Received: November 16, 2020. Revised: February 3, 2021. 484

Optimized Extreme Learning Machine for Forecasting Confirmed Cases of COVID-19

Ahmed Mudheher Hasan1* Aseel Ghazi Mahmoud2 Zainab Mudheher Hasan3

1Control and Systems Engineering Department, University of Technology-Iraq, Baghdad, Iraq 2College of Nursing, University of Baghdad, Iraq 3Ministry of Health and Environment of Iraq, Baghdad health directorate/Rusafa, Iraq * Corresponding author’s Email: [email protected]

Abstract: Recently, a new challenge to the researchers has been emerged due to the spread of a new outbreak called novel corona-virus (COVID-19) to portend the confirmed cases. Since discovering the first certain cases in Wuhan, China, the COVID-19 has been widely spreading and expanding to other provinces and other countries via travellers into various countries around the world. COVID-19 virus is not a problem of only developing countries, but also of developed countries. Artificial Intelligence (AI) techniques can be profitable to predict such; parameters, risks and influence of an outbreak. Therefore, accurate prognosis can be helpful to control the spread of such viruses and provide crucial information for identifying the type of virus interventions and intensity. Here are develop an intelligent model depicting COVID-19 transmission and resulting confirmed cases. The epidemic curve of COVID-19 cases was modelled. The main key idea of predicting the confirmed cases is based on two factors 1) cumulative number of confirmed cases and 2) the daily confirmed cases instead of using only one factor as previous research. In this study, a comparison between different intelligent techniques has been conducted. To assess the effectiveness of these intelligent models, a recorded data of 5 months has been used for training in various countries. Results obtained show the superiority of ELM model in accurate prediction and outperforms other intelligent techniques. We have used a Social Spider Optimization (SSO) method to optimize the ELM parameters. The prediction results show the superiority of the proposed intelligent predictors with accuracy greater than 93%. Therefore, medical personnel can take defensive steps earlier. Keywords: Extreme learning machine, Social spider optimization, COVID-19, Artificial intelligence, World health organization.

seafood market. Lab testing approves a similarity of 1. Introduction the new virus with bat coronavirus in 95% and more than 70% similitude to SARS-Cov. More than 162 Early of this year, a novel virus related to the countries are currently suffering from the rapid corona viruses’ family spread widely in Wuhan city spread of a novel corona virus, called COVID-19 of Hubei Province of People's Republic of China (called previously, 2019-nCoV). (PRC) and rapidly spread to the various countries in Some authors said that COVID-19 is mostly the world. In December 2019, infected people attends transferred from bats, since it is very similar to the to local hospitals cause they severe from a pneumonia Coronavirus family. However, it is not assured yet, with unknown reason. Most of the early reported and need more tests and examinations. A large cases had a mutual relation to the Huanan seafood number of confirmed cases and deaths cases are market which also traded animals as well. On reported in Wuhan city; therefore, the authorities December 31st 2019, the Government of PRC sends a decide to lockdown the Wuhan city by stopping all report regarding the outbreak to the World Health public transportation to reduce the spread of COVID- Organization (WHO), after one day they close the 19 virus. The Center for Disease Control and International Journal of Intelligent Engineering and Systems, Vol.14, No.2, 2021 DOI: 10.22266/ijies2021.0430.44

Received: November 16, 2020. Revised: February 3, 2021. 485

Prevention (CDC) approved the transmission of the in outbreaks of Long Island, in New York, for the virus between humans at the end of January 2020 and period 2001-2014. Moreover, Ture and Kurt [8] the spread of it is very quick and risky, seeking more compare various time series forecasting models in firm policies and plans [1]. Therefore, social distance order to prognosis Hepatitis A Virus (HAV) infection. is mandatory. However, incubation period ranges Data recorded monthly for 13 years (Jan. 1992-Jun. between (2-14) days considered as a big challenge to 2004). Seasonal outbreaks of Influenza are mostly the medical personnel. Actually, predicting or repeated every season in most temperature countries forecasting the COVID-19 cases, deaths, and in the world. Therefore, Shaman and Karspeck [9] recovered is very important to ensure a successful suggest a predicting model based on Kalman filter protection plans. (KF). Validation of the suggested model is confirmed Shi et al [2] derive a mathematical model for the through utilizing a cumulative data of New York City epidemic curve of COVID-19 to predict the actual for 6 years (from 2003- till 2008). While Shaman et number of positive cases, while the unreported al. [10] propose another model except using data for confirmed cases was determined through maximum 108 cities in (2012-2013) season. likelihood estimation. They claimed that the number Outbreak of Ebola which happened in some West of cases reported is increased by 21-fold and they African countries such as (Guinea, Sierra Leone, and finally estimate the value of reproduction number Liberia) encourages Jeffrey and his group to harness (Ro). Hiroshi et al [3] suggest an estimation model to a dynamic model with Bayesian inference to predict predict the incidence rate of the COVID-19 virus in the outbreak of Ebola virus [11]. Another epidemic Hubei-China, through collecting a data from 565 called Severe Acute Respiratory Syndrome (SARS) Japanese people who were evacuated from China, had been analyzed by [12] through proposing a they predict the death rate in range between (0.3% to simple mathematical model to study and investigate 0.6%). Even though, the number of samples from the the influence of control measures against this evacuated Japanese people is small and insufficient. infection. This study estimates the reproduction Moreover, By proposing an estimation model to number in different countries (i.e. Canada, and Hong estimate the transmission risk of COVID-19 in Kong). In [13] a real time model for monitoring and Beijing during the outbreak based on clinical forecasting H1N1-2009 was proposed. Furthermore, progression of the viruses, Tang et al [4] are trying to Nah et al. [14] proposed a simple statistical model estimate the importance of Ro, epidemiological status based on the effective distance to predict the of the people, and intervention measures. Accurate international propagation of Middle East Respiratory analysis shows that quarantine and isolation with Syndrome (MERS) related to Coronavirus. stopping the travel to Beijing decreases the number Emergency epidemic forecasting attracts many of people who are infected by one week to less than researchers to use AI capabilities for this reason. Jia 91.14%. In addition to, Thompson et al [5] estimate et al. [15] suggest a Recurrent Neural Network the percentage rate of transmission of COVID-19 (RNN) to predict the outbreak of hand-foot-mouth from human to human. They estimated the disease (HFMD) caused by entero viruses in China. transmission equal to 0.4 and can be reduced to 0.012 Machine learning is recently used to predict various if half of the checked samples are hospitalized when outbreaks such as Spatio-Temporal spread of symptom are appeared. Unfortunately, the Pathological diseases in [16], predict cardiovascular confidence of this conclusion is low since the study diseases in [17], or seasonal Influenza in [18], and is based on 47 patients only. Moreover, Jung et al [6] also Diarrhea virus (PEDV) outbreaks in [19], and present an estimation model based on the exported predict epidemiological clc’s of Ebola virus outbreak cases of COVID-19 outside China to estimate a (EBOV) in West Africa [20]. However, this type of model for the probability of death as a result of this learning required a large amount of data sets. virus. Their estimation outcomes with two Various techniques have been utilized recently possibilities as (5.1% and 8.4%), while the predicted including classical Kalman Filtering (KF) and Ro for the two scenarios are (2.1% and 3.2%), Artificial Intelligence (AI) techniques to predict time respectively. series problems. In KF, the state of a linear stochastic In the last decade, several studies were conducted model is estimated through efficient computational to forecast and predict various epidemics. As an techniques. Unfortunately, KF and even Extended example, DeFelice et al. [7] suggest an accurate Kalman Filter (EKF) still have major disadvantages forecast for transmission and human infection by such as: West Nile Virus (WNV) cases. Therefore, the (1) It is required to a stochastic error model for proposed model is based on employing a data the intended model which is difficult to assimilation and two observed data method registered obtain for some applications; International Journal of Intelligent Engineering and Systems, Vol.14, No.2, 2021 DOI: 10.22266/ijies2021.0430.44

Received: November 16, 2020. Revised: February 3, 2021. 486

(2) It is necessary to gain a prior knowledge for Anywise, this technique is not recommended in the measurement covariance matrices which high level of stochastic and arbitrary data [24]. Static different from one model to another; ANN technique shows significant results in (3) It suffers from computational intricacy; forecasting problems; however, it has many (4) It lacks of observability problem for different drawbacks such as its slow convergence to the state variables. specified target, and long time elapsed during training Accurate results and elevated accuracy for some phase. Therefore, various researchers extend their published intelligent techniques show that KF efforts to overcome the limitations of the ANN technique outperforms some of these intelligent techniques, including Extreme Learning Machine techniques when the required model of the system (ELM). ELM is a new and promising type of Feed- and the previous drawbacks are overcome. However, Forward Neural Network (FFNN), which contain a a systematic review of using various AI type to single hidden layer and perform an efficient predict different outbreaks is reported in [21]. It can mechanism to determine the hidden nodes in order to be observed that majority of the algorithms use a computing the output weights [25]. combined intelligent with optimization methods to Depending on deriving the mathematical model predict the future growth established on a previous in order to predict the number of infected cases of recorded data. COVID-19 is restricted and results in unreliable Therefore, in order to overcome the mentioned results. Moreover, most of the previous intelligent drawbacks of the KF, then Artificial Neural Network techniques predict the number of cases depending on (ANN) has been utilized for prediction process in the reported daily cases solely while in the current various fields. Several architectures were evaluated work utilize the daily and cumulative cases as well. through utilizing different types of ANN networks. Furthermore, the estimation of reproduction number These networks include Multi-Layer Perceptron is influenced by different factors such as the region, Neural Network (MLPNN), Radial Basis Function race, sex, age and other factors which restrict it to (RBF), and Input Delay Neural Network (IDNN). An employ in other places. Eventually, the results of the experimental result shows the superiority of RBF in model were based on the daily and cumulative reports providing a better performance than a classical in different countries at the beginning of the COVID- MLPNN in some prediction and classification 19 outbreak. In this study, we intend to (1) test the problems and functional extrapolations. applicability of the ELM technique to forecasting the Unfortunately, ANN is not sufficient since it requires certain cases of COVID-19, (2) optimize the ELM choosing the number of hidden layers and also the structure through employing a Social Spider number of nodes in each layer. On one hand, RBF Optimization (SSO) technique, and (3) examine the contain a single hidden layer but the number of nodes performance of our optimized ELM model and is decided empirically which make it unsuitable for compare the results obtained with other optimization real time implementation. Moreover, IDNN is techniques. considered an accurate model but unlikely it requires The remainder of this paper is formulated as a lot of time for training computations. Moreover, follows: section 2 illustrates the utilized Extreme Artificial based segmented forward predictor (ASFP) Learning Machine (ELM) while the proposed has been widely used for achieving forward intelligent predictor is presented in section 3. Section prediction through using the RBF. However, prosaic 4 discusses the results obtained from the accuracy obtained from using the ASFP method implemented predictor and finally the main opens the door for improving more effective AI conclusions are drawn in section 5. techniques. The Adaptive Neuro-Fuzzy Inference System (ANFIS) architecture is extensively utilized 2. Extreme learning machine (ELM) in forecasting and prediction problem. ANFIS As illustrated in Fig. 1, the training procedure in consists of fixed five-layered feed-forward neural stage 2 is performed through utilizing Extreme network (FFNN) architecture. It collects the benefits Learning Machine (ELM). ELM is extremely fast features of two main intelligent techniques named as which attribute to a gradient descent feed-forward Artificial Neural Network (ANN) and Fuzzy System neural network since it outperforms the traditional (FS) [22]. ANFIS was chosen in solving various back-propagation training algorithm in terms of problems due to its superiority in dealing with generalization, falling into local minima, and over- imprecision, uncertainty, and stochastic problem in fitting. Furthermore, ELM is consistent with the input data for the model [23]. Nevertheless, commonly all non-linear activation functions. ANFIS structure is restricted to only one output which makes it suitable for the MISO solely. International Journal of Intelligent Engineering and Systems, Vol.14, No.2, 2021 DOI: 10.22266/ijies2021.0430.44

Received: November 16, 2020. Revised: February 3, 2021. 487

Figure. 2 Extreme learning machine structure [27]

Or can be expressed in matrix form as 퐻푊푖 = 푇

퐻(푤푒1, … . , 푤푒푁̂, 푇ℎ1, … , 푇ℎ푁̂, 퐼1, … , 퐼푁) = 푓(푤푒1퐼1 + 푇ℎ1) ⋯ 푓(푤푒푁̂퐼푁̂ + 푇ℎ푁̂) [ ⋮ ⋱ ⋮ ] (3) 푓(푤푒1퐼푁 + 푇ℎ1) ⋯ 푓(푤푒푁̂퐼푁 + 푇ℎ푁) 푁×푁̂

푇 푇 푊1 푂1 푊 = [ ⋮ ] 푎푛푑 푇 = [ ⋮ ] (4) 푊푇 푂푇 푁̂ 푁̂×푚 푁̂ 푁×푚 Figure. 1 The proposed SSO-ELM forecasting module Where H represent the feed-forward neural The structure of ELM model is illustrated in Fig. 2, network output matrix and the 푖푡ℎ column of matrix while the details about the ELM structure are given H is defined as the 푖푡ℎ hidden node output which as follows: corresponds to inputs I1, I2, …, IN. Hopefully, Considering N input and target samples (퐼푖, 푂푖), 푇 푛 gradient learning algorithm can be utilized to find the where 퐼푖 = [퐼푖1 , 퐼푖2 , … . , 퐼푖푛] ∈ 푅 and 푂푖 = minimum of ‖퐻푊 = 푇‖ even if H is unknown. 푇 푚 ̂ [푂푖1, 푂푖2, … . , 푂푖푚] ∈ 푅 , with ℎ hidden nodes, Actually, the proposed ELM network tries to reach while the activation function 푓(푥) can be expressed the minimum training error. In addition to achieve as [26]: smallest norm of output weights [25, 28, 29]. During minimization procedure through utilizing gradient 퐴푐푡푖푣푎푡푖표푛 퐹푢푛푐푡푖표푛 = learning algorithm, the vector we which includes the ∑ℎ ∑ℎ 푖=1 푊푖푓푖(퐼푗) = 푖=1 푊푖 푓(푤푒푖퐼푗 + 푇ℎ푖) = 푑푖(1) weights (푤푒푖, W) and biases (푇ℎ푖) parameters could 푗 = 1, … , 푁; be updated as follows:

푇 Where 푊 = [푤 , 푤 , … , 푤 ] acts as the 휕퐸(푤푒) 푖 푒푖1 푒푖2 푒푖푛 푤푒푘 = 푤푒(푘−1) − 훾 (5) connection weights between the input nodes and the 휕푤푒 푡ℎ 푖 hidden nodes while 푊푖 = [푊 , 푊 , … , 푊 ]푇represent the weight connection Where 훾 is the learning rate of the gradient-based 푖1 푖2 푖푚 algorithm. Back-propagation (BP) learning algorithm between the 푖푡ℎ hidden nodes and the output nodes as is considered as one of the most popular learning well, while 푇ℎ is the threshold of the 푖푡ℎ hidden 푖 algorithm utilized in training the feed-forward neural node. Here the conventional feed-forward neural network. However, it suffers from lack of high network attempts to minimize the difference between convergence, falling into local minima, and 푑 (i.e. desired output) and 푂 (i.e. Actual output). 푗 푗 generalization issues. At the beginning of learning stage, all parameters are assigned randomly. Such as 푂 = ∑ℎ 푊 푓(푤 퐼 + 푇ℎ ); 푗 = 1,2, … , 푁 (2) 푗 푖=1 푖 푒푖 푗 푖 the weights of the inputs (푤푒푖) with the biases of the hidden layer (푇ℎ푖) and the hidden layer output matrix

International Journal of Intelligent Engineering and Systems, Vol.14, No.2, 2021 DOI: 10.22266/ijies2021.0430.44

Received: November 16, 2020. Revised: February 3, 2021. 488

(H). During the learning stage these parameters are updated in equivalent manner to find a least squares solution of the linear system 퐻푊 = 푇.

‖퐻(푤푒1, … , 푤푒푁̂, 푇ℎ1, … 푇ℎ푁)푊 − 푇‖ = 푚푖푛푊‖퐻(푤푒1, … , 푤푒푁̂, 푇ℎ1, … 푇ℎ푁)푊 − 푇‖(6)

Consequently, if the entire number of hidden nodes (푁) is equal to the total number of separate training samples (N), then it results in a sequence and invertible matrix (H). In addition to, random initialization for the input weight vectors (푤푒푖) and the hidden biases (푇ℎ푖) ensures an accurate trained for the feed-forward neural network (FFNN) with high accuracy. However, if the number of hidden nodes is less than the number of the training samples

(i.e. 푁 < N) then it results a non-square matrix (H) Figure. 3 Training the intelligent predictor which will not satisfy the formula 퐻푊 = 푇, which results in a solution equal to the linear system 푊 = Fig. 3. During the training phase, the SSO technique ∗ 퐻 푇. is utilized to decide the optimum number of hidden ∗ Where 퐻 is represent the Moore-Penrose nodes in hidden layer. Besides, the recorded data is generalization of the (H) matrix inverse [30]. This portioned into two parts: first part is for training inverse matrix can be calculated utilizing various (i.e.75%) and the second part for testing (25%). methods such as orthogonalization, iterative, The number of hidden nodes in ELM network is orthogonal projection and singular value prepared by SSO technique. Hence, in the training decomposition methods [31]. phase, the desired performance is calculated based on Actually, ELM can be utilized to train the FFNN the error between the actual output and the desired in two phases; first phase includes the initializations output as shown in Eq. (7). of random feature mapping for the hidden layer in order to mapping the input data to useful features 1 푀푆퐸 = ∑푁 (퐴 − 푇 )2 (7) through utilizing a classical nonlinear mapping 푁 푖=1 푖 푖 function. While computing the weights and thresholds of the hidden and output layer are Where A is the actual output, T is the desired output; computed through minimizing the approximation N is the number of samples. Actually, the smallest error in the squared error. value of the difference relates to better ELM parameters. 3. Design of the intelligent predictor

The proposed frame work consists of three modules as shown in Fig. 1. These modules are (1) Data collection, (2) Training phase, and (3) Testing phase. In this section, the proposed SSO-ELM method will be designed for predicting the confirmed cases of the COVID-19. The SSO-ELM utilizes the Social Spider Optimization (SSO) to train the ELM network through optimizing its parameters. The SSO-ELM has three main layers as conventional ELM network. Layer 1 called the input layer which contains the input variables (Daily Recorded cases and the Accumulated cases), Layer 2 is called the hidden layer (i.e. may be more than one layer), and Layer 3 called the output layer which produces the forecasted Figure. 4 Window based-ELM parameters updating values (i.e. predicted confirmed cases) as shown in strategy

International Journal of Intelligent Engineering and Systems, Vol.14, No.2, 2021 DOI: 10.22266/ijies2021.0430.44

Received: November 16, 2020. Revised: February 3, 2021. 489

Here, the number of hidden neurons is increased Table 1. Calibration parameters of the Optimization in a range from 2 to 100 and the training procedure is Techniques continual until reaching the desired accuracy, which Technique Parameter Values Number of in this paper, is the maximum number of iterations. 100 Therefore, the optimum number of hidden iteration neurons is passed to train the ELM network. After Population size 20 completing the training phase using data for three Selection Rank Crossover Two-point weeks, the intelligent predictor started with testing GA crossover phase utilizing the optimized parameters in order to Crossover 0.5 predict the estimated confirmed cases for the next 7 probability days as explained in Fig. 4. The performance of the Mutation Random intelligent predictor is evaluated through comparing Mutation 0.2 the recorded cases with the predicted cases. Finally, probability the SSO-ELM predicts the confirmed cases of COVID-19 in the next few days. Number of 100 iteration 4. Experimental results Population size 20 Inertia weight 0.4 The primary data set conducted in this study is factor (w) PSO COVID-19 recorded data set. This data set is C1 and C2 2 obtained from the World Health Organization acceleration (WHO) reports. These reports contain daily Random confirmed cases including the infected cases, number (R1 1 recovered cases, and death cases in most of the and R2 ) registered countries from 21th January 2020 to 7th Number of June 2020. As shown in Fig. 1. 75% of these data is 100 used for training phase and the rest 25% is used for iteration Population size 50 testing phase. The performance assessment for the SSO Female percent 88 proposed intelligent predictor is assessed utilizing a Male percent 12 set of performance metrics such as using the Root Pf 0.7 Mean Square Error (RMSE), Standard Deviation 2 (STD), and the coefficient of determination (R ) as Table 2. Comparison results for influenza data sets follows: Elapsed Technique RMSE STD R2 Time 1 (sec) 푅푀푆퐸 = √ ∑푁 (퐴 − 푇 )2 (8) 푁 푖=1 푖 푖 ELM 534 507 0.897 10.25 GA-ELM 427 389 0.981 73.47 PSO-ELM 409 377 0.953 29.32 1 푆푇퐷 = √ ∑푁 |퐴 − 휇|2 (9) SSO-ELM 351 319 0.985 27.49 푁 푖=1

푁 2 (PSO). The parameters setting for these optimization 2 ∑푖=1(퐴푖−푇푖) 푅 = 1 − 푁 ̅ 2 (10) techniques are listed in Table 1. ∑푖=1(퐴푖−퐴푖) As mentioned previously, a data set for influenza Where A and T are the forecasted and the original data in United States of America (USA) from year 1999- values, respectively. Ā represent the average of A and 2019 was conducted to forecast the new cases using μ is the mean value of the data set. Therefore, the the proposed SSO-ELM compared with other lowest values of RMSE, and STD denotes to the methods. It can be observed from Table 2 that the optimum prediction method while greater value of R2 proposed SSO-ELM method has superior refers to best correlation for the predictor. performance compared to other approaches in all The goal of the study is to determine the ability of measures, although the PSO-ELM is ranked second. the proposed SSO-ELM model to prognosis the The achieved results show also that GA-ELM is 2 COVID-19 through matching the obtained results ranked third in terms of RMSE, STD, R , and elapsed with further optimization techniques, namely Genetic CPU time. On the other hand, comparison results Algorithm (GA), and Particle Swarm Optimization obtained between the proposed SSO-ELM and other

International Journal of Intelligent Engineering and Systems, Vol.14, No.2, 2021 DOI: 10.22266/ijies2021.0430.44

Received: November 16, 2020. Revised: February 3, 2021. 490

2500 2000

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0 0 China South Korea Japan Thailand USA France Iraq China South Korea Japan Thailand USA France Iraq Countries Countries (a) (b)

1 35

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0.4 15 CPU Time CPU 10 0.2 5

0 0 China South Korea Japan Thailand USA France Iraq China South Korea Japan Thailand USA France Iraq Countries Countries (c) (d) Figure. 5 Performance of the proposed SSO-ELM for various criteria: (a) root mean square error, (b) standard deviation, (c) correlation, and (d) CPU time

methods show that the proposed method outperforms Table 3. Comparison with previous work these outcomes in terms of performance measures. Technique Average RMSE Comparison results for the performance of the MLP [32] 394 proposed SSO-ELM for various countries to predict ANFIS [33] 492 the confirmed cases of COVID-19 are given in Fig. 5. Proposed technique 351 After analyzing the obtained results of RMSE, STD, R2, and elapsed CPU time. Some points can be addition to previous performance comparison, Fig. spotted such as SSO-ELM attains the smallest value 6shows the outputs of the proposed technique using amongst the compared techniques, and this denotes the historical data of COVID-19 corresponding to the the high performance of the SSO-ELM. In the forecasted cases for 7 days. In addition to previous meantime, the GA-ELM ranked the second technique discussed results. Table 3 shows clearly a comparison in providing better results than the rest of techniques. in order to assess the proposed technique against However, the time elapsed for reaching the target is existing techniques such as MLP [32] and ANFIS longer than that of PSO-ELM. [33]. Moreover, the coefficient of determination According to the obtained results, hence, it can be indicates a high correlation between the desired and observed that the suggested SSO-ELM has a great predicted output results from the proposed SSO-ELM ability to predict the COVID-19 cases. The previous technique, which has approximately 0.979. In results indicate clearly the adequacies of other techniques in predicting COVID-19 cases.

International Journal of Intelligent Engineering and Systems, Vol.14, No.2, 2021 DOI: 10.22266/ijies2021.0430.44

Received: November 16, 2020. Revised: February 3, 2021. 491

4 x 10 China South Korea 10 12000 Recorded cases 10000 Predicted cases 8 W5

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2 W1 Cumulative cases (Person) cases Cumulative Cumulative cases (Person) cases Cumulative 2000 Recorded cases Predicted cases 0 0 0 20 40 60 80 100 120 140 0 20 40 60 80 100 120 140 Days Days (a) (b) 4 x 10 Japan Thailand 2 3500 Recorded cases Recorded cases Predicted cases 3000 Predicted cases W3 1.5 2500

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0 0 20 40 60 80 100 120 140 Days (g) Figure. 6 Comparison between the desired output and actual output of the intelligent technique for different countries such as: (a) China, (b) South korea, (c) Japan, (d) Thailand, (e) USA, (f) France, and (g) Iraq. International Journal of Intelligent Engineering and Systems, Vol.14, No.2, 2021 DOI: 10.22266/ijies2021.0430.44

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predicting the spread of COVID-19 Pandemic in 5. Conclusions Saudi Arabia”, International Journal of Intelligent Engineering and Systems, Vol. 13, This paper suggests using the SSO technique in No. 5, pp. 463-475, 2020. order to optimize the ELM parameters and [2] S. Zhao, S. S. Musa, Q. Lin, J. Ran, G. Yang, W. specifically the number of hidden neurons. The Wang, Y. Lou, L. Yang, D. Gao, D. He, developed SSO-ELM is utilized in order to forecast “Estimating the Unreported Number of Novel the COVID-19 cases depending on a history cases Coronavirus (2019-nCoV) Cases in China in the recorded for previous five months that was First Half of January 2020: A Data-Driven discovered in various countries such as China, South Modelling Analysis of the Early Outbreak”, J. Korea, Japan, Thailand, USA, France, and Iraq. Clin. Med. Vol. 9, No. 2, pp. 1-6, 2020. These countries are chosen due to variance in cultural, [3] H. Nishiura, T. Kobayashi, Y. Yang, K. Hayashi, citizen population, climate environments. The results T. Miyama, R. Kinoshita, N. M. Linton, S. M. show that the proposed SSO-ELM technique Jung, B. Yuan, A. Suzuki, “The Rate of outperforms other techniques and reduces the Underascertainment of Novel Coronavirus estimation error by approximately 52%, 21%, and (2019-nCoV) Infection: Estimation Using 16% against the implemented techniques (ELM, GA- Japanese Passengers Data on Evacuation ELM, PSO-ELM), respectively and by 12%, and Flights”, J. Clin. Med. Vol. 9, No. 2, pp. 1-3, 40% against (MLP and ANFIS) techniques. 2020. Therefore, the evaluations outcomes indicate clearly [4] B. Tang, X. Wang, Q. Li, N. L. Bragazzi, S. its good performance. Therefore, the promising Tang, Y. Xiao, J. Wu, “Estimation of the results ensure the ability of the proposed SSO-ELM Transmission Risk of the 2019-nCoV and Its to use in other forecasting applications. Finally, Implication for Public Health Interventions”, J. COVID-19 outbreak in Wuhan, China, highlights the Clin. Med. Vol. 9, No. 2, pp. 1-13, 2020. emergence of global surveillance of henipaviruses in [5] R. N. Thompson, “Novel Coronavirus Outbreak bats, since these bats are the main hosts for this virus in Wuhan, China, 2020: Intense Surveillance Is and many others. As a future plan of this work is to Vital for Preventing Sustained Transmission in investigate some factors such as travelers, flight, New Locations”, J. Clin. Med. Vol. 9, No. 2, pp. inside and outside tourists, and business that have a 1-8, 2020. great effect on economic during and after the [6] S. M. Jung, A. R. Akhmetzhanov, K. Hayashi, N. epidemics for long term. M. Linton, Y. Yang, B. Yuan, T. Kobayashi, R. Kinoshita, H. Nishiura, “Real time estimation of Conflicts of Interest the risk of death from novel coronavirus (2019- The authors declare no conflict of interest. nCoV) infection: Inference using exported cases”, J. Clin. Med. Vol. 9, No. 2, pp. 1-10, Author Contributions 2020. [7] N. B. DeFelice, E. Little, S. R. Campbell, J. Conceptualization, Ahmed M. Hasan and Zainab Shaman, “Ensemble forecast of human West M. Hasan; methodology, Ahmed M. Hasan; software, Nile virus cases and mosquito infection rates. Ahmed M. Hasan and Aseel G. Mahmoud; validation, Nat”, Commun. Vol. 8, pp. 1-6, 2017. Ahmed M. Hasan, Aseel G. Mahmoud, and Zainab M. [8] M. Ture and I. Kurt, “Comparison of four Hasan; formal analysis, Ahmed M. Hasan; different time series methods to forecast investigation, Zainab M. Hasan; resources, Zainab M. hepatitis A virus infection”, Expert Syst. Appl., Hasan; data curation, Ahmed M. Hasan and Aseel G. Vol. 31, No. 1, pp. 41-46, 2006. Mahmoud; writing—original draft preparation, [9] J. Shaman, A. Karspeck, “Forecasting seasonal Ahmed M. Hasan; writing—review and editing, outbreaks of influenza”, Proc. Natl. Acad. Sci. Ahmed M. Hasan and Aseel G. Mahmoud; USA, Vol. 109, No. 50, pp. 20425-20430, 2012. visualization, Ahmed M. Hasan; supervision, Ahmed [10] J. Shaman, A. Karspeck, W. Yang, J. Tamerius, M. Hasan and Aseel G. Mahmoud; project M. Lipsitch, “Real-time influenza forecasts administration, Ahmed M. Hasan; funding during the 2012-2013 season”, Nat. Commun., acquisition, not funded. Vol. 4, pp. 1-10, 2013. [11] J. Shaman, W. Yang, S. Kandula, “Inference and References forecast of the current West African Ebola [1] B. Abdullah, A. Hosam, and A. Ebtihal “A outbreak in Guinea, Sierra Leone and Liberia”, Framework of Computational Model for PLoS Curr., Vol. 6, pp. 1-9, 2014. International Journal of Intelligent Engineering and Systems, Vol.14, No.2, 2021 DOI: 10.22266/ijies2021.0430.44

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