# 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. machine learning; prediction model; COVID-19 202020  
different countries, the standard epidemiological models, i.e., susceptible-infected-resistant (SIR)-based models, have been challenged for delivering higher performance in individual nations. Furthermore, as the COVID-19 outbreak showed significant differences with the recent epidemics, e.g., swine fever, H1N1 influenza, Ebola, Cholera, dengue fever, and Zika, several advanced epidemiological models have emerged to provide higher accuracy [7]. Nevertheless, due to the involvement of numerous unknown factors, the outbreaks, various curfew strategies in different countries, and existing double standards for evaluation metrics, the model uncertainty for the COVID-19 is generally reported extensive [8; 9; 10]. As a result, SIR-based models are fundamentally challenged to provide reliable insight into the progress of COVID-19. The general strategy behind the SIR-based models for predicting the COVID-19 outbreak, similar to other epidemics, is formed around the assumption of transmitting the contagious disease through social contacts. The SIR-based models assume that infection spreads through several groups, e.g., susceptible, exposed, infected, recovered, deceased, and immune [11]. For instance, the standard SIR-based models assume that an epidemic includes susceptible to infection (class \(S\)), infected (class \(I\)), and the removed population (class \(R\)). Such principal groups build the foundation of epidemiological modeling. Note that the definition of various classes of the outbreak may vary. For instance, \(R\) is often referred to as those that have recovered, developed immunity, been isolated, or passed away. However, in various countries, \(R\) may or may not be susceptible to infection again, and there exist uncertainties in allocating \(R\) a value. Advancing SIR-based models require several assumptions. Thus, modeling with SIR-based models may include several contradicting assumptions. Nevertheless, in most epidemiological models, it is generally agreed that class \(I\) has a high probability of infecting class \(S\). The transmission probability is proportional to the total social contacts. The transmission can be estimated through the implementation of basic differential equations as follows [12; 13; 14]. \[\frac{dS}{dt}=-\alpha SI \tag{1}\]  
where \(I\), \(S\), and \(\alpha\) represent the infected population, the susceptible population, and the daily reproduction rate, respectively. The time-series of \(S\), which is calculated by the differential equation, decreases gradually. However, it is observed that at the beginning of the pandemics, where the increment \(\frac{dl}{dt}\) is linear, \(S\approx 1\). Eventually, class \(I\) is estimated as follows.