SARS-CoV-2 Omicron variant shedding during respiratory activities

BACKGROUND

As the world transitions to COVID-19 endemicity, studies focusing on aerosol shedding of highly transmissible SARS-CoV-2 variants of concern (VOCs) are vital for the calibration of infection control measures against VOCs that are likely to circulate seasonally.

OBJECTIVE

This follow-up G-II aerosol sampling study aims to compare the aerosol shedding patterns of Omicron VOC samples with pre-Omicron variants analyzed in our previous study.

STUDY DESIGN

Coarse and fine aerosol samples from 47 SARS-CoV-2 infected patients were collected during various respiratory activities (passive breathing, talking, and singing) and analyzed via reverse transcription quantitative polymerase chain reaction (RT-qPCR) and virus culture.

RESULTS

Compared to patients infected with pre-Omicron variants, comparable SARS-CoV-2 RNA copy numbers were detectable in aerosol samples of Omicron infected patients despite being fully vaccinated. Omicron-infected patients also showed a slight increase in viral aerosol shedding during breathing activities, and were more likely to have persistent aerosol shedding beyond 7 days post-disease onset.

CONCLUSION

This follow-up study reaffirms the aerosol shedding properties of Omicron, and should guide continued layering of public health interventions even in highly vaccinated populations.

Validation of a Natural Language Processing Algorithm for Detecting Infectious Disease Symptoms in Primary Care Electronic Medical Records in Singapore

Background

Free-text clinical records provide a source of information that complements traditional disease surveillance. To electronically harness these records, they need to be transformed into codified fields by natural language processing algorithms.

Objective

The aim of this study was to develop, train, and validate Clinical History Extractor for Syndromic Surveillance (CHESS), an natural language processing algorithm to extract clinical information from free-text primary care records.

Methods

CHESS is a keyword-based natural language processing algorithm to extract 48 signs and symptoms suggesting respiratory infections, gastrointestinal infections, constitutional, as well as other signs and symptoms potentially associated with infectious diseases. The algorithm also captured the assertion status (affirmed, negated, or suspected) and symptom duration.

Electronic medical records from the National Healthcare Group Polyclinics, a major public sector primary care provider in Singapore, were randomly extracted and manually reviewed by 2 human reviewers, with a third reviewer as the adjudicator. The algorithm was evaluated based on 1680 notes against the human-coded result as the reference standard, with half of the data used for training and the other half for validation.

Results

The symptoms most commonly present within the 1680 clinical records at the episode level were those typically present in respiratory infections such as cough (744/7703, 9.66%), sore throat (591/7703, 7.67%), rhinorrhea (552/7703, 7.17%), and fever (928/7703, 12.04%). At the episode level, CHESS had an overall performance of 96.7% precision and 97.6% recall on the training dataset and 96.0% precision and 93.1% recall on the validation dataset. Symptoms suggesting respiratory and gastrointestinal infections were all detected with more than 90% precision and recall. CHESS correctly assigned the assertion status in 97.3%, 97.9%, and 89.8% of affirmed, negated, and suspected signs and symptoms, respectively (97.6% overall accuracy). Symptom episode duration was correctly identified in 81.2% of records with known duration status.

Conclusions

We have developed an natural language processing algorithm dubbed CHESS that achieves good performance in extracting signs and symptoms from primary care free-text clinical records. In addition to the presence of symptoms, our algorithm can also accurately distinguish affirmed, negated, and suspected assertion statuses and extract symptom durations.