Coronavirus disease 2019 (COVID-19) has spread in more than 100 countries and 
regions around the world, raising grave global concerns. COVID-19 has a similar 
pattern of infection, clinical symptoms, and chest imaging findings to influenza 
pneumonia. In this retrospective study, we analysed clinical and chest CT data 
of 24 patients with COVID-19 and 79 patients with influenza pneumonia. 
Univariate analysis demonstrated that the temperature, systolic pressure, cough 
and sputum production could distinguish COVID-19 from influenza pneumonia. The 
diagnostic sensitivity and specificity for the clinical features are 0.783 and 
0.747, and the AUC value is 0.819. Univariate analysis demonstrates that nine CT 
features, central-peripheral distribution, superior-inferior distribution, 
anterior-posterior distribution, patches of GGO, GGO nodule, vascular 
enlargement in GGO, air bronchogram, bronchiectasis within focus, interlobular 
septal thickening, could distinguish COVID-19 from influenza pneumonia. The 
diagnostic sensitivity and specificity for the CT features are 0.750 and 0.962, 
and the AUC value is 0.927. Finally, a multivariate logistic regression model 
combined the variables from the clinical variables and CT features models was 
made. The combined model contained six features: systolic blood pressure, sputum 
production, vascular enlargement in the GGO, GGO nodule, central-peripheral 
distribution and bronchiectasis within focus. The diagnostic sensitivity and 
specificity for the combined features are 0.87 and 0.96, and the AUC value is 
0.961. In conclusion, some CT features or clinical variables can differentiate 
COVID-19 from influenza pneumonia. Moreover, CT features combined with clinical 
variables had higher diagnostic performance.