Consistent Comparison of Symptom-based Methods for COVID-19 Infection Detection (Extended Abstract)

Jesús Rufino, Juan Marcos Ramirez, José Aguilar, Carlos Baquero, Jaya Champati, Davide Frey, Rosa Elvira Lillo, Antonio Fernandez Anta

Keywords: COVID-19 diagnosis, F1-score, light gradient boosting machine, logistic regression, rule-based methods.

TL;DR: Comparison of the performance of various COVID-19 diagnosis methods using datasets extracted from UMD-CTIS survey.

Abstract: During the global pandemic crisis, several COVID-19 diagnosis methods based on survey information have been proposed with the purpose of providing medical staff with quick detection tools that allow them to efficiently plan the limited healthcare resources. In general, these methods have been developed to detect COVID-19-positive cases from a particular combination of self-reported symptoms. In addition, these methods have been evaluated using datasets extracted from different studies with different characteristics. On the other hand, the University of Maryland, in partnership with Facebook, launched the Global COVID-19 Trends and Impact Survey (UMD-CTIS), the largest health surveillance tool to date that has collected information from 114 countries/territories from April 2020 to June 2022. This survey collected information on various individual features including gender, age groups, self-reported symptoms, isolation measures, and mental health status, among others. In this paper, we compare the performance of different COVID-19 diagnosis methods using the information collected by UMD-CTIS, for the years 2020 and 2021, in six countries: Brazil, Canada, Israel, Japan, Turkey, and South Africa. The evaluation of these methods with homogeneous data across countries and years provides a solid and consistent comparison among them.