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Epidemiologic Evidence Prediction based on Clinical and Open Data

Student: Demchenko Stanislav

Supervisor: Alexey Neznanov

Faculty: Faculty of Computer Science

Educational Programme: Mathematical Methods of Optimization and Stochastics (Master)

Year of Graduation: 2018

This paper is aimed to study how to effectively predict epidemiological situation based on clinical and open data. Classical approaches used to forecast epidemics were examined. A fresh approach based on deep learning models was proposed. Based on influenza situation in England, It was proven that modern deep learning models can almost perfectly predict dynamics of epidemics. It was also shown that we can improve estimation by adding some information from open sources (data from twitter). It increased the accuracy of the model.

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