In this study, we have proposed an artificial neural network (ANN) model to estimate and forecast the number of confirmed and recovered cases of COVID-19 in the upcoming days until September 17, 2020. The proposed model is based on the existing data (training data) published in the Saudi Arabia Coronavirus disease (COVID-19) situation—Demographics. The Prey-Predator algorithm is employed for the training. Multilayer perceptron neural network (MLPNN) is used in this study. To improve the performance of MLPNN, we determined the parameters of MLPNN using the prey-predator algorithm (PPA). The proposed model is called the MLPNN–PPA. The performance of the proposed model has been analyzed by the root mean squared error (RMSE) function, and correlation coefficient (R). Furthermore, we tested the proposed model using other existing data recorded in Saudi Arabia (testing data). It is demonstrated that the MLPNN-PPA model has the highest performance in predicting the number of infected and recovering in Saudi Arabia. The results reveal that the number of infected persons will increase in the coming days and become a minimum of 9789. The number of recoveries will be 2000 to 4000 per day.

The history of coronavirus (CoV) is not new in this world and has appeared with different names like Middle East Respiratory Syndrome Coronavirus (MERS-CoV), Severe Acute Respiratory Syndrome (SARS-CoV), etc. The first one was transmitted from civet cats to humans in 2002 in China, and the second virus was transmitted from dromedary camels to humans in 2012 in the Kingdom of Saudi Arabia (KSA) [

Recently, several studies on COVID-19 have already been published on computational, mathematical, and statistical aspects of different viruses. On the mathematical side, different models are used to study the dynamics of COVID-19. One of the most used models for the dynamics of various diseases is Susceptible-Infectious-Recovered (SIR) model. This model provides the epidemic growth through a system of time-dependent differential equations. The SIR model and its various modified versions have been used extensively by researchers to Ebola and AIDS diseases [

The real number of COVID-19 data represents a series of observations, where methods used for time-series prediction are native to the statistics field, such as Machine learning-based methods (such as artificial neural networks), Meta-predictors, and Structure-based methods [

Multilayer perceptron neural network (MLPNN) is a feed-forward neural network with three types of layers (input layer, hidden layers, and output layer), as shown in

where

In the output layer, we have two input neurons that represent the infected and recovered number of persons. Also, we have a hyperbolic tangent transfer function that has an output ranging from –1 to +1

where

The supervised learning method of ANNs is the best technique using to determine the optimal values of all ANN parameters, which are the “input weights” and “output weights.” Therefore, finding the values of the parameters of an ANN leads to becoming an ANN model. This phase is known as the training ANNs via observed values (training data), and optimization algorithm (see

where

Several algorithms have been used for the training to find the optimal values of the parameters, such as metaheuristic optimization algorithms [

where

Setting different values of v will affect the size of the jump for the solution _{i}. Moreover, the best direction is chosen from the paths generated to set the global solution. Step length is another problem with updating solutions. The second issue related to updating the solution is the step length for exploration

Movement of a common prey:

If follow up probability is met,

If the follow-up probability does not meet the criteria, then

Movement of the best prey:

Movement of Predator:

In this study, we have proposed an ANN model to predict and to offer a quantitative overview of the Status of COVID 19 in KSA during the period (June 22 to September 17, 2020). Note that using artificial inelegance is a new technique in the field of epidemiological studies. The observed data (infected and recovered) during the period (March 12 to June 16, 2020) trains the ANN model, as shown in

We have used PPA for the training to determine the optimal values of the ANN model parameters (input weights and output weights). We have trained the ANN model in 20 trials, while the number of the iterations in PPA has been set for 1000, the number of population is equal 50, and the number of predators 8, local search directions 1, and the number of best prey 4, and then the best the values are reported.

With a minimal value of RMSE (13%) and correlation coefficient R (93%), represents the values of the training data and the expected data of “infected.”

On the other hand, to propose the ANN model for the number of recovered persons per day, we have used the observed data (training data) of “recovered” from June 22 to September 17, 2020. The best ANN model that we have proposed has RMSE = 35% and correlation coefficient R =93.6% (see

In this study, we have proposed an artificial neural network (ANN) prediction model using a multilayer perceptron neural network (MLPNN) and a prey-predator algorithm (PPA). This model, called hybrid MLPNN-PPA, is applied as an artificial inelegance forecasting technique for COVID-19 in Saudi Arabia. PPA is used to improve the performance of the model by determining the optimal values for the model parameters. The proposed model has a high performance in predicting the number of infected (cases), and the number of recovered in terms of root means squared error and correlation coefficient.

The proposed model has a high performance in predicting the number of infected and recovered persons within 87 days (from June 22 to September 17, 2020). According to the promising results obtained by the MLPNN-PPA model, the number of infected persons will increase in the coming days and become a minimum of 9789. The number of recoveries will be 2000 to 4000 per day.