Recurrent neural network ensemble, a new instrument for the prediction of infectious diseases

• Published in: European physical journal plus 2021-03, Vol.136 (3), p.319-319, Article 319
• Main Author: Puleio, Alessandro
• Format: Article
• Language: English
• Subjects: Algorithms; Applied and Technical Physics; Atomic; Complex Systems; Condensed Matter Physics; Decontamination and Developments of the Decision Support Systems in Case of CBRNe Events; Epidemics; Flues; Focus Point on New Technologies for Detection; Globalization; Infectious diseases; Influenza; Mathematical and Computational Physics; Molecular; Mutation; Neural networks; Optical and Plasma Physics; Pandemics; Pathogens; Physics; Physics and Astronomy; Protection; Recurrent neural networks; Regular; Regular Article; Seasons; Theoretical; Viruses
• ISSN: 2190-5444; 2190-5444
• DOI: 10.1140/epjp/s13360-021-01285-3

Infectious diseases afflict human beings since ancient times. We can classify the infectious disease in two principal types: the emerging diseases, that are caused by new pathogens, and the re-emerging diseases, due to a new spread of a known pathogen. Both types can then be subdivided in natural, accidental or intentional spreads. The risk associated to infectious diseases strongly increased in the last decades, especially because of the globalisation, which leads to a denser and more efficient link between nations, involving that a local infectious may easily spread worldwide, such as the SARS-CoV-2 in 2019–2020. The development of new methods to predict the spread of diseases is crucial. However, sometimes the variables are too many that classical algorithms fail in the prediction. Aim of this work is to investigate the use of an ensemble of recurrent neural networks for disease prediction, using real flu’s data to train and develop an instrument with the capability to determine the future flues. Two different types of study have been conducted. The first study investigates the influence of the neural network architecture, and it has been performed using 12 seasons to train the model and 3 seasons to test it. The second test aims to investigate the number of seasons needed to have a good prediction for future ones. The results demonstrated that this approach could ensure very high performances also with simple architectures. The ensemble approach allows to have information about the uncertainty of the prediction, allowing also to take countermeasures as a function of that value. In the future, the use of this approach may be applied to many other types of disease.