Symptom diaries as a digital tool to detect SARS-CoV-2 infections and differentiate between prevalent variants - Grüne, Barbara; Kugler, Sabine; Ginzel, Sebastian; Wolff, Anna; Buess, Michael; Kossow, Annelene; Küfer-Weiß, Annika; Rüping, Stefan; Neuhann, Florian (Frontiers in Public Health, 2022)
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. Symptom diaries offer the possibility to differentiate between prevailing dominant variants from early symptom profiles through machine learning. 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, Delta, Omicron). The model is evaluated, using sex and age stratified cross-validation and validated by symptom profiles of the first 6 days. The variants achieve an AUC-ROC from 0.89 for Omicron and 0.6 for Alpha. No significant differences are observed for the results of the validation set (Alpha 0.63, Omicron 0.87). The evaluation of symptom constellations using artificial intelligence in a learning system can determine the individual risk for the presence of an infection, allows assignment to virus variants, and can contribute to the management of epidemics and pandemics on a national and international level. It can help to reduce the number of specific tests in times of low labor capacity and could help to early identify new virus variants.