Abstract
In this paper, we present an approach on how to predict the coronavirus spread per country from the country-specific socio-economic indicators. To this end, firstly, we describe in detail how the growth of COVID-19 cases can be represented with a parameterized exponential curve. Then, having collected and pre-processed various country rankings, statistics and indicators of socio-economic circumstances of a country, we constructed an adequate dataset of 116 countries. In order to predict the behavior of the coronavirus spread behavior, we employed machine learning algorithms, i.e., regression and classification approach. Since the dataset is unlabelled, we also made use of the clustering methods. In essence, the results of the regression analysis indicate a strong relationship between countries’ socio-economic indicators and the behavior of the coronavirus number of novel cases. Whereas, due to the lack of labeled dataset, the classification method results in a rather poor performance.
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Altwlkany, K., Ražanica, E., Mijatović, N., Delić, A. (2021). Predicting the Coronavirus Spread Based on Countries’ Long-Term Socio-Economic Indicators. In: Hasic Telalovic, J., Kantardzic, M. (eds) Mediterranean Forum – Data Science Conference. MeFDATA 2020. Communications in Computer and Information Science, vol 1343. Springer, Cham. https://doi.org/10.1007/978-3-030-72805-2_1
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