COVID-19 has spread globally with over 90,000,000 incidences and 1,930,000 deaths by Jan 11, 2021, which poses a big threat to public health. It is urgent to distinguish
COVID-19 from common
pneumonia. In this study, we reported multiple clinical feature analyses on
COVID-19 in Inner Mongolia for the first time. We dynamically monitored multiple clinical features of all 75 confirmed
COVID-19 patients, 219
pneumonia patients, and 68 matched healthy people in Inner Mongolia. Then, we studied the association between
COVID-19 and clinical characteristics, based on which to construct a novel logistic regression model for predicting
COVID-19. As a result, among the tested clinical characteristics, WBC,
hemoglobin, C-reactive
protein (CRP), ALT, and Cr were significantly different between
COVID-19 patients and patients in other groups. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was 0.869 for the logistic regression model using multiple factors associated with
COVID-19. Furthermore, the CRP reaction showed five different time-series patterns with one-peak and double-peak modes. In conclusion, our study identified a few clinical characteristics significantly different between
COVID-19 patients and others in Inner Mongolia. The features can be used to establish a reliable logistic regression model for predicting
COVID-19.