# COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach
###### Abstract
Overall, the SIR-based models would be accurate if firstly the status of social interactions is stable. Secondly, class \(R\) can be computed precisely. To better estimate class \(R\), several data sources can be integrated with SIR-based models, e.g., CCTVs, social media, mobile apps, and call data records. Nevertheless, using such systems are reported to still associate with significant complexity and uncertainties [25; 26; 27; 28; 29; 30; 31; 32]. Due to the high level of uncertainties involved in the advancement of SIR-based models, the generalization ability is yet to be improved to achieve a scalable model with high performance [33]. Due to the presence of uncertainties and a high degree of complexity in advancing epidemiological models of the outbreak, machine learning has increasingly been seen as a potential technology. ML has already shown promising results in a contribution to developing better SIR-based models with higher performance with generalization ability and robustness [34; 35; 36; 37; 38]. Machine learning has already been recognized as a computing technique with great potential in outbreak prediction. The notable machine learning algorithms include, e.g., random forest for swine fever [39; 40], neural networks for H1N1 flu, dengue fever, and Oyster norovirus [11; 41; 42], genetic programming for Oyster norovirus [43], classification and regression tree (CART) for Dengue [44], Bayesian Network for Dengue and Aedes [45], LogitBoost for Dengue [46], multi-regression and Naive Bayes for Dengue outbreak prediction [47]. Machine learning has often been used as a complementary computation tool to enhance SIR-based models [11; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49]. Nevertheless, there is a gap in using machine learning in the case of COVID-19. However, several recent research works point out the enormous potential of machine learning for the fight against COVID-19 [49; 50; 51; 52]. Machine learning delivered promising results in several aspects for mitigation and prevention and have been endorsed in the scientific community for, e.g., case identifications [53], classification of novel pathogens [54], modification of SIR-based models [55], diagnosis [56; 57], survival prediction [58], and ICU demand prediction [59]. Furthermore, the non-peer reviewed sources suggest novel applications for fighting COVID-19 [60]. Among the applications of machine learning improvement of the existing models of prediction, identifying vulnerable groups, early diagnosis, the advancement of drugs delivery, evaluation of the probability of next pandemic, the advancement of the integrated systems for Spatio-temporal prediction, evaluating the risk of infection, advancing reliable biomedical knowledge graphs, and data mining the social networks are being noted. As stated in our recent paper, machine learning can be used for data preprocessing. Improving the quality of data can particularly improve the quality of the SIR-based model. For instance, the number of cases reported by Worldometer is not precisely the number of infected cases (\(E\) in the SEIR model), or calculating the number of infectious people (\(I\) in SEIR) cannot be easily determined, as many people who might be infectious may not turn up for testing although the number of people who are admitted to hospital and deceased will not support \(R\) as most COVID-19 positive cases recover without entering the hospital. Considering this data problem, it is challenging to fit SEIR models satisfactorily. Considering such challenges, for future research, the ability of machine learning for estimation of the missing information on the number of exposed \(E\) or infected individuals can be evaluated. Along with the prediction of the outbreak, prediction of the total mortality rate (\(n\)(deaths)/\(n\)(infecteds)) is also essential to accurately estimate the number of potential patients in the critical situation and the required beds in intensive care units.