Investigation of robustness of hybrid artificial neural network with artificial bee colony and firefly algorithm in predicting COVID-19 new cases: case study of Iran

Abstract

As an ongoing public health menace, the novel coronavirus pandemic has challenged the world. With several mutations and a high transmission rate, the virus is able to infect individuals in an exponential manner. At the same time, Iran is confronted with multiple wave peaks and the health care system is facing a major challenge. In consequence, developing a robust forecasting methodology can assist health authorities for effective planning. In that regard, with the help of Artificial Neural Network-Artificial Bee Colony (ANN-ABC) and Artificial Neural Network- Firefly Algorithm (ANN-FA) as two robust hybrid artificial intelligence-based models, the current study intends to select the optimal model with the maximum accuracy rate. To do so, first a sample of COVID-19 confirmed cases in Iran ranging from 19 February 2020 to 25 July 2021 is compiled. 75% (25%) of total observation is randomly allocated as training (testing) data. Afterwards, an ANN model is trained with Levenberg–Marquardt algorithm. Accordingly, based on R-squared and root-mean-square error criteria, the optimal number of hidden neurons is computed as 17. The proposed ANN model is employed to develop ANN-ABC and ANN-FA models for achieving the maximum accuracy rate. According to ANN-ABC, the R- squared values of the optimal model are 0.9884 and 0.9885 at train and test stages. In respect to ANN-FA, the R-squared ranged from 0.9954 to 0.9940 at the train and test phases, which indicates the outperformance of ANN-FA for predicting COVID-19 new cases in Iran. Finally, the proposed ANN-ABC and ANN-FA are applied for simulating the COVID-19 new cases data in different countries. The results revealed that both models can be used as a robust predictor of COVID-19 data and in a majority of cases ANN-FA outperforms the ANN-ABC.

Machine learning in epidemiology: Neural networks forecasting of monkeypox cases

“…Overfitting is a common issue with ANN models, where the model tends to learn noise in the data rather than the actual signals, leading to poor performance on untested datasets. To mitigate this, K-fold cross-validation is employed as a robust method [ 44 , 45 ]. In this technique, the data is randomly divided into K groups.…”

Investigating the effect of vaccinated population on the COVID-19 prediction using FA and ABC-based feed-forward neural networks

“…In comparison with ARIMA, the hybrid approach indicated a lower error rate. A combination of the neural network with firefly and bee algorithms has been proposed for modeling the COVID-19 daily cases [ 39 ]. Based on the results, it can be concluded that both models were the robust forecaster of the pandemic in various countries.…”

“…The ascendancy of ABC and FA over other common meta-heuristic algorithms (e.g., genetic algorithm and particle swarm optimization) in unravelling myriad problems has been highlighted by various studies [ [49] , [50] , [51] , [52] , [53] ]. Moreover, these algorithms have demonstrated a robust accuracy rate in predicting the COVID-19 cases in the country level [ 39 ]. Therefore, the objective of this study is to develop the ANN-FA and ANN-ABC for predicting COVID-19 confirmed cases by considering the vaccinated population.…”

A new hybrid prediction model of COVID-19 daily new case data

“…To improve the prediction accuracy, researchers introduced intelligent models. For COVID-19 prediction, researchers have chosen artificial neural network (ANN) ( Shaibani et al, 2021 , Adnan et al, 2022 ), support vector machine (SVM) ( Challab and Mardukhi, 2021 , Kwak and Soo, 2020 ), extreme learning machine (ELM) ( Cui et al, 2021 ), kernel extreme learning machine (KELM) ( Shi et al, 2021 ), long short-term memory (LSTM) ( Luo et al, 2021 , Rauf et al, 2021 ). The above intelligent models improve the prediction effect to some extent, but the selection of parameters is artificial and uncertain, and the prediction effect is not satisfactory.…”

“… The prediction effect is limited. Multiple linear regression model ( Alemu, 2020 ) Logistic regression model ( Elhag et al, 2021 ) Intelligent model ANN ( Shaibani et al, 2021 ) The prediction effect is greatly improved. To some extent, the prediction effect is improved, but the selection of parameters is artificial and uncertain, and the prediction effect is not ideal.…”