@Article{info:doi/10.2196/33576,
author="Leal-Neto, Onicio
and Egger, Thomas
and Schlegel, Matthias
and Flury, Domenica
and Sumer, Johannes
and Albrich, Werner
and Babouee Flury, Baharak
and Kuster, Stefan
and Vernazza, Pietro
and Kahlert, Christian
and Kohler, Philipp",
title="Digital SARS-CoV-2 Detection Among Hospital Employees: Participatory Surveillance Study",
journal="JMIR Public Health Surveill",
year="2021",
month="Nov",
day="22",
volume="7",
number="11",
pages="e33576",
keywords="digital epidemiology; SARS-CoV-2; COVID-19; health care workers",
abstract="Background: The implementation of novel techniques as a complement to traditional disease surveillance systems represents an additional opportunity for rapid analysis. Objective: The 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. Methods: A 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. Results: From 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{\%}). Conclusions: Loss 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. ",
issn="2369-2960",
doi="10.2196/33576",
url="https://publichealth.jmir.org/2021/11/e33576",
url="https://doi.org/10.2196/33576",
url="http://www.ncbi.nlm.nih.gov/pubmed/34727046"
}