Efficient Artificial Intelligence Forecasting Models for COVID-19 Outbreak in Russia and Brazil

Al-qaness, Mohammed A.A.; Saba, Amal I.; Elsheikh, Ammar H.; Elaziz, Mohamed Abd; Ibrahim, Rehab Ali; Lu, Songfeng; Hemedan, Ahmed Abdelmonem; Shanmugan, S.; Ewees, Ahmed A.

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.