1,720,960 research outputs found
A multivariate spatio-temporal model for the incidence of imported COVID-19 cases and COVID-19 deaths in Cuba
No full textTo monitor the COVID-19 epidemic in Cuba, data on several epidemiological indicators have been collected on a daily basis for each municipality. Studying the spatio-temporal dynamics in these indicators, and how they behave similarly, can help us better understand how COVID-19 spread across Cuba. Therefore, spatio-temporal models can be used to analyze these indicators. Univariate spatio-temporal models have been thoroughly studied, but when interest lies in studying the association between multiple outcomes, a joint model that allows for association between the spatial and temporal patterns is necessary. The purpose of our study was to develop a multivariate spatio-temporal model to study the association between the weekly number of COVID-19 deaths and the weekly number of imported COVID-19 cases in Cuba during 2021. To allow for correlation between the spatial patterns, a multivariate conditional autoregressive prior (MCAR) was used. Correlation between the temporal patterns was taken into account by using two approaches; either a multivariate random walk prior was used or a multivariate conditional autoregressive prior (MCAR) was used. All models were fitted within a Bayesian framework.status: Publishe
A joint penalized spline smoothing model for the number of positive and negative COVID-19 tests
No full textOne of the key tools to understand and reduce the spread of the SARS-CoV-2 virus is testing. The total number of tests, the number of positive tests, the number of negative tests, and the positivity rate are interconnected indicators and vary with time. To better understand the relationship between these indicators, against the background of an evolving pandemic, the association between the number of positive tests and the number of negative tests is studied using a joint modeling approach. All countries in the European Union, Switzerland, the United Kingdom, and Norway are included in the analysis. We propose a joint penalized spline model in which the penalized spline is reparameterized as a linear mixed model. The model allows for flexible trajectories by smoothing the country-specific deviations from the overall penalized spline and accounts for heteroscedasticity by allowing the autocorrelation parameters and residual variances to vary among countries. The association between the number of positive tests and the number of negative tests is derived from the joint distribution for the random intercepts and slopes. The correlation between the random intercepts and the correlation between the random slopes were both positive. This suggests that, when countries increase their testing capacity, both the number of positive tests and negative tests will increase. A significant correlation was found between the random intercepts, but the correlation between the random slopes was not significant due to a wide credible interval.Funding:
The authors declare that no specific
funds, grants, or other support were received
during the preparation of this manuscript
A Federated Data Analysis Approach for the Evaluation of Surrogate Endpoints
No full textIn clinical trials, surrogate endpoints, that are more cost-effective, occur earlier, or are more frequently measured, are sometimes used to replace costly, late, or rare true endpoints. Regulatory authorities typically require thorough evaluation and validation to accept these surrogate endpoints as reliable substitutes. To this end, the meta-analytic framework is considered a very viable approach to validate surrogates at both trial and individual levels. However, this framework requires data from multiple trials or centers, posing challenges when data sharing is not feasible. In this article, we propose a federated data analysis approach that allows organizations to maintain control over their datasets while still enabling surrogate validation through meta-analytic techniques. In this approach, there is no longer a need for raw data sharing. Instead, independent analyses are conducted at each organization. Thereafter, the results of these independent analyses are aggregated at a central analysis hub and the metrics for surrogate evaluation are extracted. We apply this approach to simulated and real clinical data, demonstrating how this federated approach can overcome data-sharing constraints and validate surrogate endpoints in decentralized settings
Assessing the impact of COVID-19 passes and mandates on disease transmission, vaccination intention, and uptake: a scoping review
No full textPurpose: Policymakers have struggled to maintain SARS-CoV-2 transmission at levels that are manageable to contain the COVID-19 disease burden while enabling a maximum of societal and economic activities. One of the tools that have been used to facilitate this is the so-called “COVID-19 pass”. We aimed to document current evidence on the effectiveness of COVID-19 passes, distinguishing their indirect effects by improving vaccination intention and uptake from their direct effects on COVID-19 transmission measured by the incidence of cases, hospitalizations, and deaths. Methods: We performed a scoping review on the scientific literature of the proposed topic covering the period January 2021 to September 2022, in accordance with the PRISMA-ScR guidelines for scoping reviews. Results: Out of a yield of 4,693 publications, 45 studies from multiple countries were retained for full-text review. The results suggest that implementing COVID-19 passes tends to reduce the incidence of cases, hospitalizations, and deaths due to COVID-19. The use of COVID-19 passes was also shown to improve overall vaccination uptake and intention, but not in people who hold strong anti-COVID-19 vaccine beliefs. Conclusion: The evidence from the literature we reviewed tends to indicate positive direct and indirect effects from the use of COVID-19 passes. A major limitation to establishing this firmly is the entanglement of individual effects of multiple measures being implemented simultaneously.SCOPUS: ar.jinfo:eu-repo/semantics/publishe
Fast and efficient joint modelling of multivariate longitudinal data and time-to-event data with a pairwise-fitting approach
No full textIn empirical studies, multiple outcomes are often measured repeatedly over time, and interest frequently lies in studying the association between these longitudinal outcomes and a time-to-event outcome. Therefore, shared-parameter joint models for longitudinal and time-to-event outcomes have been developed. However, while such joint models in theory also allow for multiple longitudinal outcomes, they are often restricted to a limited number of outcomes due to computational complexity when fitting the models. To address this problem, we propose a new joint model, which is based on correlated instead of shared random effects, and for which a pairwise-modelling strategy can be used. In this approach, the longitudinal outcomes are modelled with (generalized) linear mixed models and the survival outcome with a Weibull proportional hazards frailty model. Instead of fitting the full joint model, this approach involves fitting all possible bivariate models, and inference is based on pseudo-likelihood theory. The main advantage of our approach is that there is no restriction on the number of longitudinally measured outcomes that are jointly modelled with the time-to-event outcome.The authors received no financial support for the research, authorship and/or publication of this article
Do health beliefs about COVID‐19 predict morbidity? A longitudinal study
No full textIn a highly powered (N & AP; 5000), six-months longitudinal study (December 2020-May 2021), we tested the assumption that beliefs concerning COVID-19 and the precautions against it predicted morbidity. Six months after having filled out a survey measuring beliefs about the disease and the precautions against it, participants reported if they were or had been ill with COVID-19. A lower likelihood of being or having been ill with COVID-19 was predicted by personal optimism concerning infection, perceived personal control over infection, perceived effectiveness of precautions, and self-reported personal or better-than-average adherence to the precautions. A higher likelihood of being or having been ill with COVID-19 was predicted by perceived personal control over a good outcome of an infection, egocentric impact perception concerning the impact of the disease, perceived difficulty of adherence to the precautions, and both personal and egocentric impact perception concerning the impact of the precautions. Comparative optimism did not predict morbidity, nor did personal optimism concerning severe disease or a good outcome, perceived personal control over severe disease, and moralization of the precautions. We discuss implications for public health communication.The research in this paper was supported by FWO-Grant G0G6620N, awarded to the last four authors and Eliane Deschrijver (UGent & UNSW). We warmly thank Roel Vercammen, Gunther Ackermans, and Lander Van den Eynde for their help in the organization of the data collection
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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