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

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