000 Allgemeines, Informatik, Informationswissenschaft
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In this bachelor thesis, different models for predicting the influenza virus are
examined in more detail.
The focus is on epidemiological compartmental models, as well as on different
Machine Learning approaches.
In particular, the basics chapter presents the SIR model and its various extensions.
Furthermore, Deep Learning and Social Network approaches are
investigated and the applied methods of a selected article are analysed in more
detail.
The practical part of this work consists in the implementation of a Multiple
Linear Regression model and an Artificial Neural Network. For the development
of both models the programming language Python was chosen using the
Deep Learning Framework Keras.
Tests were performed with real data from the Réseau Sentinelles, a French
organisation for monitoring national health.
The results of the tests show that the Neural Network is able to make better
predictions than the Multiple Linear Regression model.
The discussion shows ideas for improving influenza prediction including the
establishment of a worldwide collaboration between the surveillance centres as
well as the consolidation of historical data with real-time social media data.
Therefore, this work consists of a state-of-the art of models regarding the
spread of influenza virus, the development and comparison of several models
programmed in Python, evaluated on real data.