Efficient artificial intelligence forecasting models for COVID-19 outbreak in Russia and Brazil

COVID-19 is a new member of the Coronaviridae family that has serious effects on respiratory, gastrointestinal, and neurological systems. COVID-19 spreads quickly worldwide and affects more than 41.5 million persons (till 23 October 2020). It has a high hazard to the safety and health of people all over the world. COVID-19 has been declared as a global pandemic by the World Health Organization (WHO). Therefore, strict special policies and plans should be made to face this pandemic. Forecasting COVID-19 cases in hotspot regions is a critical issue, as it helps the policymakers to develop their future plans. In this paper, we propose a new short term forecasting model using an enhanced version of the Adaptive Neuro-Fuzzy Inference System (ANFIS). An improved Marine Predators Algorithm (MPA), called Chaotic MPA (CMPA), is applied to enhance the ANFIS and to avoid its shortcomings. More so, we compared the proposed CMPA with three artificial intelligence-based models include the original ANFIS, and two modified versions of ANFIS model using both of the original Marine Predators Algorithm (MPA) and Particle Swarm Optimization (PSO). The forecasting accuracy of the models was compared using different statistical assessment criteria. CMPA significantly outperformed all other investigated models.

[1]  Vinay Kumar Reddy Chimmula,et al.  Time series forecasting of COVID-19 transmission in Canada using LSTM networks , 2020, Chaos, Solitons & Fractals.

[2]  Mohamed Abd Elaziz,et al.  An enhanced productivity prediction model of active solar still using artificial neural network and Harris Hawks optimizer , 2020 .

[3]  Antanas Verikas,et al.  Agreeing to disagree: active learning with noisy labels without crowdsourcing , 2017, International Journal of Machine Learning and Cybernetics.

[4]  Ahmed A. Ewees,et al.  Improved Adaptive Neuro-Fuzzy Inference System Using Gray Wolf Optimization: A Case Study in Predicting Biochar Yield , 2018, J. Intell. Syst..

[5]  Lei Cao,et al.  Estimating the instant case fatality rate of COVID-19 in China , 2020, International Journal of Infectious Diseases.

[6]  Ronghua Xu,et al.  Delivery of infection from asymptomatic carriers of COVID-19 in a familial cluster , 2020, International Journal of Infectious Diseases.

[7]  Mohamed Abd Elaziz,et al.  A Modified Adaptive Neuro-Fuzzy Inference System Using Multi-Verse Optimizer Algorithm for Oil Consumption Forecasting , 2019, Electronics.

[8]  T. Chakraborty,et al.  Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis , 2020, Chaos, Solitons & Fractals.

[9]  Hong Fan,et al.  Optimization Method for Forecasting Confirmed Cases of COVID-19 in China , 2020, Journal of clinical medicine.

[10]  Mohammed A A Al-Qaness,et al.  Marine Predators Algorithm for Forecasting Confirmed Cases of COVID-19 in Italy, USA, Iran and Korea , 2020, International journal of environmental research and public health.

[11]  Amal I. Saba,et al.  Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks , 2020, Process Safety and Environmental Protection.

[12]  Liangxu Wang,et al.  Prediction of the COVID-19 spread in African countries and implications for prevention and control: A case study in South Africa, Egypt, Algeria, Nigeria, Senegal and Kenya , 2020, Science of The Total Environment.

[13]  Mohammed H. Alsharif,et al.  Evaluation and forecasting of solar radiation using time series adaptive neuro‐fuzzy inference system: Seoul city as a case study , 2019, IET Renewable Power Generation.

[14]  Labode Popoola,et al.  ONLINE FORECASTING OF COVID-19 CASES IN NIGERIA USING LIMITED DATA , 2020, Data in Brief.

[15]  Mohamed Elhoseny,et al.  Prediction of biochar yield using adaptive neuro-fuzzy inference system with particle swarm optimization , 2017, 2017 IEEE PES PowerAfrica.

[16]  Mohamed Abd Elaziz,et al.  Modeling of friction stir welding process using adaptive neuro-fuzzy inference system integrated with harris hawks optimizer , 2019, Journal of Materials Research and Technology.

[17]  Xiaoru Wang,et al.  A novel hybrid methodology for short-term wind power forecasting based on adaptive neuro-fuzzy inference system , 2017 .

[18]  Hiroshi Nishiura,et al.  The Rate of Underascertainment of Novel Coronavirus (2019-nCoV) Infection: Estimation Using Japanese Passengers Data on Evacuation Flights , 2020, Journal of clinical medicine.

[19]  F. Amenta,et al.  COVID-19 virus outbreak forecasting of registered and recovered cases after sixty day lockdown in Italy: A data driven model approach , 2020, Journal of Microbiology, Immunology and Infection.

[20]  K. C. Santosh,et al.  AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data , 2020, Journal of Medical Systems.

[21]  Ahmed A. Ewees,et al.  Improving Adaptive Neuro-Fuzzy Inference System Based on a Modified Salp Swarm Algorithm Using Genetic Algorithm to Forecast Crude Oil Price , 2019, Natural Resources Research.

[22]  Guoqiang Sun,et al.  Clinical analysis of ten pregnant women with COVID-19 in Wuhan, China: A retrospective study , 2020, International Journal of Infectious Diseases.

[23]  Amir H. Gandomi,et al.  Marine Predators Algorithm: A nature-inspired metaheuristic , 2020, Expert Syst. Appl..

[24]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[25]  Daozhou Gao,et al.  Estimating the Unreported Number of Novel Coronavirus (2019-nCoV) Cases in China in the First Half of January 2020: A Data-Driven Modelling Analysis of the Early Outbreak , 2020, Journal of clinical medicine.

[26]  Sarbjit Singh,et al.  Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19 , 2020, Chaos, Solitons & Fractals.

[27]  Danna Zhou,et al.  d. , 1934, Microbial pathogenesis.

[28]  Abdon Atangana,et al.  Modeling and forecasting of epidemic spreading: The case of Covid-19 and beyond , 2020, Chaos, Solitons & Fractals.

[29]  Q. Fu,et al.  Possible environmental effects on the spread of COVID-19 in China , 2020, Science of The Total Environment.

[30]  Mohamed Abd Elaziz,et al.  Noise prediction of axial piston pump based on different valve materials using a modified artificial neural network model , 2019, Alexandria Engineering Journal.

[31]  Jue Liu,et al.  Effects of temperature and humidity on the daily new cases and new deaths of COVID-19 in 166 countries , 2020, Science of The Total Environment.

[32]  Mohamed Abd Elaziz,et al.  Improved prediction of oscillatory heat transfer coefficient for a thermoacoustic heat exchanger using modified adaptive neuro-fuzzy inference system , 2019, International Journal of Refrigeration.

[33]  M. Abd Elaziz,et al.  Modeling of solar energy systems using artificial neural network: A comprehensive review , 2019, Solar Energy.

[34]  Mohammed A. A. Al-qaness,et al.  Oil Consumption Forecasting Using Optimized Adaptive Neuro-Fuzzy Inference System Based on Sine Cosine Algorithm , 2018, IEEE Access.

[35]  D. Wraith,et al.  Time series modelling to forecast the confirmed and recovered cases of COVID-19. , 2020, Travel medicine and infectious disease.

[36]  Jungsoon Choi,et al.  Spatial epidemic dynamics of the COVID-19 outbreak in China , 2020, International Journal of Infectious Diseases.

[37]  Dimitrios Zissis,et al.  Adaptive neuro fuzzy inference system for vessel position forecasting , 2017, 2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC).

[38]  Mohamed Abd Elaziz,et al.  A Novel Method for Predicting Tensile Strength of Friction Stir Welded AA6061 Aluminium Alloy Joints Based on Hybrid Random Vector Functional Link and Henry Gas Solubility Optimization , 2020, IEEE Access.

[39]  A. Ahmar,et al.  SutteARIMA: Short-term forecasting method, a case: Covid-19 and stock market in Spain , 2020, Science of The Total Environment.

[40]  Mohammad Reza Mahmoudi,et al.  Time series modelling to forecast the confirmed and recovered cases of COVID-19 , 2020, Travel Medicine and Infectious Disease.