Risk Identification and Prediction for COVID-19 Mortality: Risk Identification and Prediction for COVID-19 Mortality - Nguyen, Hanh; Shao, Qin (Translation: The University of Toledo Journal of Medical Sciences, 2021)
This paper studies several key metrics for COVID-19 using a public surveillance system data set. It compares the difference between two case fatality rates: the naive case fatality rate, which has been frequently mentioned in media outlets, and one which is the sample estimate for the mortality rate. A logistic regression model is applied to modeling the daily mortality rate. The conclusion is that time, gender, age and some of their interactions, appear to have a significant impact on the mortality rate; the daily mortality rate has been decreasing since the outbreak; males older than 60 has been the most vulnerable group. The receiver operating characteristics curve and the curve under the area show that the proposed logistic model is capable of predicting the outcome of a reported case with accuracy as high as 89%. These findings are helpful in assessing the magnitude of the risk posed by the COVID-19 virus to certain groups, predicting outcome severity, and optimally allocating medical resources such as intensive care units and ventilators.