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Prediction of the influenza virus propagation by using different epidemiological and machine learning models

  • 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.

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Author:Carina Schmidt
Advisor:Heinrich Krayl, Ahmed Bounekkar
Document Type:Bachelor Thesis
Year of Completion:2019
Publishing Institution:Hochschule Heilbronn
Granting Institution:Hochschule Heilbronn
Release Date:2019/03/05
Tag:Influenzavirus; Multiple Lineare Regression
GND Keyword:Deep learning; Grippe; Maschinelles Lernen; Neuronales Netz
Pagenumber:VIII, 49 Seiten
DDC classes:000 Allgemeines, Informatik, Informationswissenschaft
600 Technik, Medizin, angewandte Wissenschaften
Access Right:Frei zugänglich
Licence (German):License LogoCreative Commons - Namensnennung-Keine Bearbeitung