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.

doi:10.1016/j.psep.2020.11.007

Cited by 71 publications

(50 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Since the outbreak of COVID-19, researchers worldwide have been carrying out a lot of research works on it. These researches can be mainly divided into the following six categories: (1) to study the impact of COVID-19 on human physical and mental health from a biomedical perspective ( Tsamakis et al, 2020 , Xiong et al, 2020 , Pascoal et al, 2021 ); (2) to study the impact of COVID-19 on human production, life, and social and economic development from a sociological perspective ( Takyi and Bentum-Ennin, 2020 , Qian et al, 2021 , Shang et al, 2021 , Beiderbeck et al, 2021 , Jiang et al, 2021 ); (3) to creatively propose new mathematical models or revise some existing models based on relevant data for predicting and analyzing the development of the epidemic in a specific area ( Vianello et al, 2021 , Willis et al, 2021 , Mun and Geng, 2021 , Al-qaness et al, 2021 , Manenti et al, 2020 , Hu et al, 2020 , Cao et al, 2020 , Mojjada et al, 2020 , Yang et al, 2020 ); (4) to analyze the spatial-temporal characteristics of the epidemic in a specific area ( Lv and Cheng, 2020 , Feng et al, 2020 ); (5) to explore related factors which may affect the development of the epidemic ( Hu et al, 2021 ); (6) to evaluate the effects of different epidemic prevention measures ( Leung et al, 2020 , Hasnain et al, 2020 ). In terms of the research purpose and content, the third, the fourth, and the fifth categories are more relevant to the work carried out in this paper.…”

Section: Related Workmentioning

confidence: 99%

“…The experiments based on the observed data from 127 countries during the initial phase of the COVID-19 pandemic have validated their model's superiority because it can remove an implicit assumption on reaction order in the classic SIR compartmental models to be more general, flexible, and accurate. Al-qaness et al (2021) 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 avoid its shortcomings.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Analysis on the spatio-temporal characteristics of COVID-19 in mainland China

Jin

Yang

et al. 2021

Process Safety and Environmental Protection

View full text Add to dashboard Cite

COVID-19 has brought many unfavorable effects on humankind and taken away many lives. Only by understanding it more profoundly and comprehensively can it be soundly defeated. This paper is dedicated to studying the spatial-temporal characteristics of the epidemic development at the provincial-level in mainland China and the civic-level in Hubei Province. Moreover, a correlation analysis on the possible factors that cause the spatial differences in the epidemic's degree is conducted. After completing these works, three different methods are adopted to fit the daily-change tendencies of the number of confirmed cases in mainland China and Hubei Province. The three methods are the Logical Growth Model (LGM), Polynomial fitting, and Fully Connected Neural Network (FCNN). The analysis results on the spatial-temporal differences and their influencing factors show that: (1) The Chinese government has contained the domestic epidemic in early March 2020, indicating that the number of newly diagnosed cases has almost zero increase since then. (2) Throughout the entire mainland of China, effective manual intervention measures such as community isolation and urban isolation have significantly weakened the influence of the subconscious factors that may impact the spatial differences of the epidemic. (3) The classification results based on the number of confirmed cases also prove the effectiveness of the isolation measures adopted by the governments at all levels in China from another aspect. It is reflected in the small monthly grade changes (even no change) in the provinces of mainland China and the cities in Hubei Province during the study period. Based on the experimental results of curve-fitting and considering the time cost and goodness of fit comprehensively, the Polynomial( Degree = 18) model is recommended in this paper for fitting the daily-change tendency of the number of confirmed cases.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Analysis on the spatio-temporal characteristics of COVID-19 in mainland China

Jin

Yang

et al. 2021

Process Safety and Environmental Protection

View full text Add to dashboard Cite

show abstract

“…Sjödin et al [10] introduced a compartmental epidemiological model based on the SEIR formulation, and extended it to account for additional variables including compartments for health and intensive care unit (ICU) care. Al-Qaness et al [11] proposed a new short-term forecasting model using an enhanced version of the adaptive neuro-fuzzy inference system (ANFIS), which has a significantly better chaotic marine predators algorithm (CMPA) than other survey models. Odagaki et al [12] reformulated a SIQR (susceptible, quarantined, infectious, recovered) model to be appropriate to COVID-19, and the exact properties of the model were presented.…”

Section: Introductionmentioning

confidence: 99%

Stability and optimal control strategies for a novel epidemic model of COVID-19

et al. 2021

View full text Add to dashboard Cite

In this paper, a novel two-stage epidemic model with a dynamic control strategy is proposed to describe the spread of Corona Virus Disease 2019 in China. Combined with local epidemic control policies, an epidemic model with a traceability process is established. We aim to investigate the appropriate control strategies to minimize the control cost and ensure the normal operation of society under the premise of containing the epidemic. This work mainly includes: (i) propose the concept about the first and the second waves of COVID-19, as well as study the case data and regularity of four cities; (ii) derive the existence and stability of the equilibrium, the parameter sensitivity of the model, and the existence of the optimal control strategy; (iii) carry out the numerical simulation associated with the theoretical results and construct a dynamic control strategy and verify its feasibility.

show abstract

“…Nature-inspired optimizers [20]- [22] have been widely applied to ANFIS parameter estimation and have demonstrated significant success in diverse real-life applications such as epidemiological disease predictions, for example see [23]. Performance of ANFIS can be enhanced using nature-inspired optimization approaches [24]- [26] to predicts confirmed cases of COVID-19 using time-series data; however, those methods basically focused on forecasting epidemic virus spread using daily number of cases, where each time of the day is correlated with the number of observed cases on that particular day, though Al-qaness et al [25] trained ANFIS using air quality index time series data to predict PM 2.5 and relates it's effects to COVID-19 restriction order. It is desirable to train ANFIS for predicting impacts of climate condition on respiratory viruses such as COVID- 19.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven AI-Based Parameters Tuning Using Grid Partition Algorithm for Predicting Climatic Effect on Epidemic Diseases

Abdullahi¹,

Muangchoo

Abubakar

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Adaptive Neuro-fuzzy Inference System (ANFIS) remains one of the promising AI techniques to handle data over-fitting and as well, improves generalization. Presently, many ANFIS optimization techniques have been synergized and found effective at some points through trial and error procedures. In this work, we tune ANFIS using Grid partition algorithm to handle unseen data effectively with fast convergence. This model is initialized using a careful selection of effective parameters that discriminate climate conditions; minimum temperature, maximum temperature, average temperature, wind speed and relative humidity. These parameters are used as inputs for ANFIS, whereas confirmed cases of COVID-19 is chosen as dependent values for two consecutive months and first ten days of December for new COVID-19 confirmed cases according to the Department of disease control (DDC) Thailand. The proposed ANFIS model provides outstanding achievement to predict confirmed cases of COVID-19 with R 2 of 0.99. Furthermore, data set trend analysis is done to compare fluctuations of daily climatic parameters, to satisfy our proposition, and illustrates the serious effect of these parameters on COVID-19 epidemic virus spread.

show abstract

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

Cited by 71 publications

References 35 publications

Analysis on the spatio-temporal characteristics of COVID-19 in mainland China

Analysis on the spatio-temporal characteristics of COVID-19 in mainland China

Stability and optimal control strategies for a novel epidemic model of COVID-19

Data-Driven AI-Based Parameters Tuning Using Grid Partition Algorithm for Predicting Climatic Effect on Epidemic Diseases

Contact Info

Product

Resources

About