1,721,390 research outputs found
On the choice of the mesh for the analysis of geostatistical data using R-INLA
No full textMany methods used in spatial statistics are computationally demanding, and so, the development of more computationally efficient methods has received attention. A important development is the integrated nested Laplace approximation method which is carry out Bayesian analysis more efficiently This method, for geostatistical data, is done considering the SPDE approach that requires the creation of a mesh overlying the study area and all the obtained results depend on it. The impact of the mesh on inference and prediction is investigated through simulations. As there is no formal procedure to specify it, we investigate a guideline to create an optimal mesh.The first author acknowledge the financial support of the "Ciencia sem Fronteiras" program of CNPq (Brazil) under the process number 200573/2015-2. Support from the IAP Research Network P7/06 of the Belgian State (Belgian Science Policy) is also gratefully acknowledged by the second and third author.Ribeiro, PJ (reprint author), Univ Sao Paulo, Dept Ciencias Exatas, BR-13418900 Piracicaba, SP, Brazil.
[email protected]
Spatial risk assessment of traffic safety on urban roads
No full textBecause road accidents are still one of the leading causes of death in the world, traffic safety has become a hot topic for policy makers, for the media as well as for academics. One of the main policy aims within this domain is to produce safer roads and, consequently, to reduce the number of (fatal) accidents. In order to make the step from evidence to policy, one needs to make an inventory of the hazardous locations and try to understand the adhering risk on these roads
Spatio-temporal Bayesian models for malaria risk using survey and health facility routine data in Rwanda
No full text
Spatial risk assessment of traffic safety on urban roads
No full textBecause road accidents are still one of the leading causes of death in the world, traffic safety has become a hot topic for policy makers, for the media as well as for academics. One of the main policy aims within this domain is to produce safer roads and, consequently, to reduce the number of (fatal) accidents. In order to make the step from evidence to policy, one needs to make an inventory of the hazardous locations and try to understand the adhering risk on these roads
Bayesian Nowcasting with Laplacian-P-Splines
No full textDuring an epidemic, the daily number of reported infected cases, deaths or hospitalizations is often lower than the actual number due to reporting delays. Nowcasting aims to estimate the cases that have not yet been reported and combine it with the already reported cases to obtain an estimate of the daily cases. In this article, we present a fast and flexible Bayesian approach for nowcasting by combining P-splines and Laplace approximations. Laplacian-P-splines provide a flexible framework for nowcasting that is computationally less demanding as compared to traditional Markov chain Monte Carlo techniques. The proposed approach also permits to naturally quantify the prediction uncertainty. Model performance is assessed through simulations and the nowcasting method is applied to COVID-19 mortality and incidence cases in Belgium. Supplementary materials for this article are available online.VERDI: This project was supported by the VERDI project (101045989), funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the Health and Digital Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.
ESCAPE: This project was supported by the ESCAPE project (101095619), funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Health and Digital Executive Agency (HADEA). Neither the European Union nor the granting authority can be held responsible for them. The authors acknowledge funding from the Special Research Fund through the Methusalem project BOF22M01
Bayesian multi-scale modeling for aggregated disease mapping data
No full textIn disease mapping, a scale effect due to an aggregation of data from a finer resolution level to a coarser level is a common phenomenon. This article addresses this issue using a hierarchical Bayesian modeling framework. We propose four different multiscale models. The first two models use a shared random effect that the finer level inherits from the coarser level. The third model assumes two independent convolution models at the finer and coarser levels. The fourth model applies a convolution model at the finer level, but the relative risk at the coarser level is obtained by aggregating the estimates at the finer level. We compare the models using the deviance information criterion (DIC) and Watanabe-Akaike information criterion (WAIC) that are applied to real and simulated data. The results indicate that the models with shared random effects outperform the other models on a range of criteria.The authors would like to acknowledge support from the National Institutes of Health via grant
R01CA172805. The third author also acknowledges support from the IAP Research Network P7/06 of the
Belgian State (Belgian Science Policy)
Geospatial patterns of excess mortality in Belgium: Insights from the first year of the COVID-19 pandemic
No full textObjectives: Belgium experienced multiple COVID-19 waves that hit various groups in the population, which changed the mortality pattern compared to periods before the pandemic. In this study, we investigated the geographical excess mortality trend in Belgium during the first year of the COVID-19 pandemic. Methods: We retrieved the number of deaths and population data in 2020 based on gender, age, and municipality of residence, and we made a comparison with the mortality data in 2017–2019 using a spatially discrete model. Results: Excess mortality was significantly associated with age, gender, and COVID-19 incidence, with larger effects in the second half of 2020. Most municipalities had higher risks of mortality with a number of exceptions in the northeastern part of Belgium. Some discrepancies in excess mortality were observed between the north and south regions. Conclusions: This study offers useful insight into excess mortality and will aid local and regional authorities in monitoring mortality trends.Funding T.N. gratefully acknowledges funding by the Internal Funds KU Leuven, Belgium (project number 3M190682) and the Fund for Scientific Research– Flanders, Belgium (grant number G0A4121N). C.F. acknowledges support from the European Union’s Horizon 2020project EpiPose (Grant agreement number 101003688) and European Union’s Horizon Europe– project ESCAPE (Grant agreement number 101095619). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Health and Digital Executive Agency (HADEA). Neither the European Union nor the granting authority can be held responsible for them
Spatio-temporal dynamic of the COVID-19 epidemic and the impact of imported cases in Rwanda
No full textIntroductionAfrica was threatened by the coronavirus disease 2019 (COVID-19) due to the limited health care infrastructure. Rwanda has consistently used non-pharmaceutical strategies, such as lockdown, curfew, and enforcement of prevention measures to control the spread of COVID-19. Despite the mitigation measures taken, the country has faced a series of outbreaks in 2020 and 2021.In this paper, we investigate the nature of epidemic phenomena in Rwanda and the impact of imported cases on the spread of COVID-19 using endemic-epidemic spatio-temporal models. Our study provides a framework for understanding the dynamics of the epidemic in Rwanda and monitoring its phenomena to inform public health decision-makers for timely and targeted interventions.ResultsThe findings provide insights into the effects of lockdown and imported infections in Rwanda's COVID-19 outbreaks. The findings showed that imported infections are dominated by locally transmitted cases. The high incidence was predominant in urban areas and at the borders of Rwanda with its neighboring countries. The inter-district spread of COVID-19 was very limited due to mitigation measures taken in Rwanda.ConclusionThe study recommends using evidence-based decisions in the management of epidemics and integrating statistical models in the analytics component of the health information system.The last author (CF) received funding from the European Union’s Horizon 2020 research and innovation programme - project EpiPose (No. 101003688
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