Developing a Pre-Testing Diagnostic Tool for COVID-19 Using Big Data Predictive Analytics

Abstract

Background: Standard Polymerase Chain reaction (PCR) tests for SARS-COV-2 are in short supply to meet demand in many countries presenting a need to improve testing efficiency. Pre-testing tools can be used to ensure continued public safety as systems move through the pandemic. In this study we set out to create an instrument based on big data predictive tools to assess pre-test probability for COVID-19. Methods: We analyzed data reported by the Israeli Ministry of Health (IMOH) for standard PCR tests done for SARS-COV-2 from March to April, 2020, overall 108,852 cases. Demographics and symptoms of the patients were collected at time of testing. Four supervised machine learning algorithms were used to analyze 20,537 test results of cases who presented with symptoms. Model results were used to develop efficient pre-test diagnostic tool. Findings: Of symptomatic patients tested, 6,427 (31.3%) tested positive for SARS-COV-2, and 14,110 (68.7%) tested negative. In all models used headache, shortness of breath, sore throat, fever, and having contact with an infected person came up as most predictive of a positive test. The area under the curve of the receiver operating characteristic curve for the test sample was found to be 0.88 and the misclassification rate was between 4.7% and 6.5% for all predictive models, demonstrating effective classification ability. Using our pre-test probability screening tool with conventional PCR testing can potentially increase efficiency by 141%. Interpretation: We suggest a simple diagnostic pre-test tool for assessing the probability of infection can increase efficiency of testing and effectiveness of public health COVID-19 programs. Funding: NoneDeclaration of Interests: The authors declare no competing interests. Ethics Approval Statement: The authors noted that the analysis presented was approved by the IMOH Data Sharing Institutional Review Board.