Digital SARS-CoV-2 Detection Among Hospital Employees: Participatory Surveillance Study.
JMIR Public Health Surveill
; 7(11): e33576, 2021 11 22.
Article
em En
| MEDLINE
| ID: mdl-34727046
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.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
SARS-CoV-2
/
COVID-19
Tipo de estudo:
Diagnostic_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
/
Screening_studies
Limite:
Child
/
Humans
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article