An Analysis of Corona Virus Disease (COVID-19) Predictors: Logistic Regression Model Approach

Usman Aliyu; Abubakar Umar Bashar; Umar Usman

doi:doi:10.11648/j.ijsd.20210704.13

| Peer-Reviewed

An Analysis of Corona Virus Disease (COVID-19) Predictors: Logistic Regression Model Approach

Usman Aliyu, Abubakar Umar Bashar, Umar Usman

Published in International Journal of Statistical Distributions and Applications (Volume 7, Issue 4)

Received: 15 August 2021 Accepted: 17 September 2021 Published: 19 November 2021

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

Although Corona Virus disease (COVID-19) is a contagious disease cause by severe acute respiratory syndrome which affects mostly people whose immune system are weak or not resistance to the disease, there exists no vaccine that is 100% effective for its cure though efforts are being intensify by researchers in discovering the vaccine as well as model for prediction of Corona Virus Disease. In this era of advanced information and communication technology, as well as evidence-based medicine, statistical modeling has become as necessary the medical practitioners who are interested in lasting solution to diagnosed problems. In this work a logistic regressions model has been proposed to serve the purpose. The data was obtained from Nigeria Centre for Disease Control (NCDC) and was analyzed using binary logistic regression model in which Corona Virus disease was considered as categorical dependant variable (COVID-19 status: chance of being positive or negative) and the predictors considered are; Age, any of either Headache or Vomiting, Fever, Sore throat/runny nose, Any of Cold, cough or sweating, Loss of Smell or taste, and Breathing Difficulties. The results shows the significant predictors for predicting Corona Virus Diseases are; Loss of Smell or taste, Breathing Difficulties, Fever, Sore throat or runny nose, Age, any of either Headache or Vomiting, and Any of Cold, cough or sweating. The logit model obtained was: Logit (P(y=1)) = -3.748 + 0.356 Age +2.938 any of either Headache or Vomiting + 0.752 Fever + 2.792 Sore throat or runny nose – 0.028 Any of Cold, cough or sweating + 1.872 Loss of Smell or taste + 0.844 Breathing Difficulties. So also from the same results, it was found among predictors that; Sex/Gender, Temperature >37.5 degree and Fatigue or Muscle Pain were not good predictors of Corona Virus disease.

Published in	International Journal of Statistical Distributions and Applications (Volume 7, Issue 4)
DOI	10.11648/j.ijsd.20210704.13
Page(s)	95-101
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

Logit Function, COVID-19, Logistic Regression, Maximum Likelihood Estimation

References

[1]	Afifi, W. A. Dillow, M. R. and Morse, C. (2004). Examining Predictors and Consequences of Information seeking in Close Relationships, Personal relationships, 11, 429-449.
[2]	Agresti, A. (1996). An Introduction to Categorical Data Analysis. John Wiley and Sons, Inc. http://lib.stat.cmu.edu/datasets/agresti
[3]	Agresti, A. (2007), An Introduction to Categorical Data Analysis, Second Edition, Wiley, Inc., New York.
[4]	Agresti, Alan. (2002). Categorical Data Analysis. New York: Wiley-Interscience. ISBN 0-471-36093-7.
[5]	Amemiya, T. (1985). Advanced Econometrics. Harvard University Press. ISBN 0-674-00560-0.
[6]	Balakrishnan, N. (1991). Handbook of the Logistic Distribution. Marcel Dekker, Inc. ISBN 978-0-8247-8587-1.
[7]	Berkson, J. (1944). Application of Logistic function to bio-assay. Journal of the American Statistical Association 39, 357-365.
[8]	Bliss, C. J. (1935). The calculation of the dosage mortality curve. Annals of Applied Biology 22, 134-167.
[9]	Christensen, R. (1997), Log-linear Models and Logistic Regression, Second edition. Springer-Verlag, New York.
[10]	Collett, D. R. (2003). Modeling Binary data, Chapman & Hall, London.
[11]	Cox, D. R. and Snell, E. J (1994). Analysis of binary data. Chapman & Hall, London.
[12]	Cox, D. R. and Snell, E. J. (1989), Analysis of Binary Data (second edition), Chapman & Hall/CRC.
[13]	Devita, V. T., Hellman, S. L. and Rosenberg’s, S. A. (2008), Principles and Practice of Oncology, Volume 2.
[14]	Greene, William H. (2003). Econometric Analysis, fifth edition. Prentice Hall. ISBN 0-13-066189-9.
[15]	Grimms, L. G. and Yarnold, P. R. (1995), Reading and Understanding Multivariate Statistics, American Psychological Association, Washington, D. C.
[16]	Grizzle, J. E., Starmer, C. F. and Koch, G. G. (1969), Analysis of Categorical data by Linear Models, Biometrics, 25, 489-504.
[17]	Hauck, W. W, and Donner, A., (1997), Wald’s test as Applied to Hypothesis in Logit Analysis. Journal of the American Statistical Association, 72, 851 – 853.
[18]	Hilbe, Joseph M. (2009). Logistic Regression Models. Chapman & Hall/CRC Press. ISBN 978-1-4200-7575-5.
[19]	Hosmer D. W and Lemeshow S. (2000). Applied Logistic Regression. 2nd ed. New York, USA: John Wiley and Sons.
[20]	Hosmer, David W.; Lemeshow, Stanley (2000). Applied Logistic Regression (2nd ed.). Wiley. ISBN 0-471-35632-8.
[21]	Howell, David C. (2010). Statistical Methods for Psychology, 7th ed. Belmont, CA; Thomson Wadsworth. ISBN 978-0-495-59786-5.
[22]	Ingeles, C. J.; Garcia-Fernandez, J. M. Castejon, J. L.; Valle Antonio, B. D. and Marzo, J. C (2009), Reliability and Validity Evidence of Score on the Achievement Goal Tendencies: Questionnaire in a sample of Spanish students of compulsory secondary education, Psychology in the school, Vol. 46. 1048 – 1060, Wiley Periodicals, Inc; A Wiley company.
[23]	Jennings, D. E. (1986), Judging Inference Adequacy in Logistic Regression, Journal of the American statistical Association, 81, 471 – 476.
[24]	Kleinbaum, D. G. (1994), Logistic Regression, A self-learning text. Springer - Verlag, New York, 104 – 119.
[25]	McCaullagh, H. and Nelder, J. N., (1992), Generalized linear Models (2^nd Edition). Chapman and Hall, Madras.
[26]	Michael, H. K.; Christopher, J. N., John N., and William. L (2005). “Applied Linear Statistical Model”, fifth Ed.; 555-623., McGraw Hill International, New York.
[27]	Peduzzi, P.; J. Concato, E. Kemper, T. R. Holford, A. R. Feinstein (1996). "A simulation study of the number of events per variable in logistic regression analysis". Journal of Clinical Epidemiology. 49 (12): 1373–1379. PMID 8970487.

Cite This Article

Plain Text BibTeX RIS

APA Style

Usman Aliyu, Abubakar Umar Bashar, Umar Usman. (2021). An Analysis of Corona Virus Disease (COVID-19) Predictors: Logistic Regression Model Approach. International Journal of Statistical Distributions and Applications, 7(4), 95-101. https://doi.org/10.11648/j.ijsd.20210704.13

Copy | Download

ACS Style

Usman Aliyu; Abubakar Umar Bashar; Umar Usman. An Analysis of Corona Virus Disease (COVID-19) Predictors: Logistic Regression Model Approach. Int. J. Stat. Distrib. Appl. 2021, 7(4), 95-101. doi: 10.11648/j.ijsd.20210704.13

Copy | Download

AMA Style

Usman Aliyu, Abubakar Umar Bashar, Umar Usman. An Analysis of Corona Virus Disease (COVID-19) Predictors: Logistic Regression Model Approach. Int J Stat Distrib Appl. 2021;7(4):95-101. doi: 10.11648/j.ijsd.20210704.13

Copy | Download

@article{10.11648/j.ijsd.20210704.13,
  author = {Usman Aliyu and Abubakar Umar Bashar and Umar Usman},
  title = {An Analysis of Corona Virus Disease (COVID-19) Predictors: Logistic Regression Model Approach},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {7},
  number = {4},
  pages = {95-101},
  doi = {10.11648/j.ijsd.20210704.13},
  url = {https://doi.org/10.11648/j.ijsd.20210704.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20210704.13},
  abstract = {Although Corona Virus disease (COVID-19) is a contagious disease cause by severe acute respiratory syndrome which affects mostly people whose immune system are weak or not resistance to the disease, there exists no vaccine that is 100% effective for its cure though efforts are being intensify by researchers in discovering the vaccine as well as model for prediction of Corona Virus Disease. In this era of advanced information and communication technology, as well as evidence-based medicine, statistical modeling has become as necessary the medical practitioners who are interested in lasting solution to diagnosed problems. In this work a logistic regressions model has been proposed to serve the purpose. The data was obtained from Nigeria Centre for Disease Control (NCDC) and was analyzed using binary logistic regression model in which Corona Virus disease was considered as categorical dependant variable (COVID-19 status: chance of being positive or negative) and the predictors considered are; Age, any of either Headache or Vomiting, Fever, Sore throat/runny nose, Any of Cold, cough or sweating, Loss of Smell or taste, and Breathing Difficulties. The results shows the significant predictors for predicting Corona Virus Diseases are; Loss of Smell or taste, Breathing Difficulties, Fever, Sore throat or runny nose, Age, any of either Headache or Vomiting, and Any of Cold, cough or sweating. The logit model obtained was: Logit (P(y=1)) = -3.748 + 0.356 Age +2.938 any of either Headache or Vomiting + 0.752 Fever + 2.792 Sore throat or runny nose – 0.028 Any of Cold, cough or sweating + 1.872 Loss of Smell or taste + 0.844 Breathing Difficulties. So also from the same results, it was found among predictors that; Sex/Gender, Temperature >37.5 degree and Fatigue or Muscle Pain were not good predictors of Corona Virus disease.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - An Analysis of Corona Virus Disease (COVID-19) Predictors: Logistic Regression Model Approach
AU  - Usman Aliyu
AU  - Abubakar Umar Bashar
AU  - Umar Usman
Y1  - 2021/11/19
PY  - 2021
N1  - https://doi.org/10.11648/j.ijsd.20210704.13
DO  - 10.11648/j.ijsd.20210704.13
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 95
EP  - 101
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20210704.13
AB  - Although Corona Virus disease (COVID-19) is a contagious disease cause by severe acute respiratory syndrome which affects mostly people whose immune system are weak or not resistance to the disease, there exists no vaccine that is 100% effective for its cure though efforts are being intensify by researchers in discovering the vaccine as well as model for prediction of Corona Virus Disease. In this era of advanced information and communication technology, as well as evidence-based medicine, statistical modeling has become as necessary the medical practitioners who are interested in lasting solution to diagnosed problems. In this work a logistic regressions model has been proposed to serve the purpose. The data was obtained from Nigeria Centre for Disease Control (NCDC) and was analyzed using binary logistic regression model in which Corona Virus disease was considered as categorical dependant variable (COVID-19 status: chance of being positive or negative) and the predictors considered are; Age, any of either Headache or Vomiting, Fever, Sore throat/runny nose, Any of Cold, cough or sweating, Loss of Smell or taste, and Breathing Difficulties. The results shows the significant predictors for predicting Corona Virus Diseases are; Loss of Smell or taste, Breathing Difficulties, Fever, Sore throat or runny nose, Age, any of either Headache or Vomiting, and Any of Cold, cough or sweating. The logit model obtained was: Logit (P(y=1)) = -3.748 + 0.356 Age +2.938 any of either Headache or Vomiting + 0.752 Fever + 2.792 Sore throat or runny nose – 0.028 Any of Cold, cough or sweating + 1.872 Loss of Smell or taste + 0.844 Breathing Difficulties. So also from the same results, it was found among predictors that; Sex/Gender, Temperature >37.5 degree and Fatigue or Muscle Pain were not good predictors of Corona Virus disease.
VL  - 7
IS  - 4
ER  -

Copy | Download

Author Information

Usman Aliyu

Department of Statistics, Waziri Umar Federal Polytechnic, Birnin Kebbi, Nigeria
Abubakar Umar Bashar

Department of Statistics, Waziri Umar Federal Polytechnic, Birnin Kebbi, Nigeria
Umar Usman

Department of Statistics, Usmanu Danfodiyo University Sokoto, Nigeria

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Usman Aliyu, Abubakar Umar Bashar, Umar Usman. (2021). An Analysis of Corona Virus Disease (COVID-19) Predictors: Logistic Regression Model Approach. International Journal of Statistical Distributions and Applications, 7(4), 95-101. https://doi.org/10.11648/j.ijsd.20210704.13

Copy | Download

ACS Style

Usman Aliyu; Abubakar Umar Bashar; Umar Usman. An Analysis of Corona Virus Disease (COVID-19) Predictors: Logistic Regression Model Approach. Int. J. Stat. Distrib. Appl. 2021, 7(4), 95-101. doi: 10.11648/j.ijsd.20210704.13

Copy | Download

AMA Style

Usman Aliyu, Abubakar Umar Bashar, Umar Usman. An Analysis of Corona Virus Disease (COVID-19) Predictors: Logistic Regression Model Approach. Int J Stat Distrib Appl. 2021;7(4):95-101. doi: 10.11648/j.ijsd.20210704.13

Copy | Download

@article{10.11648/j.ijsd.20210704.13,
  author = {Usman Aliyu and Abubakar Umar Bashar and Umar Usman},
  title = {An Analysis of Corona Virus Disease (COVID-19) Predictors: Logistic Regression Model Approach},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {7},
  number = {4},
  pages = {95-101},
  doi = {10.11648/j.ijsd.20210704.13},
  url = {https://doi.org/10.11648/j.ijsd.20210704.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20210704.13},
  abstract = {Although Corona Virus disease (COVID-19) is a contagious disease cause by severe acute respiratory syndrome which affects mostly people whose immune system are weak or not resistance to the disease, there exists no vaccine that is 100% effective for its cure though efforts are being intensify by researchers in discovering the vaccine as well as model for prediction of Corona Virus Disease. In this era of advanced information and communication technology, as well as evidence-based medicine, statistical modeling has become as necessary the medical practitioners who are interested in lasting solution to diagnosed problems. In this work a logistic regressions model has been proposed to serve the purpose. The data was obtained from Nigeria Centre for Disease Control (NCDC) and was analyzed using binary logistic regression model in which Corona Virus disease was considered as categorical dependant variable (COVID-19 status: chance of being positive or negative) and the predictors considered are; Age, any of either Headache or Vomiting, Fever, Sore throat/runny nose, Any of Cold, cough or sweating, Loss of Smell or taste, and Breathing Difficulties. The results shows the significant predictors for predicting Corona Virus Diseases are; Loss of Smell or taste, Breathing Difficulties, Fever, Sore throat or runny nose, Age, any of either Headache or Vomiting, and Any of Cold, cough or sweating. The logit model obtained was: Logit (P(y=1)) = -3.748 + 0.356 Age +2.938 any of either Headache or Vomiting + 0.752 Fever + 2.792 Sore throat or runny nose – 0.028 Any of Cold, cough or sweating + 1.872 Loss of Smell or taste + 0.844 Breathing Difficulties. So also from the same results, it was found among predictors that; Sex/Gender, Temperature >37.5 degree and Fatigue or Muscle Pain were not good predictors of Corona Virus disease.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - An Analysis of Corona Virus Disease (COVID-19) Predictors: Logistic Regression Model Approach
AU  - Usman Aliyu
AU  - Abubakar Umar Bashar
AU  - Umar Usman
Y1  - 2021/11/19
PY  - 2021
N1  - https://doi.org/10.11648/j.ijsd.20210704.13
DO  - 10.11648/j.ijsd.20210704.13
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 95
EP  - 101
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20210704.13
AB  - Although Corona Virus disease (COVID-19) is a contagious disease cause by severe acute respiratory syndrome which affects mostly people whose immune system are weak or not resistance to the disease, there exists no vaccine that is 100% effective for its cure though efforts are being intensify by researchers in discovering the vaccine as well as model for prediction of Corona Virus Disease. In this era of advanced information and communication technology, as well as evidence-based medicine, statistical modeling has become as necessary the medical practitioners who are interested in lasting solution to diagnosed problems. In this work a logistic regressions model has been proposed to serve the purpose. The data was obtained from Nigeria Centre for Disease Control (NCDC) and was analyzed using binary logistic regression model in which Corona Virus disease was considered as categorical dependant variable (COVID-19 status: chance of being positive or negative) and the predictors considered are; Age, any of either Headache or Vomiting, Fever, Sore throat/runny nose, Any of Cold, cough or sweating, Loss of Smell or taste, and Breathing Difficulties. The results shows the significant predictors for predicting Corona Virus Diseases are; Loss of Smell or taste, Breathing Difficulties, Fever, Sore throat or runny nose, Age, any of either Headache or Vomiting, and Any of Cold, cough or sweating. The logit model obtained was: Logit (P(y=1)) = -3.748 + 0.356 Age +2.938 any of either Headache or Vomiting + 0.752 Fever + 2.792 Sore throat or runny nose – 0.028 Any of Cold, cough or sweating + 1.872 Loss of Smell or taste + 0.844 Breathing Difficulties. So also from the same results, it was found among predictors that; Sex/Gender, Temperature >37.5 degree and Fatigue or Muscle Pain were not good predictors of Corona Virus disease.
VL  - 7
IS  - 4
ER  -

Copy | Download