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 the lack of essential data and uncertainty, the epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-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 proposed 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 9 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. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

WITHDRAWN: A Fuzzy Rule-Based Compartmental Modeling for SARS-CoV-2

“…A number of studies on SARS-CoV-2 have applied these types of algorithms, such as the adaptive neuro-fuzzy inference system (ANFIS), multilayered perceptron-imperialist competitive algorithm (MLP-ICA), Genetic Algorithm (GA) etc. with the traditional compartmental and statistical approaches to achieve better accuracy in prediction conducted by Pinter et al [23], Al-qaness et al [24], Alsayed et al [25] and others. All the classical compartmental models use system of ordinary differential equations (ODE) and totally ignore the different uncertainties or impreciseness involved in the dynamic system of the infection process.…”

Analysis and Prediction of COVID-19 Pandemic in Bangladesh by using Long short-term memory network (LSTM) and Adaptive neuro fuzzy inference system(ANFIS)

“…The ANFIS architecture is modified part of the Artificial Neural Networks (ANNs). With the assimilation of the Takagi-Sugeno fuzzy which is the modification of the fuzzy logic [11,12] system and it prospers a high performance in both computing and learning technique which are dealing with non-linearity [11,12] There are five main layers in the ANFIS model [13]. The layer starts by the input layer which takes in the parameters and then constructs them into the model, this layer is also the input layer of the fuzzy system.…”

“…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 [13]. The ANFIS was implemented on the MATLAB's AN-FIS toolbox.…”

“…The dataset range is from 10 April 2020 to 30 June 2020, for training the dataset it is taken from 10 April, 2020 to 19 June 2020 and the remaining days till 30 June (2020) is taken for the testing the model. It is very important to train the algorithm at the initial step for developing a firm model [13].…”

“…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)) [13]. The time series data of Bangladesh was taken into three scenarios.…”