Skip to main content
SpringerLink
Log in

Viral Diseases Propagation Analysis in Short Time

  • Conference paper
  • First Online:
Cloud Computing, Big Data & Emerging Topics (JCC-BD&ET 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1291))

Included in the following conference series:

Abstract

Studying potentially harmful infectious agents for some population and trying to explain and predicts how the disease evolves in the time are difficult because many factors interactions. An solution is to analyse real systems by mean of simulations models. In these cases, Cellular Automata have been used with success, they can recreate a virtual world take account problem main features and their correlations. We developed an efficient and portable cellular automata model in Graphic Processing Units to simulate viral diseases propagation. The achieved efficiency allows us estimate in a short time the viral disease behaviour when it is known or not, as well as its associated uncertainty. Besides, it is suitable to test effects of different measures that tending towards stop the spread. We describe the solution and evaluate it for two viral diseases: Seasonal Influenza and COVID-19.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Beauchemin, C., Samuel, J., Tuszynski, J.: A simple cellular automaton model for influenza a viral infections. J. Theor. Biol. 232(2), 223–234 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  2. Casares, F., Tissera, P., Piccoli, F.: A parallel proposal for seir model using cellular automata. In: XXII Congreso Argentino de Ciencias de la Computación (CACIC 2016), pp. 208–219 (2016)

    Google Scholar 

  3. Gardner, M.: Mathematical games. Sci. Am. 222(1), 124–127 (1970)

    Article  Google Scholar 

  4. Gaudiani, A., Luque, E., Garcia, P., Naiouf, M., De Giusti, A.: Optimización y computación paralela aplicados a mejorar la predicción de un simulador de cauce de ríos. In: XXII Congreso Argentino de Ciencias de la Computación (CACIC 2016), pp. 179–188 (2016)

    Google Scholar 

  5. Gu, Y., Ding, J.: Research on rumors spread based on cellular automata. In: Yang, Y., Ma, M. (eds.) Proceedings of the 2nd International Conference on Green Communications and Networks 2012 (GCN 2012): Volume 1. Lecture Notes in Electrical Engineering, vol. 223. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-35419-9_28

  6. Han, J., Sharma, B.: Learn CUDA Programming: A Beginner’s Guide to GPU Programming and Parallel Computing with CUDA 10.x and C/C++. Packt Publishing, Birmingham (2019)

    Google Scholar 

  7. Kaeli, D.R., Mistry, P., Schaa, D., Zhang, D.P.: Heterogeneous Computing with OpenCL 2.0. Elsevier, Amsterdam (2015)

    Google Scholar 

  8. Kauffman, S.: Emergent properties in random complex automata. Phys. D: Nonlinear Phenom. 10(1), 145–156 (1984)

    Article  MathSciNet  Google Scholar 

  9. Kermack, W., McKendrick, A., Walker, G.: A contribution to the mathematical theory of epidemics. Proc. R. Soc. Lond. Ser. A Contain. Pap. Math. Phys. Character 115(772), 700–721 (1927)

    MATH  Google Scholar 

  10. Kier, L.B., Seybold, P.G., Cheng, C.K.: Modeling Chemical Systems Using Cellular Automata. Springer, Heidelberg (2005). https://doi.org/10.1007/1-4020-3690-6

    Book  Google Scholar 

  11. Kirk, D., Hwu, W.: Programming Massively Parallel Processors, A Hands on Approach. Morgan Kaufmann, Elsevier, Burlington (2010)

    Google Scholar 

  12. Kurgalin, S., Borzunov, S.: Implementation of parallel algorithms. A Practical Approach to High-Performance Computing, pp. 93–115. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-27558-7_6

    Chapter  Google Scholar 

  13. Li, X., Wu, J., Li, X.: Concluding remarks—looking to the future. Theory of Practical Cellular Automaton, pp. 323–352. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-7497-4_9

    Chapter  MATH  Google Scholar 

  14. McMillen, C.W.: Pandemics: A Very Short Introduction. Very Short Introductions. Oxford University Press, Oxford (2016)

    Google Scholar 

  15. World Health Organization: Influenza (seasonal) (2018). Fact sheet

    Google Scholar 

  16. World Health Organization: Coronavirus disease (covid-19) (2020). Situation Report 116

    Google Scholar 

  17. Owens, J., Houston, M., Luebke, D., Green, S., Stone, J., Phillips, J.: GPU computing. IEEE 96, 879–899 (2008)

    Google Scholar 

  18. Pacheco, P.: An Introduction to Parallel Programming. Elsevier, Amsterdam (2011)

    Google Scholar 

  19. Tissera, P., Castro, A., Printista, A., Luque, E.: Simulating behaviours to face up an emergency evacuation. CoRR arxiv:1401.5209 (2014)

  20. Wolfram, S.: Universality and complexity in cellular automata. Phys. D: Nonlinear Phenom. 10(1), 1–35 (1984)

    MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. LIDIC, Universidad Nacional de San Luis, San Luis, Argentina

    Maximiliano Lucero, Natalia Miranda & Fabiana Piccoli

Authors
  1. Maximiliano Lucero
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Natalia Miranda
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fabiana Piccoli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fabiana Piccoli .

Editor information

Editors and Affiliations

  1. III-LIDI, Facultad de Informatica, Universidad Nacional de La Plata and CIC, La Plata, Argentina

    Enzo Rucci

  2. III-LIDI, Facultad de Informática, Universidad Nacional de La Plata and CIC, La Plata, Argentina

    Marcelo Naiouf

  3. III-LIDI, Facultad de Informática, Universidad Nacional de La Plata and CIC, La Plata, Argentina

    Franco Chichizola

  4. III-LIDI, Facultad de Informática, Universidad Nacional de La Plata and CIC, La Plata, Argentina

    Laura De Giusti

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lucero, M., Miranda, N., Piccoli, F. (2020). Viral Diseases Propagation Analysis in Short Time. In: Rucci, E., Naiouf, M., Chichizola, F., De Giusti, L. (eds) Cloud Computing, Big Data & Emerging Topics. JCC-BD&ET 2020. Communications in Computer and Information Science, vol 1291. Springer, Cham. https://doi.org/10.1007/978-3-030-61218-4_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-61218-4_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61217-7

  • Online ISBN: 978-3-030-61218-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation