Symptom diaries as a digital tool to detect SARS-CoV-2 infections and differentiate between prevalent variants.

ABSTRACT: The COVID-19 pandemic and the high numbers of infected individuals pose major challenges for public health departments. To overcome these challenges, the health department in Cologne has developed a software called DiKoMa. This software offers the possibility to track contact and index persons, but also provides a digital symptom diary. In this work, the question of whether these can also be used for diagnostic purposes will be investigated. Machine learning makes it possible to identify infections based on early symptom profiles and to distinguish between the predominant dominant variants. Focusing on the occurrence of the symptoms in the first week, a decision tree is trained for the differentiation between contact and index persons and the prevailing dominant variants (Wildtype, Alpha,

SUBMITTER: Grune B 

PROVIDER: S-EPMC9701827 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature