Consistent comparison of symptom-based methods for COVID-19 infection detection

• Type: Another Publication in an International Scientific Journal
• Date: 2023

Authors

Rufino, J (Author) Other; Ramirez, JM (Author) Other; Aguilar, J (Author) Other; Baquero, C (Author) FEUP; Champati, J (Author) Other; Frey, D (Author) Other; Lillo, RE (Author) Other; Fernandez Anta, A (Author) Other

Journal

Title: International Journal of Medical Informatics

Vol. 177

ISSN: 1386-5056

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 0 Citations

Other information

Authenticus ID: P-00Y-TGE

DOI: 10.1016/j.ijmedinf.2023.105133

Abstract (EN): Background: During the global pandemic crisis, various detection methods of COVID-19-positive cases based on self-reported information were introduced to provide quick diagnosis tools for effectively planning and managing healthcare resources. These methods typically identify positive cases based on a particular combination of symptoms, and they have been evaluated using different datasets.Purpose: This paper presents a comprehensive comparison of various COVID-19 detection methods based on self-reported information using the University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS), a large health surveillance platform, which was launched in partnership with Facebook.Methods: Detection methods were implemented to identify COVID-19-positive cases among UMD-CTIS participants reporting at least one symptom and a recent antigen test result (positive or negative) for six countries and two periods. Multiple detection methods were implemented for three different categories: rule-based approaches, logistic regression techniques, and tree-based machine-learning models. These methods were evaluated using different metrics including F1-score, sensitivity, specificity, and precision. An explainability analysis has also been conducted to compare methods.Results: Fifteen methods were evaluated for six countries and two periods. We identify the best method for each category: rule-based methods (F1-score: 51.48% -71.11%), logistic regression techniques (F1-score: 39.91% -71.13%), and tree-based machine learning models (F1-score: 45.07% -73.72%). According to the explainability analysis, the relevance of the reported symptoms in COVID-19 detection varies between countries and years. However, there are two variables consistently relevant across approaches: stuffy or runny nose, and aches or muscle pain.Conclusions: Regarding the categories of detection methods, evaluating detection methods using homogeneous data across countries and years provides a solid and consistent comparison. An explainability analysis of a tree-based machine-learning model can assist in identifying infected individuals specifically based on their relevant symptoms. This study is limited by the self-report nature of data, which cannot replace clinical diagnosis.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10