1,720,969 research outputs found
Spatio-temporal model to investigate COVID-19 spread accounting for the mobility amongst municipalities
No full textThe rapid spread of COVID-19 worldwide led to the implementation of various non-pharmaceutical interventions to limit transmission and hence reduce the number of infections. Using telecom-operator-based mobility data and a spatio-temporal dynamic model, the impact of mobility on the evolution of the pandemic at the level of the 581 Belgian municipalities is investigated. By decomposing incidence, particularly into within- and between-municipality components, we noted that the global epidemic component is relatively more important in larger municipalities (e.g., cities), while the local component is more relevant in smaller (rural) municipalities. Investigation of the effect of mobility on the pandemic spread showed that reduction of mobility has a significant impact in reducing the number of new infections.sponsorship: NH and CF acknowledge support from the European Union's Horizon 2020 research and innovation programme-project EpiPose (Grant agreement number 101003688) . (European Union|101003688)status: Publishe
Development and application of statistical methodology for analysis of the phenomenon of multi‐drug resistance in the EU: demonstration of analytical approaches using antimicrobial resistance isolate‐based data
No full textSince antimicrobial resistance (AMR) has been one of the major public health burdens over the last decade, it is of great importance to appropriately monitor and analyse AMR data. Isolate-based data within the EU have been routinely collected since 2010 and reported to EFSA on a yearly basis. AMR data are collected for several bacterial species, tested for susceptibility against different antimicrobials and minimum inhibitory concentration (MIC) is reported. For analysis purposes, a dichotomised version of the MIC values based on the epidemiological cut-off is used to represent different resistance patterns. This report describes various methods to analyse multi-drug resistance data, including the identification of structure to construct groups of isolates with similar resistance patterns or with similar MIC values. Multivariate classification trees and hierarchical cluster analysis after application of principal components and multiple correspondence analyses are applied aiming at group discovering. Latent class analysis is presented as an alternative model-based approach. The generalised estimating equations method is presented handling univariate and multivariate binary outcomes. Bayesian network analysis provides the user with a graphical representation of the underlying associations in the data to identify new co-resistance patterns. Models that deal with spatial distribution of resistant isolates, in combination with their evolution over time, are constructed for univariate and bivariate outcomes. Finally, pattern and source attributions tools are presented, providing, in addition to exploratory analyses, a logistic model to assess variables influencing certain resistance patterns. Source attribution is used to attribute resistance cases in humans to resistance observed in animal, human food consumption patterns and antimicrobial usage data. For illustration purposes, these methods are applied to a subset of the AMR data using an application developed with the R package “shiny”
Research on the effectiveness of an in-depth training program for schools and parents, aimed at installing a challenging learning environment for gifted children in Belgian Schools
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Research on the effectiveness of an in-depth training program for schools and parents, aimed at installing a challenging learning environment for gifted children in Belgian Schools
No full text- …
