Shedding of infectious SARS-CoV-2 despite vaccination.

The SARS-CoV-2 Delta Variant of Concern is highly transmissible and contains mutations that confer partial immune escape. The emergence of Delta in North America caused the first surge in COVID-19 cases after SARS-CoV-2 vaccines became widely available. To determine whether individuals infected despite vaccination might be capable of transmitting SARS-CoV-2, we compared RT-PCR cycle threshold (Ct) data from 20,431 test-positive anterior nasal swab specimens from fully vaccinated (n = 9,347) or unvaccinated (n = 11,084) individuals tested at a single commercial laboratory during the interval 28 June- 1 December 2021 when Delta variants were predominant. We observed no significant effect of vaccine status alone on Ct value, nor when controlling for vaccine product or sex. Testing a subset of low-Ct (<25) samples, we detected infectious virus at similar rates, and at similar titers, in specimens from vaccinated and unvaccinated individuals. These data indicate that vaccinated individuals infected with Delta variants are capable of shedding infectious SARS-CoV-2 and could play a role in spreading COVID-19.

Correction: Identifying COVID-19 Outbreaks From Contact-Tracing Interview Forms for Public Health Departments: Development of a Natural Language Processing Pipeline.

[This corrects the article DOI: 10.2196/36119.].

Identifying COVID-19 Outbreaks From Contact-Tracing Interview Forms for Public Health Departments: Development of a Natural Language Processing Pipeline.

BACKGROUND: In Wisconsin, COVID-19 case interview forms contain free-text fields that need to be mined to identify potential outbreaks for targeted policy making. We developed an automated pipeline to ingest the free text into a pretrained neural language model to identify businesses and facilities as outbreaks. OBJECTIVE: We aimed to examine the precision and recall of our natural language processing pipeline against existing outbreaks and potentially new clusters. METHODS: Data on cases of COVID-19 were extracted from the Wisconsin Electronic Disease Surveillance System (WEDSS) for Dane County between July 1, 2020, and June 30, 2021. Features from the case interview forms were fed into a Bidirectional Encoder Representations from Transformers (BERT) model that was fine-tuned for named entity recognition (NER). We also developed a novel location-mapping tool to provide addresses for relevant NER. Precision and recall were measured against manually verified outbreaks and valid addresses in WEDSS. RESULTS: There were 46,798 cases of COVID-19, with 4,183,273 total BERT tokens and 15,051 unique tokens. The recall and precision of the NER tool were 0.67 (95% CI 0.66-0.68) and 0.55 (95% CI 0.54-0.57), respectively. For the location-mapping tool, the recall and precision were 0.93 (95% CI 0.92-0.95) and 0.93 (95% CI 0.92-0.95), respectively. Across monthly intervals, the NER tool identified more potential clusters than were verified in WEDSS. CONCLUSIONS: We developed a novel pipeline of tools that identified existing outbreaks and novel clusters with associated addresses. Our pipeline ingests data from a statewide database and may be deployed to assist local health departments for targeted interventions.

Interventions to Disrupt Coronavirus Disease Transmission at a University, Wisconsin, USA, August-October 2020.

University settings have demonstrated potential for coronavirus disease (COVID-19) outbreaks; they combine congregate living, substantial social activity, and a young population predisposed to mild illness. Using genomic and epidemiologic data, we describe a COVID-19 outbreak at the University of Wisconsin-Madison, Madison, Wisconsin, USA. During August-October 2020, a total of 3,485 students, including 856/6,162 students living in dormitories, tested positive. Case counts began rising during move-in week, August 25-31, 2020, then rose rapidly during September 1-11, 2020. The university initiated multiple prevention efforts, including quarantining 2 dormitories; a subsequent decline in cases was observed. Genomic surveillance of cases from Dane County, in which the university is located, did not find evidence of transmission from a large cluster of cases in the 2 quarantined dorms during the outbreak. Coordinated implementation of prevention measures can reduce COVID-19 spread in university settings and may limit spillover to the surrounding community.

Self-harm injury hospitalisations: an analysis of case selection criteria.

BACKGROUND: This study explores the impact of using different criteria to identify nonfatal hospitalisations with self-harm injuries using 2017-2018 Wisconsin discharge data. METHODS: Using International Classification of Diseases, 10th Revision, Clinical Modification codes, we classified records by three mutually exclusive selection criteria: subset A--principal diagnosis of injury, and any code for self-harm, initial encounter only; subset B--non-injury principal diagnosis, and any code for self-harm, initial encounter only; subset C--any principal diagnosis, and any code for self-harm, subsequent and sequelae encounters only. These categories were used to conduct two separate logistic regression models. Model 1 analysed the impact of surveillance limited to a principal diagnosis of injury, initial self-harm encounter (subset B compared with A). Model 2 analysed the impact if limited to initial encounters for self-harm, regardless of principal diagnosis (subset C compared with (A+B)). Both patient-level and visit-level analyses were conducted. RESULTS: For both patient-level models, subsets that included additional records based on an expansion of selection criteria were significantly more likely to include children (model 1: OR 2.8, model 2: OR 2.9; compared with those 25-54 years), those with mental health disorders (model 1: OR 6.5, model 2: OR 4.3) and rural residents (model 1: OR 1.2, model 2: OR 1.4). Drug-related disorder and means of self-harm were significantly different among subsets for both models. Visit-level analyses revealed similar results. DISCUSSION: Expanding case selection criteria would better capture the scale of hospitalisation for nonfatal self-harm. Using restrictive selection criteria may result in biased understanding of the affected populations, potentially impacting the development of policy and prevention programmes.