Researchers trained five classification algorithms to create an accurate model to predict COVID-19 test results. Results identify the key symptom features associated with COVID-19 infection and provide a way for rapid screening and cost effective infection detection. Findings reveal that number of days experiencing symptoms such as fever and difficulty breathing play a large role in COVID-19 test results. Findings also show that molecular tests have much narrower post-symptom onset days compared to post-symptom onset days of serology tests. As a result, the molecular test has the lowest positive rate because it measures current infection.