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Currently, the prevention and control of COVID-19 outside Hubei province in China, and other countries has become more and more critically serious. We developed and validated a diagnosis aid model without CT images for early identification of suspected COVID-19 pneumonia (S-COVID-19-P) on admission in adult fever patients and made the validated model available via an online triage calculator. Patients admitted from Jan 14 to Feb 26, 2020 with the epidemiological history of exposure to COVID-19 were included [Model development (n = 132) and validation (n = 32)]. Candidate features included clinical symptoms, routine laboratory tests and other clinical information on admission. Features selection and model development were based on Lasso regression. The primary outcome is the development and validation of a diagnosis aid model for S-COVID-19-P early identification on admission. The development cohort contains 26 S-COVID-19-P and 7 confirmed COVID-19 pneumonia cases. The model performance in held-out testing set and validation cohort resulted in AUCs of 0.841 and 0.938, F-1 score of 0.571 and 0.667, recall of 1.000 and 1.000, specificity of 0.727 and 0.778, and the precision of 0.400 and 0.500. Based on this model, an optimized strategy for S-COVID-19-P early identification in fever clinics has also been designed. S-COVID-19-P could be identified early by a machine-learning model only used collected clinical information without CT images on admission in fever clinics with 100% recall score. The well performed and validated model has been deployed as an online triage tool, which is available at: https://intensivecare.shinyapps.io/COVID19/.