RETRACTED ARTICLE: A new prediction approach of the COVID-19 virus pandemic behavior with a hybrid ensemble modular nonlinear autoregressive neural network

Abstract

We describe in this paper an approach for predicting the COVID-19 time series in the world using a hybrid ensemble modular neural network, which combines nonlinear autoregressive neural networks. At the level of the modular neural network, which is formed with several modules (ensembles in this case), the modules are designed to be efficient predictors for each country. In this case, an integrator is used to combine the outputs of the modules, in this way achieving the goal of predicting a set of countries. At the level of the ensembles, forming a part of the modular network, these are constituted by a set of modules, which are nonlinear autoregressive neural networks that are designed to be efficient predictors under particular conditions for each country. In each ensemble, the results of the modules are combined with an aggregator to achieve a better and improved result for the ensemble. Publicly available datasets of coronavirus cases around the globe from the last months have been used in the analysis. Interesting conclusions have been obtained that could be helpful in deciding the best strategies in dealing with this virus for countries in their fight against the coronavirus pandemic. In addition, the proposed approach could be helpful in proposing strategies for similar countries.

Review on COVID‐19 diagnosis models based on machine learning and deep learning approaches

“…These methods are used to accomplish the following: (i) determination of how the epidemic will end, (ii) prediction of the coronavirus transmission over regions, (iii) analysis of the expansion rate and forms of cure over different countries, (iv) correlation of the effect of weather condition and coronavirus and (v) analysis of the transmission rate of the virus (M. Yadav et al, 2020 ). Recent studies focused on the use of a logistic forecasting model (Wang, Zheng, et al, 2020 ), neural network‐based prediction model (Wieczorek et al, 2020 ), a hybrid ensemble nonlinear autoregressive neural network ensemble model combining neural networks with type‐2 fuzzy and the firefly algorithm (Melin et al, 2020a ), prediction of COVID 19 (Kavadi et al, 2020 ), prediction of respiratory decompensation in Covid‐19 patients with ML (Burdick et al, 2020 ), development of association rules between weather data and COVID‐19 pandemic for the prediction of death rate (Malki et al, 2020 ), classification of images with target outputs, such as pneumonia, COVID‐19 and healthy lungs in using Q‐deformed entropy and DL features (Hasan et al, 2020 ), supervised ML models for predicting COVID‐19 cases (Rustam et al, 2020 ), analysis of the spatial relationship in the spread of COVID‐19 (Melin et al, 2020b ), evaluation of health opinions and online contents relevant to COVID‐19 with ML (Sear et al, 2020 ) and ML techniques for investigating pandemic COVID 19 effects on young students activities, mental health and learning styles (Khattar et al, 2020 ).…”

Epidemic efficacy of Covid-19 vaccination against Omicron: An innovative approach using enhanced residual recurrent neural network

“…The MSE for RNNCON-Res is calculated to be 4067567.11. The model is also compared with other models like ARIMA [32], BSTS [32], and NAR Neural Model [33]. The ARIMA and BSTS model delivered RMSE of 4391 and 3874 on the HDX dataset, and the NAR Neural Model delivered RMSE of 47366 on the entire data.…”

Optimization in the Context of COVID-19 Prediction and Control: A Literature Review

“…Finally, some studies [16] – [22] , [131] – [146] followed a model-agnostic approach and relied solely on the historical time series data of COVID-19 cases or other relevant predictors to forecast future cases. These methods employ machine learning models (neural networks [17] – [21] , [133] , [135] , [138] , [142] and deep learning [139] ) to make predictions while using various optimization algorithms (such as Gaussian process regression [16] , Bayesian optimization [17] , and metaheuristic algorithms [18] – [22] , [144] , [147] – [149] ) to optimize the model hyperparameters.…”

“…Finally, some studies [16] – [22] , [131] – [146] followed a model-agnostic approach and relied solely on the historical time series data of COVID-19 cases or other relevant predictors to forecast future cases. These methods employ machine learning models (neural networks [17] – [21] , [133] , [135] , [138] , [142] and deep learning [139] ) to make predictions while using various optimization algorithms (such as Gaussian process regression [16] , Bayesian optimization [17] , and metaheuristic algorithms [18] – [22] , [144] , [147] – [149] ) to optimize the model hyperparameters. When the only input was COVID-19 time series data, the optimization model was essentially a curve-fitting problem, where the objective function was to minimize the squared error between the predicted and actual values.…”