27 citations · 62 across the 12 of their papers we have counts for
12 papers
A Bayesian Multisource Fusion Model for Spatiotemporal PM2.5 in an Urban Setting
Abi I. Riley, Marta Blangiardo, Frédéric B. Piel +5
Airborne particulate matter (PM2.5) is a major public health concern in urban environments, where population density and emission sources exacerbate exposure risks. We present a no…
A Bayesian hierarchical small-area population model accounting for data source specific methodologies from American Community Survey, Population Estimates Program, and Decennial Census data
Emily N Peterson, Rachel C Nethery, Tullia Padellini +6
Small area estimates of population are necessary for many epidemiological studies, yet their quality and accuracy are often not assessed. In the United States, small area estimates…
Interoperability of statistical models in pandemic preparedness: principles and reality
George Nicholson, Marta Blangiardo, Mark Briers +12
We present "interoperability" as a guiding framework for statistical modelling to assist policy makers asking multiple questions using diverse datasets in the face of an evolving p…
School neighbourhood and compliance with WHO-recommended annual NO2 guideline: a case study of Greater London
Niloofar Shoari, Shahram Heydari, Marta Blangiardo
Despite several national and local policies towards cleaner air in England, many schools in London breach the WHO-recommended concentrations of air pollutants such as NO2 and PM2.5…
A joint bayesian space-time model to integrate spatially misaligned air pollution data in R-INLA
Chiara Forlani, Samir Bhatt, Michela Cameletti +2
In air pollution studies, dispersion models provide estimates of concentration at grid level covering the entire spatial domain, and are then calibrated against measurements from m…
A spatio-temporal model to understand forest fires causality in Europe
Oscar Rodriguez de Rivera, Antonio López-Quílez, Marta Blangiardo +1
Forest fires are the outcome of a complex interaction between environmental factors, topography and socioeconomic factors (Bedia et al, 2014). Therefore, understand causality and e…
Missing data analysis and imputation via latent Gaussian Markov random fields
Virgilio Gómez-Rubio, Michela Cameletti, Marta Blangiardo
In this paper we recast the problem of missing values in the covariates of a regression model as a latent Gaussian Markov random field (GMRF) model in a fully Bayesian framework. O…
A hierarchical modelling approach to assess multi pollutant effects in time-series studies
Marta Blangiardo, Monica Pirani, Lauren Kanapka +2
When assessing the short term effect of air pollution on health outcomes, it is common practice to consider one pollutant at a time, due to their high correlation. Multi pollutant…
A Bayesian Approach to Modelling Fine-Scale Spatial Dynamics of Non-State Terrorism: World Study, 2002-2013
André Python, Janine Illian, Charlotte Jones-Todd +1
To this day, terrorism persists as a worldwide threat, as exemplified by the ongoing lethal attacks perpetrated by ISIS in Iraq, Syria, Al Qaeda in Yemen, and Boko Haram in Nigeria…
Using Ecological Propensity Score to Adjust for Missing Confounders in Small Area Studies
Yingbo Wang, Sylvia Richardson, Anna Hansell +1
Small area ecological studies are commonly used in epidemiology to assess the impact of area level risk factors on health outcomes when data are only available in an aggregated for…
Modelling collinear and spatially correlated data
Silvia Liverani, Aurore Lavigne, Marta Blangiardo
In this work we present a statistical approach to distinguish and interpret the complex relationship between several predictors and a response variable at the small area level, in…
Evidence of bias in the Eurovision song contest: modelling the votes using Bayesian hierarchical models
Marta Blangiardo, Gianluca Baio
The Eurovision Song Contest is an annual musical competition held among active members of the European Broadcasting Union since 1956. The event is televised live across Europe. Eac…