Development and application of an optimal COVID-19 screening scale utilizing an interpretable machine learning algorithm.

COVID-19 is a huge global challenge, on which numerous people and resources all over the world have been focused over the last three years to slow down its spread. A screening scale for the early detection of COVID-19 cases is highly important to diagnose patients before they transmit the disease to others. Using the data collected from Sept. 2020 to Nov. 2020 from 12,123 employees of a company including a weekly self-report questionnaire and information on their demographics, chronic disease, work, and commute, we developed a short, optimized screening scale for the early detection of COVID-19. To develop the scale, we have modified an interpretable machine learning (ML) method which uses integer programming​ techniques, and consequently reduced its computational cost significantly and made it robust to erroneous data and protective of sensitive information without loss of performance. The developed scale is highly interpretable and easily scored to the extent that it does not require a computer or even a calculator. However, we showed that the operating characteristics of the screening scale on the test dataset are excellent (area under the ROC curve = 82% and area under the Precision–Recall curve = 27%) and comparable to other well-known ML models such as penalized logistic regression and random forest when predicting positive PCR test results within 7 days from the date the self-report questionnaire was filled out. From Dec. 2020 to Nov. 2021, the developed scale has been used in the company weekly to monitor and assess its employees. Although different variants of COVID-19 have emerged since the development of the scale, the results show that the scale has performed well in the early detection of COVID-19, which helped the company take appropriate actions to slow down the spread of COVID-19 among its employees successfully. We expect our findings in this paper to help researchers have a better understanding of COVID-19 and control the spread of the disease. [ABSTRACT FROM AUTHOR]