Digital SARS-CoV-2 Detection Among Hospital Employees: Participatory Surveillance Study

• Main Authors: Onicio Leal-Neto (Author), Thomas Egger (Author), Matthias Schlegel (Author), Domenica Flury (Author), Johannes Sumer (Author), Werner Albrich (Author), Baharak Babouee Flury (Author), Stefan Kuster (Author), Pietro Vernazza (Author), Christian Kahlert (Author), Philipp Kohler (Author)
• Format: Book
• Published: JMIR Publications, 2021-11-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2369-2960; 10.2196/33576
• Keywords: Public aspects of medicine; RA1-1270
• Source: JMIR Public Health and Surveillance, Vol 7, Iss 11, p e33576 (2021)

BackgroundThe implementation of novel techniques as a complement to traditional disease surveillance systems represents an additional opportunity for rapid analysis. ObjectiveThe objective of this work is to describe a web-based participatory surveillance strategy among health care workers (HCWs) in two Swiss hospitals during the first wave of COVID-19. MethodsA prospective cohort of HCWs was recruited in March 2020 at the Cantonal Hospital of St. Gallen and the Eastern Switzerland Children's Hospital. For data analysis, we used a combination of the following techniques: locally estimated scatterplot smoothing (LOESS) regression, Spearman correlation, anomaly detection, and random forest. ResultsFrom March 23 to August 23, 2020, a total of 127,684 SMS text messages were sent, generating 90,414 valid reports among 1004 participants, achieving a weekly average of 4.5 (SD 1.9) reports per user. The symptom showing the strongest correlation with a positive polymerase chain reaction test result was loss of taste. Symptoms like red eyes or a runny nose were negatively associated with a positive test. The area under the receiver operating characteristic curve showed favorable performance of the classification tree, with an accuracy of 88% for the training data and 89% for the test data. Nevertheless, while the prediction matrix showed good specificity (80.0%), sensitivity was low (10.6%). ConclusionsLoss of taste was the symptom that was most aligned with COVID-19 activity at the population level. At the individual level-using machine learning-based random forest classification-reporting loss of taste and limb/muscle pain as well as the absence of runny nose and red eyes were the best predictors of COVID-19.