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

Azizur Rahman; Arifa Tabassum; Mariam Akter

doi:doi:10.11648/j.pamj.20211006.12

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

Azizur Rahman, Arifa Tabassum, Mariam Akter

Published in Pure and Applied Mathematics Journal (Volume 10, Issue 6)

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

[1]	“Influenza (flu) - Symptoms and causes - Mayo Clinic.” https://www.mayoclinic.org/diseases-conditions/flu/symptoms-causes/syc-20351719 (Access on 24.07.2020).
[2]	WHO Influenza (Seasonal) [Internet]. Fact Sheet Number 211, 2015 [cited 2016 May 10]. Available from: http://www.who.int/mediacentre/factsheets/fs211/en/index.html.
[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.
[6]	Lefgren E., Fefferman N H, Naumor Y N., Groski J., Noumova E N. (2007) Influenza seasonality: underlying causes and modeling theories. J Virology, 81 (11): 5429-36, doi: 10.1128/Jul.01680-06.
[7]	Teserius D. J., Shaman J., Alonso J. W., Feshbach K. B., Uejio C. K., Conries A., and Viboud C., (2013) Environmental Prediction of Seasonal Influenza Epidemics across Temperature and Tropical climates. PLOS Pathogens, https://doi.org/10.1371/journal.ppat.1003194
[8]	Wenger B. J., and Naumova N. E. (2010) Seasonal synchronization of influenza in the United States older adult people. PLOS One, 5 (4): e10187, doi: 10.1371/journal.pom.0010187.
[9]	Schanzer D, Vachon J, Pelletier L. (2011) Age-specific differences in influenza A epidemic curves: do children 690 drive the spread of influenza epidemics? Am J Epidemiol. 174 (1): 109–17. doi: 10.1093/aje/kwr037.
[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.
[11]	Brownstein S. J., Wolfe C. J. and Mandl D. K., (2006) Empirical evidence for the effect of Airline travel on Inter-Regional influenza spread in the United States. PLoS Medicine, 3 (10): 125-135, doi: 10.1371/journal.pmed.0030401.
[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.
[16]	Sloan C., Chandrashekhar R., Mitchel E., Schaffer W., and Lindergren M. L., (2015) Socioeconomic Disparities and influenza Hospitalizations, Tennessee, USA. Emer Infect Dis, 21 (9): 1602-1610, doi: 10.3201/eid2109.141881.
[17]	Crighton, E. J., Elliott, S. J., Moineddin, R., Kanaroglou, P., and Upshur, R. (2007) A spatial analysis of the determinants of pneumonia and influenza hospitalizations in Ontario (1992-2001). Social science & medicine (1982), 64 (8): 1636–1650, doi: 10.1016/j.socscimed.2006.12.001.

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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

Azizur Rahman

Department of Statistics, Jahangirnagar University, Savar, Dhaka, Bangladesh
Arifa Tabassum

Department of Statistics, Jahangirnagar University, Savar, Dhaka, Bangladesh
Mariam Akter

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

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Plain Text BibTeX RIS

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}
}

Copy | Download

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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