Who should we test for COVID-19? A triage model built from national symptom surveys

Abstract The gold standard for COVID-19 diagnosis is detection of viral RNA in a reverse transcription PCR test. Due to global limitations in testing capacity, effective prioritization of individuals for testing is essential. Here, we devised a model that estimates the probability of an individual to test positive for COVID-19 based on answers to 9 simple questions regarding age, gender, presence of prior medical conditions, general feeling, and the symptoms fever, cough, shortness of breath, sore throat and loss of taste or smell, all of which have been associated with COVID-19 infection. Our model was devised from a subsample of a national symptom survey that was answered over 2 million times in Israel over the past 2 months and a targeted survey distributed to all residents of several cities in Israel. Overall, 43,752 adults were included, from which 498 self-reported as being COVID-19 positive. We successfully validated the model on held-out individuals from Israel where it achieved a positive predictive value (PPV) of 46.3% at a 10% sensitivity and demonstrated its applicability outside of Israel by further validating it on an independently collected symptom survey dataset from the U.K., U.S. and Sweden, where it achieved a PPV of 34.7% at 10% sensitivity. Moreover, evaluating the model’s performance on this latter independent dataset on entries collected one week prior to the PCR test and up to the day of the test we found the highest performance on the day of the test. As our tool can be used online and without the need of exposure to suspected patients, it may have worldwide utility in combating COVID-19 by better directing the limited testing resources through prioritization of individuals for testing, thereby increasing the rate at which positive individuals can be identified and isolated.