# COVID-19 Outbreak Prediction with Machine Learning
## 5 Conclusions  
The global pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become the primary national security issue of many nations. Advancement of accurate prediction models for the outbreak is essential to provide insights into the spread and consequences of this infectious disease. Due to the high level of uncertainty and lack of crucial data, standard epidemiological models have shown low accuracy for long-term prediction. This paper presents a comparative analysis of ML and soft computing models to predict the COVID-19 outbreak. The results of two ML models (MLP and ANFIS) reported a high generalization ability for long-term prediction. With respect to the results reported in this paper and due to the highly complex nature of the COVID-19 outbreak and differences among nations, this study suggests ML as an effective tool to model the time series of the outbreak. We should note that this paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. For the advancement of higher performance models for long-term prediction, future research should be devoted to comparative studies on various ML models for individual countries. Due to the fundamental differences between the outbreak in various countries, advancement of global models with generalization ability would not be feasible. As observed and reported in many studies, it is unlikely that an individual outbreak will be replicated elsewhere [1]. Although the most difficult prediction is to estimate the maximum number of infected patients, estimation of the individual mortality rate (\(n\)(deaths)/\(n\)(infected)) is also essential. The mortality rate is particularly important to accurately estimate the number of patients and the required beds in intensive care units. For future research, modeling the mortality rate would be of the utmost importance for nations to plan for new facilities. For future research, integration of machine learning and SIR/SEIR models is suggested to enhance the existing standard epidemiological models in terms of accuracy and longer lead time. **Author Contributions:** Conceptualization, S.F.A. and A.M.; methodology, S.F.A. and A.M.; software, A.R.V.-K., P.G., F.F., U.R., T.R. and P.M.A. ; validation, A.R.V.-K., P.G., F.F., U.R., T.R. and P.M.A. ; formal analysis, A.R.V.-K., P.G., F.F., U.R., T.R. and P.M.A. ; investigation, A.R.V.-K., P.G., F.F., U.R., T.R. and P.M.A. ; resources, A.R.V.-K., P.G., F.F., U.R., T.R. and P.M.A. ; data curation, A.R.V.-K., P.G., F.F., U.R., T.R. and P.M.A. ; writing--original draft preparation, S.F.A. and A.M.; writing--review and editing, P.M.A., P.G. ; visualization, S.F.A. and A.M.; project administration, A.M.; funding acquisition, A.M. All authors have read and agreed to the published version of the manuscript. **Funding:** This research is supported within the project of \"Support of research and development activities of the J. Selye University in the field of Digital Slovakia and creative industry\" of the Research and Innovation Operational Programme (ITMS code: NF9313010T504) co-funded by the European Regional Development Fund. **Acknowledgments:** We acknowledge the financial support of this work by the Hungarian-Mexican bilateral Scientific and Technological (2019-2.1.11-TET-2019-00007) project. The research presented in this paper was carried out as part of the EFOP-3.6.2-16-2017-00016 project in the framework of the New Szechenyi Plan. The completion of this project is funded by the European Union and co-financed by the European Social Fund. **Conflicts of Interest:** The authors declare no conflict of interest. **Nomenclature**