020. The Holt-Trend model was applied to forecast future values by using data from the WHO. Figures demonstrate the performance of the Holt-Trend to forecast the future values; the trend is going up, which indicates that the number of confirmed cases will increase.

This study applied deep learning and the Holt-Trend model to predict the risk of Covid-19 outbreaks based on real-time data collected from the WHO. The main objective of the proposed model is to predict the number of future cases and deaths. The proposed models can be used to estimate the future risk of Covid-19 outbreaks. Max–min normalization was applied to save the range of data. The algorithms were applied to predict the number of Covid-19 cases and deaths. For the LSTM algorithm, the data were divided into 80% training (used for self-prediction) and 20% testing (used for validation and future forecasting). The statistical Holt-Trend model was applied to predict the number of cases and deaths. To validate the model, we forecasted future values over a period of 30 days. The prediction results demonstrated that the LSTM and Holt-Trend models can be effectively employed to predict Covid-19 outbreaks by using real-time data gathered from the WHO. Comparative predicted results between the LSTM and Holt-Trend models were presented. The proposed models showed effective performance according to the MSE, RMSE, mean error, and correlation of increment performance measures. In addition, the LSTM and Holt-Trend models are more satisfying to predict Covid-19 cases. One limitation of this study was the lockdown. We could not meet with medical 525 experts due to the quarantine, so we collected the data from the WHO. In future work, we will use Google search terms to predict Covid-19 cases.