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Fitting Spatial Joint Model for U.S Regional Influenza-like Illness (ILINet) Data Set

Received: 25 October 2021     Accepted: 13 November 2021     Published: 29 December 2021
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Abstract

Background: Influenza is commonly known as the flu, which is a viral infectious disease that attacks our respiratory systems, such as the nose, throat, and lungs. Several studies have been performed on influenza determinants, concentrating on the role of biological and behavioral risk factors at the personal level to reduce the burden of the disease. However, few studies conducted to identify geographical patterns of infectious disease and its associated factors. Objective: This study aimed to provide a step-by-step process of finding the geographic patterns of influenza cases and the role that they can be determined by the racial factor. Method: In this study, first non-spatial and spatial models were estimated, and then a step-by-step procedure was used to fit a spatial joint model to the US Influenza Like Illness (ILINet) dataset using a single predictor: percentage of African American people in each state. Results: Findings revealed that for both non-spatial and spatial models, the racial variable was positively associated with standard morbidity ratio (SMR) and was highly statistically significant (p<0.0001). In addition, it showed that there was a large residual spatial dependency for the spatial joint model, which meant for our dataset, the spatial component explained much of the variability. Conclusion: Researchers that desire to create a joint special model from the ground up in the instance of infectious illness modelling can benefit from this research.

Published in Pure and Applied Mathematics Journal (Volume 10, Issue 6)
DOI 10.11648/j.pamj.20211006.12
Page(s) 127-138
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Spatial Analysis, Influenza-Like Illness, Heterogeneity, Standardized Morbidity Rates (SMRs)

References
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[3] Molinari M, A et al., (2007). The annual impact of seasonal influenza in the US: measuring disease burden and cost. Vaccine, 25 (27): 5086-96.
[4] World Health Organization (WHO) Global Influenza Strategy, [Internet]. Access on 07.07.202 and available from https://www.who.int/influenza/global_influenza_strategy_2019_2030/en.
[5] Overview of influenza Surveillance in the United States | Seasonal Influenza (Flu) | CDC [Internet]. [Cited 2016 May 31] Available from: http://www.cdc.gov/flu/weekly/overview.html.
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[10] Gracies R. G., Ellis J. H., Kress A., and Glass G. E. (2004) Modeling the spread of annual influenza epidemics in the U.S.: The Potential Role of Air Travel. Health Care Management Science. 7, 127-134. doi: 10.1023/b:hcms.0000020652.38181.da.
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[12] Shaman J, Pitzer VE, Viboud C, Grenfell BT, Lipsitch M (2010) Absolute Humidity and the Seasonal Onset of Influenza in the Continental United States. PLoS Biol 8 (2): e1000316, doi: 10.1371/journal.pbio.1000316.
[13] Deyle R. E., Maher C. M., Hernandez D. R., Sanjay B., Sugihara G., (2016) Global environmental drivers of influenza. Proc Natl Acad Sci, 13 (46): 13081-13086, doi: 10.1073/pnas.1607747113.
[14] Mossong J., et al., (2008) Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Disease, PLoS Medicine, 5 (3): 381-390, doi: 10.137/journal.pmed.0050079.
[15] Marion Z. H., Ferdyce A. J., and Fitzpatrick M. B., (2018) A hierarchical Bayesian model to incorporate uncertainty into methods for diversity partitioning, Ecology, 99 (4): 947-950.
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Cite This Article
  • APA Style

    Azizur Rahman, Arifa Tabassum, Mariam Akter. (2021). Fitting Spatial Joint Model for U.S Regional Influenza-like Illness (ILINet) Data Set. Pure and Applied Mathematics Journal, 10(6), 127-138. https://doi.org/10.11648/j.pamj.20211006.12

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    ACS Style

    Azizur Rahman; Arifa Tabassum; Mariam Akter. Fitting Spatial Joint Model for U.S Regional Influenza-like Illness (ILINet) Data Set. Pure Appl. Math. J. 2021, 10(6), 127-138. doi: 10.11648/j.pamj.20211006.12

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    AMA Style

    Azizur Rahman, Arifa Tabassum, Mariam Akter. Fitting Spatial Joint Model for U.S Regional Influenza-like Illness (ILINet) Data Set. Pure Appl Math J. 2021;10(6):127-138. doi: 10.11648/j.pamj.20211006.12

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  • @article{10.11648/j.pamj.20211006.12,
      author = {Azizur Rahman and Arifa Tabassum and Mariam Akter},
      title = {Fitting Spatial Joint Model for U.S Regional Influenza-like Illness (ILINet) Data Set},
      journal = {Pure and Applied Mathematics Journal},
      volume = {10},
      number = {6},
      pages = {127-138},
      doi = {10.11648/j.pamj.20211006.12},
      url = {https://doi.org/10.11648/j.pamj.20211006.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.pamj.20211006.12},
      abstract = {Background: Influenza is commonly known as the flu, which is a viral infectious disease that attacks our respiratory systems, such as the nose, throat, and lungs. Several studies have been performed on influenza determinants, concentrating on the role of biological and behavioral risk factors at the personal level to reduce the burden of the disease. However, few studies conducted to identify geographical patterns of infectious disease and its associated factors. Objective: This study aimed to provide a step-by-step process of finding the geographic patterns of influenza cases and the role that they can be determined by the racial factor. Method: In this study, first non-spatial and spatial models were estimated, and then a step-by-step procedure was used to fit a spatial joint model to the US Influenza Like Illness (ILINet) dataset using a single predictor: percentage of African American people in each state. Results: Findings revealed that for both non-spatial and spatial models, the racial variable was positively associated with standard morbidity ratio (SMR) and was highly statistically significant (p<0.0001). In addition, it showed that there was a large residual spatial dependency for the spatial joint model, which meant for our dataset, the spatial component explained much of the variability. Conclusion: Researchers that desire to create a joint special model from the ground up in the instance of infectious illness modelling can benefit from this research.},
     year = {2021}
    }
    

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  • TY  - JOUR
    T1  - Fitting Spatial Joint Model for U.S Regional Influenza-like Illness (ILINet) Data Set
    AU  - Azizur Rahman
    AU  - Arifa Tabassum
    AU  - Mariam Akter
    Y1  - 2021/12/29
    PY  - 2021
    N1  - https://doi.org/10.11648/j.pamj.20211006.12
    DO  - 10.11648/j.pamj.20211006.12
    T2  - Pure and Applied Mathematics Journal
    JF  - Pure and Applied Mathematics Journal
    JO  - Pure and Applied Mathematics Journal
    SP  - 127
    EP  - 138
    PB  - Science Publishing Group
    SN  - 2326-9812
    UR  - https://doi.org/10.11648/j.pamj.20211006.12
    AB  - Background: Influenza is commonly known as the flu, which is a viral infectious disease that attacks our respiratory systems, such as the nose, throat, and lungs. Several studies have been performed on influenza determinants, concentrating on the role of biological and behavioral risk factors at the personal level to reduce the burden of the disease. However, few studies conducted to identify geographical patterns of infectious disease and its associated factors. Objective: This study aimed to provide a step-by-step process of finding the geographic patterns of influenza cases and the role that they can be determined by the racial factor. Method: In this study, first non-spatial and spatial models were estimated, and then a step-by-step procedure was used to fit a spatial joint model to the US Influenza Like Illness (ILINet) dataset using a single predictor: percentage of African American people in each state. Results: Findings revealed that for both non-spatial and spatial models, the racial variable was positively associated with standard morbidity ratio (SMR) and was highly statistically significant (p<0.0001). In addition, it showed that there was a large residual spatial dependency for the spatial joint model, which meant for our dataset, the spatial component explained much of the variability. Conclusion: Researchers that desire to create a joint special model from the ground up in the instance of infectious illness modelling can benefit from this research.
    VL  - 10
    IS  - 6
    ER  - 

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Author Information
  • Department of Statistics, Jahangirnagar University, Savar, Dhaka, Bangladesh

  • Department of Statistics, Jahangirnagar University, Savar, Dhaka, Bangladesh

  • Department of Statistics, Jahangirnagar University, Savar, Dhaka, Bangladesh

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