COVID-19 Risk Factor Identification based on Ohio Data


  • Dr. Shao University of Toledo
  • Gerard Thompson
  • Amy Thompson School of Population Health, The University of Toledo


COVID-19, Logistic Regression, Odds Ratio, Mortality


In January COVID-19 was declared to be a global emergency and everyday life was disrupted. Many questions about COVID-19 remain to be answered. This paper provides an examination of the Ohio COVID-19 data set. In
particular, logistic regression is applied to the analysis of age and gender characteristics on the mortality of a patient. Based on the statistics and the p-values, gender and age play an important role in the outcome of a patient and the most vulnerable group is comprised of male patients who are more than eighty years old. This paper is an attempt to help in the formulation of public health policy towards confronting COVID-19 and paves
the way towards a more comprehensive quantitative analysis as more data become available.

Author Biographies

Dr. Shao, University of Toledo

Dr. Shao is a statistician in the Department of Mathematics and Statistics. She became full professor in 2013.

Gerard Thompson

Dr. Thompson is a mathematician in the Department of Mathematics and Statistics. He became a full professor in 2001.

Amy Thompson, School of Population Health, The University of Toledo

Dr. Thompson is full professor at School of Population Health. She is Vice Provost for Faculty Affairs at The University of Toledo.



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How to Cite

Shao, Q., Thompson, G., & Thompson, A. (2020). COVID-19 Risk Factor Identification based on Ohio Data. Translation: The University of Toledo Journal of Medical Sciences, 8. Retrieved from