Analysing spatiotemporal patterns of Covid-19 confirmed deaths at the NUTS-2 regional level

During the ongoing Covid-19 pandemic, understanding the spatiotemporal patterns of the virus is crucial for policymakers to intervene promptly. The relevance of spatial proximity in the spread of the pandemic necessitates adequate tools, and noisy data must be properly treated. This study proposes obtaining clusters of European regions using smoothed curves of daily deaths from March 2020-March 2022. A functional representation of the curves w<s implemented to extract the features used in a clustering algorithm that allows spatial proximity. In a spatial regression model, the authors also investigated the role of clusters and pre-existing conditions on cumulative deaths, and observed that air pollution, health conditions, and population age structure are significantly associated with Covid-19 confirmed deaths.

Quantile regression for count data: jittering versus regression coefficients modelling in the analysis of credits earned by university students after remote teaching.

The extension of quantile regression to count data raises several issues. We compare the traditional approach, based on transforming the count variable using jittering, with a recently proposed approach in which the coefficients of quantile regression are modelled by parametric functions. We exploit both methods to analyse university students' data to evaluate the effect of emergency remote teaching due to COVID-19 on the number of credits earned by the students. The coefficients modelling approach performs a smoothing that is especially convenient in the tails of the distribution, preventing abrupt changes in the point estimates and increasing precision. Nonetheless, model selection is challenging because of the wide range of options and the limited availability of diagnostic tools. Thus the jittering approach remains fundamental to guide the choice of the parametric functions.

Understanding Research Trends in HyFlex (hybrid flexible) Instruction Model: A Scientometric Approach

The onset of the Covid-19 pandemic necessitated that higher education institutions go online and utilize a HyFlex instruction model. The current study used a scientometric approach to evaluate the current status of HyFlex, as well as a visual analysis of the topic. Published research from 1989-2021 was retrieved from Web of Science (WoS) and the search generated 1453 results, which were analysed by title, year of publication, authors, country, journal, and research area. The data was processed using VOSviewer and Bibliometrix R software to visualize trends for HyFlex. The research identified document types, author collaborations, annual scientific production, most relevant journals, collaboration network between authors, institutions, country, cluster coupling of authors, documents and sources, thematic evolution, and co-occurrence of all key wrd. The results indicated the topic gained interest in 2008, with the highest number of articles published in 2019-2020. The top collaborator and country with the highest volume of citations and published articles was the United States. Word clusters indicated the most repetitive words were students, education, performance, and knowledge. The visualization of data offers information on trends on the body of research as well as providing researchers an understanding of the topic. © 2022 Eskisehir Osmangazi University. All rights reserved.

Public debt sustainability: a bibliometric co-citation visualization analysis

Purpose: Due to the financial disturbances created by the COVID-19 pandemic and the burden on the government exchequer, it is expected to see a rise in the knowledge base of the research corpus so far as the government's fiscal sustainability is concerned. Therefore, the present research examines a systematic quantitative analysis of public debt sustainability research by applying a bibliometric approach. Research also analyzes journals, institutions, countries and authors contributing to public debt sustainability. Design/methodology/approach: This paper scrutinizes the published scientific research on public debt sustainability based on the dataset of 535 articles from 1991 to 2021 obtained from the Scopus database. Biblioshiny (R-based application) and VoSviewer software were used to perform bibliometric analysis through Performance analysis and science mapping techniques. The authors combined co-citation analysis (CCA), bibliometric analysis, keyword co-occurrence analysis (KCA) and a conceptual thematic map of the most cited articles to find the intellectual structure. Findings: The research identified three dominating clusters, e.g. fiscal sustainability and policy rules, empirical sustainability testing and debt and growth dynamics. Another finding was that most articles were analytical and empirical and few descriptive articles were found. Owing to the empirical nature of the domain, the issues concerning public debt sustainability have continued to change over the past decades for different economies, reflecting the complexity and diversity of economic structures of different economies at different times. Originality/value: The insight of this article provides academicians and researchers with a more refined comprehension of the conceptual and intellectual structure of the research corpus. The present research complements the existing literature review studies by pushing the research towards emerging or less developed issues such as financial and debt crises. © 2022, Emerald Publishing Limited.

Papers Please-Predictive Factors of National and International Attitudes Toward Immunity and Vaccination Passports: Online Representative Surveys

Background: In response to the COVID-19 pandemic, countries are introducing digital passports that allow citizens to return to normal activities if they were previously infected with (immunity passport) or vaccinated against (vaccination passport) SARS-CoV-2. To be effective, policy decision-makers must know whether these passports will be widely accepted by the public and under what conditions. This study focuses attention on immunity passports, as these may prove useful in countries both with and without an existing COVID-19 vaccination program;however, our general findings also extend to vaccination passports.Objective: We aimed to assess attitudes toward the introduction of immunity passports in six countries, and determine whatMethods: We collected 13,678 participants through online representative sampling across six countries-Australia, Japan, Taiwan, Germany, Spain, and the United Kingdom-during April to May of the 2020 COVID-19 pandemic, and assessed attitudes and support for the introduction of immunity passports.Results: Immunity passport support was moderate to low, being the highest in Germany (775/1507 participants, 51.43%) and the United Kingdom (759/1484, 51.15%);followed by Taiwan (2841/5989, 47.44%), Australia (963/2086, 46.16%), and Spain (693/1491, 46.48%);and was the lowest in Japan (241/1081, 22.94%). Bayesian generalized linear mixed effects modeling was used to assess predictive factors for immunity passport support across countries. International results showed neoliberal worldviews (odds ratio [OR] 1.17, 95% CI 1.13-1.22), personal concern (OR 1.07, 95% CI 1.00-1.16), perceived virus severity (OR 1.07, 95% CI 1.01-1.14), the fairness of immunity passports (OR 2.51, 95% CI 2.36-2.66), liking immunity passports (OR 2.77, 95% CI 2.61-2.94), and a willingness to become infected to gain an immunity passport (OR 1.6, 95% CI 1.51-1.68) were all predictive 0.61, 95% CI 0.57-0.65), and risk of harm to society (OR 0.71, 95% CI 0.67-0.76) predicted a decrease in support for immunity

Complement activation induces excessive T cell cytotoxicity in severe COVID-19

Severe COVID-19 is linked to both dysfunctional immune response and unrestrained immunopathology, and it remains unclear whether T cells contribute to disease pathology. Here, we combined single-cell transcriptomics and single-cell proteomics with mechanistic studies to assess pathogenic T cell functions and inducing signals. We identified highly activated CD16(+) T cells with increased cytotoxic functions in severe COVID-19. CD16 expression enabled immune-complex-mediated, T cell receptor-independent degranulation and cytotoxicity not found in other diseases. CD16(+) T cells from COVID-19 patients promoted microvascular endothelial cell injury and release of neutrophil and monocyte chemoattractants. CD16(+) T cell clones persisted beyond acute disease maintaining their cytotoxic phenotype. Increased generation of C3a in severe COVID-19 induced activated CD16(+) cytotoxic T cells. Proportions of activated CD16(+) T cells and plasma levels of complement proteins upstream of C3a were associated with fatal outcome of COVID-19, supporting a pathological role of exacerbated cytotoxicity and complement activation in COVID-19.

Extending the susceptible-exposed-infected-removed (SEIR) model to handle the false negative rate and symptom-based administration of COVID-19 diagnostic tests: SEIR-fansy.

False negative rates of severe acute respiratory coronavirus 2 diagnostic tests, together with selection bias due to prioritized testing can result in inaccurate modeling of COVID-19 transmission dynamics based on reported "case" counts. We propose an extension of the widely used Susceptible-Exposed-Infected-Removed (SEIR) model that accounts for misclassification error and selection bias, and derive an analytic expression for the basic reproduction number R0 as a function of false negative rates of the diagnostic tests and selection probabilities for getting tested. Analyzing data from the first two waves of the pandemic in India, we show that correcting for misclassification and selection leads to more accurate prediction in a test sample. We provide estimates of undetected infections and deaths between April 1, 2020 and August 31, 2021. At the end of the first wave in India, the estimated under-reporting factor for cases was at 11.1 (95% CI: 10.7,11.5) and for deaths at 3.58 (95% CI: 3.5,3.66) as of February 1, 2021, while they change to 19.2 (95% CI: 17.9, 19.9) and 4.55 (95% CI: 4.32, 4.68) as of July 1, 2021. Equivalently, 9.0% (95% CI: 8.7%, 9.3%) and 5.2% (95% CI: 5.0%, 5.6%) of total estimated infections were reported on these two dates, while 27.9% (95% CI: 27.3%, 28.6%) and 22% (95% CI: 21.4%, 23.1%) of estimated total deaths were reported. Extensive simulation studies demonstrate the effect of misclassification and selection on estimation of R0 and prediction of future infections. A R-package SEIRfansy is developed for broader dissemination.

KeyPathwayMineR: De Novo Pathway Enrichment in the R Ecosystem.

De novo pathway enrichment is a systems biology approach in which OMICS data are projected onto a molecular interaction network to identify subnetworks representing condition-specific functional modules and molecular pathways. Compared to classical pathway enrichment analysis methods, de novo pathway enrichment is not limited to predefined lists of pathways from (curated) databases and thus particularly suited for discovering novel disease mechanisms. While several tools have been proposed for pathway enrichment, the integration of de novo pathway enrichment in end-to-end OMICS analysis workflows in the R programming language is currently limited to a single tool. To close this gap, we have implemented an R package KeyPathwayMineR (KPM-R). The package extends the features and usability of existing versions of KeyPathwayMiner by leveraging the power, flexibility and versatility of R and by providing various novel functionalities for performing data preparation, visualization, and comparison. In addition, thanks to its interoperability with a plethora of existing R packages in e.g., Bioconductor, CRAN, and GitHub, KPM-R allows carrying out the initial preparation of the datasets and to meaningfully interpret the extracted subnetworks. To demonstrate the package's potential, KPM-R was applied to bulk RNA-Seq data of nasopharyngeal swabs from SARS-CoV-2 infected individuals, and on single cell RNA-Seq data of aging mice tissue from the Tabula Muris Senis atlas.

RiskEstim: A Software Package to Quantify COVID-19 Importation Risk

We present an R package developed to quantify coronavirus disease 2019 (COVID-19) importation risk. Quantifying and visualizing the importation risk of COVID-19 from inbound travelers is urgent and imperative to trigger public health responses, especially in the early stages of the COVID-19 pandemic and emergence of new SARS-CoV-2 variants. We provide a general modeling framework to estimate COVID-19 importation risk using estimated pre-symptomatic prevalence of infection and air traffic data from the multi-origin places. We use Hong Kong as a case study to illustrate how our modeling framework can estimate the COVID-19 importation risk into Hong Kong from cities in Mainland China in real time. This R package can be used as a complementary component of the pandemic surveillance system to monitor spread in the next pandemic.

cosinoRmixedeffects: an R package for mixed-effects cosinor models.

BACKGROUND: Wearable devices enable monitoring and measurement of physiological parameters over a 24-h period, and some of which exhibit circadian rhythm characteristics. However, the currently available R package cosinor could only analyze daily cross-sectional data and compare the parameters between groups with two levels. To evaluate longitudinal changes in the circadian patterns, we need to extend the model to a mixed-effect model framework, allowing for random effects and interaction between COSINOR parameters and time-varying covariates. RESULTS: We developed the cosinoRmixedeffects R package for modelling longitudinal periodic data using mixed-effects cosinor models. The model allows for covariates and interactions with the non-linear parameters MESOR, amplitude, and acrophase. To facilitate ease of use, the package utilizes the syntax and functions of the widely used emmeans package to obtain estimated marginal means and contrasts. Estimation and hypothesis testing involving the non-linear circadian parameters are carried out using bootstrapping. We illustrate the package functionality by modelling daily measurements of heart rate variability (HRV) collected among health care workers over several months. Differences in circadian patterns of HRV between genders, BMI, and during infection with SARS-CoV2 are evaluated to illustrate how to perform hypothesis testing. CONCLUSION: cosinoRmixedeffects package provides the model fitting, estimation and hypothesis testing for the mixed-effects COSINOR model, for the linear and non-linear circadian parameters MESOR, amplitude and acrophase. The model accommodates factors with any number of categories, as well as complex interactions with circadian parameters and categorical factors.

nCov2019: an R package for studying the COVID-19 coronavirus pandemic.

BACKGROUND: The global spreading of the COVID-19 coronavirus is still a serious public health challenge. Although there are a large number of public resources that provide statistics data, tools for retrospective historical data and convenient visualization are still valuable. To provide convenient access to data and visualization on the pandemic we developed an R package, nCov2019 (https://github.com/YuLab-SMU/nCov2019). METHODS: We collect stable and reliable data of COVID-19 cases from multiple authoritative and up-to-date sources, and aggregate the most recent and historical data for each country or even province. Medical progress information, including global vaccine development and therapeutics candidates, were also collected and can be directly accessed in our package. The nCov2019 package provides an R language interfaces and designed functions for data operation and presentation, a set of interfaces to fetch data subset intuitively, visualization methods, and a dashboard with no extra coding requirement for data exploration and interactive analysis. RESULTS: As of January 14, 2021, the global health crisis is still serious. The number of confirmed cases worldwide has reached 91,268,983. Following the USA, India has reached 10 million confirmed cases. Multiple peaks are observed in many countries. Under the efforts of researchers, 51 vaccines and 54 drugs are under development and 14 of these vaccines are already in the pre-clinical phase. DISCUSSION: The nCov2019 package provides detailed statistics data, visualization functions and the Shiny web application, which allows researchers to keep abreast of the latest epidemic spread overview.

Duplex Shiny app quantification of the sepsis biomarkers C-reactive protein and interleukin-6 in a fast quantum dot labeled lateral flow assay.

Fast point-of-care (POC) diagnostics represent an unmet medical need and include applications such as lateral flow assays (LFAs) for the diagnosis of sepsis and consequences of cytokine storms and for the treatment of COVID-19 and other systemic, inflammatory events not caused by infection. Because of the complex pathophysiology of sepsis, multiple biomarkers must be analyzed to compensate for the low sensitivity and specificity of single biomarker targets. Conventional LFAs, such as gold nanoparticle dyed assays, are limited to approximately five targets-the maximum number of test lines on an assay. To increase the information obtainable from each test line, we combined green and red emitting quantum dots (QDs) as labels for C-reactive protein (CRP) and interleukin-6 (IL-6) antibodies in an optical duplex immunoassay. CdSe-QDs with sharp and tunable emission bands were used to simultaneously quantify CRP and IL-6 in a single test line, by using a single UV-light source and two suitable emission filters for readout through a widely available BioImager device. For image and data processing, a customized software tool, the MultiFlow-Shiny app was used to accelerate and simplify the readout process. The app software provides advanced tools for image processing, including assisted extraction of line intensities, advanced background correction and an easy workflow for creation and handling of experimental data in quantitative LFAs. The results generated with our MultiFlow-Shiny app were superior to those generated with the popular software ImageJ and resulted in lower detection limits. Our assay is applicable for detecting clinically relevant ranges of both target proteins and therefore may serve as a powerful tool for POC diagnosis of inflammation and infectious events.

Global Research on Coronaviruses: An R Package.

BACKGROUND: In these trying times, we developed an R package about bibliographic references on coronaviruses. Working with reproducible research principles based on open science, disseminating scientific information, providing easy access to scientific production on this particular issue, and offering a rapid integration in researchers' workflows may help save time in this race against the virus, notably in terms of public health. OBJECTIVE: The goal is to simplify the workflow of interested researchers, with multidisciplinary research in mind. With more than 60,500 medical bibliographic references at the time of publication, this package is among the largest about coronaviruses. METHODS: This package could be of interest to epidemiologists, researchers in scientometrics, biostatisticians, as well as data scientists broadly defined. This package collects references from PubMed and organizes the data in a data frame. We then built functions to sort through this collection of references. Researchers can also integrate the data into their pipeline and implement them in R within their code libraries. RESULTS: We provide a short use case in this paper based on a bibliometric analysis of the references made available by this package. Classification techniques can also be used to go through the large volume of references and allow researchers to save time on this part of their research. Network analysis can be used to filter the data set. Text mining techniques can also help researchers calculate similarity indices and help them focus on the parts of the literature that are relevant for their research. CONCLUSIONS: This package aims at accelerating research on coronaviruses. Epidemiologists can integrate this package into their workflow. It is also possible to add a machine learning layer on top of this package to model the latest advances in research about coronaviruses, as we update this package daily. It is also the only one of this size, to the best of our knowledge, to be built in the R language.