Optimal strategies to screen health care workers for COVID-19 in the US: a cost-effectiveness analysis

Background Transmission of SARS-CoV-2 in health care facilities poses a challenge against pandemic control. Health care workers (HCWs) have frequent and high-risk interactions with COVID-19 patients. We undertook a cost-effectiveness analysis to determine optimal testing strategies for screening HCWs to inform strategic decision-making in health care settings. Methods We modeled the number of new infections, quality-adjusted life years lost, and net costs related to six testing strategies including no test. We applied our model to four strata of HCWs, defined by the presence and timing of symptoms. We conducted sensitivity analyses to account for uncertainty in inputs. Results When screening recently symptomatic HCWs, conducting only a PCR test is preferable; it saves costs and improves health outcomes in the first week post-symptom onset, and costs $83,000 per quality-adjusted life year gained in the second week post-symptom onset. When screening HCWs in the late clinical disease stage, none of the testing approaches is cost-effective and thus no testing is preferable, yielding $11 and 0.003 new infections per 10 HCWs. For screening asymptomatic HCWs, antigen testing is preferable to PCR testing due to its lower cost. Conclusions Both PCR and antigen testing are beneficial strategies to identify infected HCWs and reduce transmission of SARS-CoV-2 in health care settings. IgG tests’ value depends on test timing and immunity characteristics, however it is not cost-effective in a low prevalence setting. As the context of the pandemic evolves, our study provides insight to health-care decision makers to keep the health care workforce safe and transmissions low.

Heterogeneity in testing for infectious diseases

Testing strategies have varied widely between nation states during the COVID-19 pandemic, in intensity as well as methodology. Some countries have mainly performed diagnostic testing while others have opted for mass-screening for the presence of SARS-CoV-2 as well. COVID passport solutions have been introduced, in which access to several aspects of public life requires either testing, proof of vaccination or a combination thereof. This creates a coupling between personal activity levels and testing behaviour which, as we show, leverages the heterogeneous behaviours in the population and turns this heterogeneity from a disadvantage to an advantage for epidemic control.

Analytic sensitivity of the Abbott BinaxNOW lateral flow immunochromatographic assay for the SARS-CoV-2 Omicron variant

The emergence of the SARS-CoV-2 Omicron variant has motivated a re-evaluation of the test characteristics for lateral flow immunochromatographic assays (LFIAs), commonly referred to as rapid antigen tests. To address this need, we evaluated the analytic sensitivity of one of the most widely used LFIAs in the US market, the Abbott BinaxNOW COVID-19 Ag At-Home Card using 32 samples of Omicron and 30 samples of the Delta variant. Samples were chosen to intentionally over-represent the range of viral loads where differences are most likely to appear. We found no changes in the analytic sensitivity of the BinaxNOW assay by variant even after controlling for variation in cycle threshold values in the two populations. Similar to prior studies, the sensitivity of the assay is highly dependent on the amount of virus present in the sample. While the analytic sensitivity of the BinaxNOW LFIA remains intact versus the Omicron variant, its clinical sensitivity is influenced by the interaction between viral replication, the dynamics of tissue tropism and the timing of sampling. Further research is necessary to optimally adapt current testing strategies to robustly detect early infection by the Omicron variant to prevent transmission.

Digital Participatory Platforms for Civic Engagement: A New Way of Participating in Society?

The article is focused on the digital participatory platforms (DPPs) as a tool to enhance civic engagement through dialogue and interaction with the Public Administration and to reduce Digital Divide. To this end, the article presents the objectives and the outcomes of “My Smart Quartier,” a project funded within the ERASMUS + 2017 Program, in which the Department of Architecture and Design (DAD) of the Politecnico di Torino participates with a consortium of European partners. The aim of this project is the setting up and testing strategies and actions to reduce digital illiteracy and increase citizen participation. Best practices from the project partner countries (France, Italy, Spain, Portugal) are selected and analyzed in order to understand if they can constitute innovative ways of participation and civic engagement. Specifically, the article identifies some parameters and indicators that can be used to bring out key success factors of digital participatory platforms.

Trends in Development of Novel Machine Learning Methods for the Identification of Gliomas in Datasets That Include Non-Glioma Images: A Systematic Review

PurposeMachine learning has been applied to the diagnostic imaging of gliomas to augment classification, prognostication, segmentation, and treatment planning. A systematic literature review was performed to identify how machine learning has been applied to identify gliomas in datasets which include non-glioma images thereby simulating normal clinical practice.Materials and MethodsFour databases were searched by a medical librarian and confirmed by a second librarian for all articles published prior to February 1, 2021: Ovid Embase, Ovid MEDLINE, Cochrane trials (CENTRAL), and Web of Science-Core Collection. The search strategy included both keywords and controlled vocabulary combining the terms for: artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, as well as related terms. The review was conducted in stepwise fashion with abstract screening, full text screening, and data extraction. Quality of reporting was assessed using TRIPOD criteria.ResultsA total of 11,727 candidate articles were identified, of which 12 articles were included in the final analysis. Studies investigated the differentiation of normal from abnormal images in datasets which include gliomas (7 articles) and the differentiation of glioma images from non-glioma or normal images (5 articles). Single institution datasets were most common (5 articles) followed by BRATS (3 articles). The median sample size was 280 patients. Algorithm testing strategies consisted of five-fold cross validation (5 articles), and the use of exclusive sets of images within the same dataset for training and for testing (7 articles). Neural networks were the most common type of algorithm (10 articles). The accuracy of algorithms ranged from 0.75 to 1.00 (median 0.96, 10 articles). Quality of reporting assessment utilizing TRIPOD criteria yielded a mean individual TRIPOD ratio of 0.50 (standard deviation 0.14, range 0.37 to 0.85).ConclusionSystematic review investigating the identification of gliomas in datasets which include non-glioma images demonstrated multiple limitations hindering the application of these algorithms to clinical practice. These included limited datasets, a lack of generalizable algorithm training and testing strategies, and poor quality of reporting. The development of more robust and heterogeneous datasets is needed for algorithm development. Future studies would benefit from using external datasets for algorithm testing as well as placing increased attention on quality of reporting standards.Systematic Review Registrationwww.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020209938, International Prospective Register of Systematic Reviews (PROSPERO 2020 CRD42020209938).

Modeling the Influence of Vaccine Administration on COVID-19 Testing Strategies

Vaccination is considered the best strategy for limiting and eliminating the COVID-19 pandemic. The success of this strategy relies on the rate of vaccine deployment and acceptance across the globe. As these efforts are being conducted, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is continuously mutating, which leads to the emergence of variants with increased transmissibility, virulence, and resistance to vaccines. One important question is whether surveillance testing is still needed in order to limit SARS-CoV-2 transmission in a vaccinated population. In this study, we developed a multi-scale mathematical model of SARS-CoV-2 transmission in a vaccinated population and used it to predict the role of testing in an outbreak with variants of increased transmissibility. We found that, for low transmissibility variants, testing was most effective when vaccination levels were low to moderate and its impact was diminished when vaccination levels were high. For high transmissibility variants, widespread vaccination was necessary in order for testing to have a significant impact on preventing outbreaks, with the impact of testing having maximum effects when focused on the non-vaccinated population.

RECENT DEVELOPMENTS IN LABORATORY DIAGNOSIS OF COVID-19 INFECTION- AN UPDATE

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This new virus and disease were unknown before the outbreak began in Wuhan, Hubei Province of China, in December 2019. The World Health Organization (WHO) initially declared COVID19 as the global public health emergency on 30th January 2020 and subsequently a pandemic on March 11, 2020. Besides availability of RT-PCR there is need for development of rapid point of care tests with better sensitivity and specicity which helps in early and accurate diagnosis and also aids in containing the spread . This review summarizes various molecular diagnostics methods, technical guidelines, and advanced testing strategies adopted in India for laboratory diagnosis of COVID-19.