On analysis of complex administrative data: neural networks, modelling and prediction

Abstract

Eco-geographical heterogenicity of countries such as Chile and in cities exhibiting a territorial and demographic important diversity is relevant for the epidemiological studies of the apparition and spread of SARS-CoV-2. That situation is the opposite to countries such as the Czech Republic, with small or less diverse territories where the apparition and spread of SARS-CoV-2 can be correlated mainly to demographic and seasonal variables more than climate, pollution, or other physical and biological variables. It is well visible that there is no simple model for measured active cases and given parameters. This motivates a more general question to develop models that predict the future number of reported COVID-19 cases (by laboratory testing). We tune several neural networks to show the complexity issues of such a problem. These predictions are made for the countries, Austria, Czech Republic, and Slovakia, with the model class used for making these predictions being the artificial neural network, for the data from February 2020 until February 2021. Two different architectures of the neural network are compared the feed-forward network and the recurrent neural network. Ultimately, it is found that there are notable differences between the three countries studied, with the data for the Czech Republic being easier to predict with good accuracy than the data from the other two countries. Likewise, it turns out that the feed-forward approach delivers better results for Austria and the Czech Republic, whereas, for Slovakia, the recurrent approach performs better. Likewise, it is found that combining the data from all three countries does not lead to improved accuracy compared to models using data from only one single country. Both of the findings mentioned above might be related to the relatively small amount of data available.