IDEAS home Printed from https://ideas.repec.org/a/wsi/acsxxx/v21y2018i05ns0219525918500091.html
   My bibliography  Save this article

Evolution Of Influenza A Nucleotide Segments Through The Lens Of Different Complexity Measures

Author

Listed:
  • IGOR BALAZ

    (Laboratory for Biophysics, Physics and Meteorology, University of Novi Sad, Trg Dositeja Obradovića 8, 21000 Novi Sad, Serbia)

  • TAICHI HARUNA

    (Department of Information and Sciences, School of Arts and Sciences, Tokyo Woman’s Christian University, 2-6-1 Zempukuji, Suginami-ku, Tokyo 167-8585, Japan)

Abstract

Evolution of influenza viruses is a highly complex process that is still poorly understood. Multiyear persistence of similar variants and accumulating evidences of existence of multigenic traits indicates that influenza viruses operate as integrated units and not only as sets of distinct genes. However, there is still no consensus on whether it is the case, and to what extent. One of the main problems is the lack of framework for analyzing and interpreting large body of available high dimensional genomic, clinical and epidemiological data. By reducing dimensionality of data we intend to show whether in addition to gene-centric selective pressure, the evolution of influenza RNA segments is also shaped by their mutual interactions. Therefore, we will analyze how different complexity/entropy measures (Shannon entropy, topological entropy and Lempel–Ziv complexity) can be used to study evolution of nucleotide segments of different influenza subtypes, while reducing data dimensionality. We show that, at the nucleotide level, multiyear clusters of genome-wide entropy/complexity correlations emerged during the H1N1 pandemic in 2009. Our data are the first empirical results that indirectly support the suggestion that a component of influenza evolutionary dynamics involves correlation between RNA segments. Of all used complexity/entropy measures, Shannon entropy shows the best correlation with epidemiological data.

Suggested Citation

  • Igor Balaz & Taichi Haruna, 2018. "Evolution Of Influenza A Nucleotide Segments Through The Lens Of Different Complexity Measures," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 21(05), pages 1-24, August.
  • Handle: RePEc:wsi:acsxxx:v:21:y:2018:i:05:n:s0219525918500091
    DOI: 10.1142/S0219525918500091
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0219525918500091
    Download Restriction: Access to full text is restricted to subscribers

    File URL: https://libkey.io/10.1142/S0219525918500091?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Andrew Rambaut & Oliver G. Pybus & Martha I. Nelson & Cecile Viboud & Jeffery K. Taubenberger & Edward C. Holmes, 2008. "The genomic and epidemiological dynamics of human influenza A virus," Nature, Nature, vol. 453(7195), pages 615-619, May.
    2. Doshi, P., 2008. "Trends in recorded influenza mortality: United States, 1900-2004," American Journal of Public Health, American Public Health Association, vol. 98(5), pages 939-945.
    3. Gavin J. D. Smith & Dhanasekaran Vijaykrishna & Justin Bahl & Samantha J. Lycett & Michael Worobey & Oliver G. Pybus & Siu Kit Ma & Chung Lam Cheung & Jayna Raghwani & Samir Bhatt & J. S. Malik Peiris, 2009. "Origins and evolutionary genomics of the 2009 swine-origin H1N1 influenza A epidemic," Nature, Nature, vol. 459(7250), pages 1122-1125, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kin Keung Lai & Ming Wang & Jiangze Du, 2019. "Modeling and Predicting Infectious Diseases Cases with Climatic Factors in Hong Kong," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 23(1), pages 17147-17150, November.
    2. Correia, Sergio & Luck, Stephan & Verner, Emil, 2022. "Pandemics Depress the Economy, Public Health Interventions Do Not: Evidence from the 1918 Flu," The Journal of Economic History, Cambridge University Press, vol. 82(4), pages 917-957, December.
    3. Joseph A. Lewnard & Vennis Hong & Jeniffer S. Kim & Sally F. Shaw & Bruno Lewin & Harpreet Takhar & Marc Lipsitch & Sara Y. Tartof, 2023. "Increased vaccine sensitivity of an emerging SARS-CoV-2 variant," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    4. Mario Arturo Ruiz Estrada & Evangelos Koutronas & Donghyun Park & Alam Khan & Muhammad Tahir, 2023. "The impact of COVID-19 on the economic performance of Wuhan, China (2019–2021)," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(1), pages 847-862, February.
    5. Charles Stoecker & Nicholas J. Sanders & Alan Barreca, 2016. "Success Is Something to Sneeze At: Influenza Mortality in Cities that Participate in the Super Bowl," American Journal of Health Economics, MIT Press, vol. 2(1), pages 125-143, January.
    6. Kucharski, Adam J. & Gog, Julia R., 2012. "Age profile of immunity to influenza: Effect of original antigenic sin," Theoretical Population Biology, Elsevier, vol. 81(2), pages 102-112.
    7. Good, Benjamin H. & Desai, Michael M., 2013. "Fluctuations in fitness distributions and the effects of weak linked selection on sequence evolution," Theoretical Population Biology, Elsevier, vol. 85(C), pages 86-102.
    8. Guo, Zun-Guang & Sun, Gui-Quan & Wang, Zhen & Jin, Zhen & Li, Li & Li, Can, 2020. "Spatial dynamics of an epidemic model with nonlocal infection," Applied Mathematics and Computation, Elsevier, vol. 377(C).
    9. Simin Zou & Xuhui He, 2021. "Effect of Train-Induced Wind on the Transmission of COVID-19: A New Insight into Potential Infectious Risks," IJERPH, MDPI, vol. 18(15), pages 1-17, August.
    10. Serena Ng, 2021. "Modeling Macroeconomic Variations After COVID-19," Papers 2103.02732, arXiv.org, revised Jul 2021.
    11. Xi-Ling Wang & Lin Yang & King-Pan Chan & Susan S Chiu & Kwok-Hung Chan & J S Malik Peiris & Chit-Ming Wong, 2012. "Model Selection in Time Series Studies of Influenza-Associated Mortality," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-7, June.
    12. Katherine A. Amato & Luis A. Haddock & Katarina M. Braun & Victoria Meliopoulos & Brandi Livingston & Rebekah Honce & Grace A. Schaack & Emma Boehm & Christina A. Higgins & Gabrielle L. Barry & Katia , 2022. "Influenza A virus undergoes compartmentalized replication in vivo dominated by stochastic bottlenecks," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    13. Charles Stoecker & Nicholas J. Sanders & Alan Barreca, 2015. "Success is Something to Sneeze at: Influenza Mortality in Regions that Send Teams to the Super Bowl," Working Papers 1501, Tulane University, Department of Economics.
    14. Vijaykrishna Dhanasekaran & Sheena Sullivan & Kimberly M. Edwards & Ruopeng Xie & Arseniy Khvorov & Sophie A. Valkenburg & Benjamin J. Cowling & Ian G. Barr, 2022. "Human seasonal influenza under COVID-19 and the potential consequences of influenza lineage elimination," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    15. Rodney P. Jones & Andriy Ponomarenko, 2022. "Trends in Excess Winter Mortality (EWM) from 1900/01 to 2019/20—Evidence for a Complex System of Multiple Long-Term Trends," IJERPH, MDPI, vol. 19(6), pages 1-24, March.
    16. David A Rasmussen & Oliver Ratmann & Katia Koelle, 2011. "Inference for Nonlinear Epidemiological Models Using Genealogies and Time Series," PLOS Computational Biology, Public Library of Science, vol. 7(8), pages 1-11, August.
    17. Cecilia Solér, 2012. "Conceptualizing Sustainably Produced Food for Promotional Purposes: A Sustainable Marketing Approach," Sustainability, MDPI, vol. 4(3), pages 1-47, March.
    18. Maud Thomas & Holger Rootzén, 2022. "Real‐time prediction of severe influenza epidemics using extreme value statistics," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(2), pages 376-394, March.
    19. Rodney P Jones, 2021. "Excess Winter Mortality (EWM) as a Dynamic Forensic Tool: Where, When, Which Conditions, Gender, Ethnicity and Age," IJERPH, MDPI, vol. 18(4), pages 1-22, February.
    20. Goulas, Sofoklis & Megalokonomou, Rigissa, 2016. "Swine Flu and The Effect of Compulsory Class Attendance on Academic Performance," MPRA Paper 75395, University Library of Munich, Germany.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wsi:acsxxx:v:21:y:2018:i:05:n:s0219525918500091. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/acs/acs.shtml .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.