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. 2021 Nov 22;7(11):e33576.
doi: 10.2196/33576.

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

Onicio Leal-Neto  1 , Thomas Egger  2 , Matthias Schlegel  2 , Domenica Flury  2 , Johannes Sumer  2 , Werner Albrich  2 , Baharak Babouee Flury  2   3 , Stefan Kuster  4 , Pietro Vernazza  2 , Christian Kahlert #  2   5 , Philipp Kohler #  2
Affiliations

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

Onicio Leal-Neto et al. JMIR Public Health Surveill. .

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.

Keywords: COVID-19; SARS-CoV-2; digital epidemiology; health care workers.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Temporal distribution and LOESS regression of symptoms related to acute respiratory infection in health care workers at two hospitals in Switzerland. FOPH: cases documented by the Federal Office of Public Health; LOESS: locally estimated scatterplot smoothing.
Figure 2
Figure 2
Temporal distribution of the FOPH proportion of positives, indicating which types of anomalies occurred in health care workers in two hospitals in Switzerland. FOPH: Federal Office of Public Health.
Figure 3
Figure 3
Correlation matrix using the Spearman method for symptoms and positive results in health care workers in two hospitals in Switzerland during the study period. FOPH: Federal Office of Public Health.
Figure 4
Figure 4
Significance matrix showcasing the positive and negative correlations between variables in health care workers in two hospitals in Switzerland during the study period. A larger dot represents a higher correlation.
Figure 5
Figure 5
Receiver operating characteristic curve for the random forest model.
Figure 6
Figure 6
Boxplot of the importance of symptoms and their capacity to predict the expected outcome based on the random forest algorithm (P<.05). Loss of taste, limb/muscle pain, FOPH (Federal Office of Public Health), sore throat, cough, and shortness of breath were positively associated with the outcome. Runny nose and red itchy eyes were negatively associated with the outcome. Fever was neither positively nor negatively associated with the outcome.
See this image and copyright information in PMC

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