COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach

Abstract

Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptibleinfected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

Analysis and Prediction of COVID-19 Pandemic in Bangladesh by Using ANFIS and LSTM Network

“…There are five main layers in the ANFIS model [ 17 ]. The layer starts with the input layer, which takes in the parameters and then constructs them into the model.…”

“…The second last layer normalizes the functions, and the nodes facilitate the production of the outputs and finally send them to the final layer, which is the output layer. Furthermore, the accuracy of the ANFIS model is determined using the number and type of MFs, the optimum method, and the output of the MF type [ 17 ]. The input parameters are set as the independent variables on each scenario, and the outcome was the number of cases.…”

“…They collected data from other authentic sources. Many researchers are doing their research by collecting the data from this online source [ 17 , 20 ]. The dataset range is from 10 April 2020 to 30 June 2020, for training the dataset is taken from 10 April 2020 to 19 June 2020, and the remaining days till 30 June (2020) is taken for testing the model.…”

“…The dataset range is from 10 April 2020 to 30 June 2020, for training the dataset is taken from 10 April 2020 to 19 June 2020, and the remaining days till 30 June (2020) is taken for testing the model. It is essential to train the algorithm at the initial step for developing a firm model [ 17 ]. Moreover, a forecasting model is recommended to be classified into two categories, i.e., input(s) (as independent variable(s)) and output(s) (as dependent variable(s)) [ 17 ].…”

“…It is essential to train the algorithm at the initial step for developing a firm model [ 17 ]. Moreover, a forecasting model is recommended to be classified into two categories, i.e., input(s) (as independent variable(s)) and output(s) (as dependent variable(s)) [ 17 ]. The time-series data of Bangladesh was taken into three scenarios.…”