This manuscript systematically reviewed and critically appraised currently available prediction models for COVID-19, in particular diagnostic and prognostic models for the disease. Findings indicate that proposed models are poorly reported, at high risk of bias, and their reported performance is probably optimistic.

2696 titles were screened, and 27 studies describing 31 prediction models were included.

The search identified:

• 3 models for predicting hospital admission from pneumonia and other events (as proxy outcomes for covid-19 pneumonia) in the general population
• 18 diagnostic models for detecting COVID-19 infection (13 were machine learning based on computed tomography scans)
• 10 prognostic models for predicting mortality risk, progression to severe disease, or length of hospital stay.
• 1 study using patient data from outside of China

Within these studies:

• The most reported predictors of presence of COVID-19 in patients with suspected disease included age, body temperature, and signs and symptoms
• The most reported predictors of severe prognosis in patients with COVID-19 included age, sex, features derived from computed tomography scans, C reactive protein, lactic dehydrogenase, and lymphocyte count.
• C index estimates ranged from 0.73 to 0.81 in prediction models for the general population (reported for all three models), from 0.81 to more than 0.99 in diagnostic models (reported for 13 of the 18 models), and from 0.85 to 0.98 in prognostic models (reported for six of the 10 models)

All studies were rated at high risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, and high risk of model overfitting. Reporting quality varied substantially between studies. Most reports did not include a description of the study population or intended use of the models, and calibration of predictions was rarely assessed.

Prediction models for COVID-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. Proposed models are poorly reported, at high risk of bias, and their reported performance is probably optimistic. Immediate sharing of well documented individual participant data from COVID-19 studies is needed for collaborative efforts to develop more rigorous prediction models and validate existing ones.