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Boosting Performance of Influenza Outbreak Prediction Framework

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Digital Transformation and Global Society (DTGS 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 745))

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Abstract

The work is dedicated to enhancing the performance of the model calibration framework used for influenza prediction in Russian cities. To increase the speed of calculations and to avoid the decreased fitting accuracy due to local minima problem, the parallelized version of the algorithm is introduced and the comparison of the available optimization methods is performed. The numerical experiments were performed on the data of influenza outbreak incidence in Moscow and Saint Petersburg. The obtained speedup of the calibration algorithm allows to work with bigger incidence datasets and obtain influenza dynamics prediction in more reasonable time compared to the initial serial algorithm. The work results can be applied to enhance performance of different healthcare decision support systems, as well as for increasing the effectiveness of decision making in adjacent areas.

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Acknowledgements

The authors are thankful to the two anonymous referees who helped to improve significantly the quality of the paper. This paper is financially supported by The Russian Scientific Foundation, Agreement #14-21-00137.

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Authors and Affiliations

  1. ITMO University, 49 Kronverksky Pr., 197101, St. Petersburg, Russia

    Nikita E. Seleznev & Vasiliy N. Leonenko

Authors
  1. Nikita E. Seleznev
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  2. Vasiliy N. Leonenko
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Corresponding author

Correspondence to Vasiliy N. Leonenko .

Editor information

Editors and Affiliations

  1. National Research University Higher School of Economics, St. Petersburg, Russia

    Daniel A. Alexandrov

  2. ITMO University, St. Petersburg, Russia

    Alexander V. Boukhanovsky

  3. ITMO University, St. Petersburg, Russia

    Andrei V. Chugunov

  4. National Research University Higher School of Economics, St. Petersburg, Russia

    Yury Kabanov

  5. National Research University Higher School of Economics, St. Petersburg, Russia

    Olessia Koltsova

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Seleznev, N.E., Leonenko, V.N. (2017). Boosting Performance of Influenza Outbreak Prediction Framework. In: Alexandrov, D., Boukhanovsky, A., Chugunov, A., Kabanov, Y., Koltsova, O. (eds) Digital Transformation and Global Society. DTGS 2017. Communications in Computer and Information Science, vol 745. Springer, Cham. https://doi.org/10.1007/978-3-319-69784-0_32

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  • DOI: https://doi.org/10.1007/978-3-319-69784-0_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69783-3

  • Online ISBN: 978-3-319-69784-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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