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International Conference on Computer Science, Engineering and Education Applications

ICCSEEA 2018: Advances in Computer Science for Engineering and Education pp 555–566Cite as

Quality of Symptom-Based Diagnosis of Rotavirus Infection Based on Mathematical Modeling

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 754))

Abstract

Rotavirus is the leading cause of severe childhood gastroenteritis worldwide. The laboratory diagnosis requires testing of fecal specimens with commercial assays that often are not available in low resource settings. Therefore, estimation of rotavirus presence based on clinical symptoms is expected to improve the disease management without laboratory verification.

We aimed to develop and compare different mathematical approaches to model-based evaluation of expected rotavirus presence in patients with similar clinical symptoms. Two clinical datasets were used to develop clinical evaluation models of rotavirus presence or absence based on Bayesian network (BN), linear and nonlinear regression.

The developed models produced different levels of reliability. BN compared with regression models showed better rotavirus detection results according to optimal cut-off points. Such approach is viable to help physicians refer patient to the group with suspected rotavirus infection to avoid unnecessary antibiotic treatment and to prevent rotavirus infection spread in a hospital ward.

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Acknowledgements

The authors thank the Indonesia Endowment Fund for Education (LPDP) for funding Ph.D. fellowship to Mohamad S. Hakim.

Author information

Authors and Affiliations

  1. Department of Applied Mathematics, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine

    Serhii O. Soloviov

  2. Department of Virology, Shupyk National Medical Academy of Postgraduate Education, Kyiv, Ukraine

    Serhii O. Soloviov & Iryna V. Dzyublyk

  3. Department of Gastroenterology and Hepatology, Erasmus MC-University Medical Center, Rotterdam, The Netherlands

    Mohamad S. Hakim & Qiuwei Pan

  4. Department of Microbiology, Faculty of Medicine, Universitas Gadjah Mada, Yogyakarta, Indonesia

    Mohamad S. Hakim, Hera Nirwati & Abu T. Aman

  5. Department of Child Health, Faculty of Medicine, Universitas Gadjah Mada, Yogyakarta, Indonesia

    Yati Soenarto

  6. Department of Public Health, Babeș-Bolyai University, Cluj-Napoca, Romania

    Tatiana I. Andreeva

  7. Alcohol and Drug Information Center, Kyiv, Ukraine

    Tatiana I. Andreeva

Authors
  1. Serhii O. Soloviov
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  2. Mohamad S. Hakim
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  3. Hera Nirwati
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  4. Abu T. Aman
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  5. Yati Soenarto
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  6. Qiuwei Pan
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  7. Iryna V. Dzyublyk
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  8. Tatiana I. Andreeva
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Corresponding author

Correspondence to Serhii O. Soloviov .

Editor information

Editors and Affiliations

  1. School of Educational Information Technology, Central China Normal University, Wuhan, Hubei, China

    Zhengbing Hu

  2. Mechanical Engineering Research Institute, Russian Academy of Sciences, Moscow, Russia

    Sergey Petoukhov

  3. Faculty of Applied Mathematics, National Technical University of Ukraine “Igor Sikorsky Kiev Polytechnic Institute”, Kiev, Ukraine

    Ivan Dychka

  4. Halmos College of Natural Sciences and Oceanography, Nova Southeastern University, Ft. Lauderdale, Florida, USA

    Matthew He

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The authors declare to have no conflict of interest.

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Soloviov, S.O. et al. (2019). Quality of Symptom-Based Diagnosis of Rotavirus Infection Based on Mathematical Modeling. In: Hu, Z., Petoukhov, S., Dychka, I., He, M. (eds) Advances in Computer Science for Engineering and Education. ICCSEEA 2018. Advances in Intelligent Systems and Computing, vol 754. Springer, Cham. https://doi.org/10.1007/978-3-319-91008-6_56

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