133 citations · 349 across the 11 of their papers we have counts for
11 papers
A Bayesian modelling framework to quantify multiple sources of spatial variation for disease mapping
Sophie A Lee, Theodoros Economou, Rachel Lowe
Spatial connectivity is an important consideration when modelling infectious disease data across a geographical region. Connectivity can arise for many reasons, including shared ch…
A deep mixture density network for outlier-corrected interpolation of crowd-sourced weather data
Charlie Kirkwood, Theo Economou, Henry Odbert +1
As the costs of sensors and associated IT infrastructure decreases - as exemplified by the Internet of Things - increasing volumes of observational data are becoming available for…
Bayesian deep learning for mapping via auxiliary information: a new era for geostatistics?
Charlie Kirkwood, Theo Economou, Nicolas Pugeault
For geospatial modelling and mapping tasks, variants of kriging - the spatial interpolation technique developed by South African mining engineer Danie Krige - have long been regard…
A framework for probabilistic weather forecast post-processing across models and lead times using machine learning
Charlie Kirkwood, Theo Economou, Henry Odbert +1
Forecasting the weather is an increasingly data intensive exercise. Numerical Weather Prediction (NWP) models are becoming more complex, with higher resolutions, and there are incr…
A Powerful Modelling Framework for Nowcasting and Forecasting COVID-19 and Other Diseases
Oliver Stoner, Theo Economou, Alba Halliday
The COVID-19 pandemic has highlighted delayed reporting as a significant impediment to effective disease surveillance and decision-making. In the absence of timely data, statistica…
An Advanced Hidden Markov Model for Hourly Rainfall Time Series
Oliver Stoner, Theo Economou
For hydrological applications, such as urban flood modelling, it is often important to be able to simulate sub-daily rainfall time series from stochastic models. However, modelling…
Multivariate Hierarchical Frameworks for Modelling Delayed Reporting in Count Data
Oliver Stoner, Theo Economou
In many fields and applications count data can be subject to delayed reporting. This is where the total count, such as the number of disease cases contracted in a given week, may n…
Global Household Energy Model: A Multivariate Hierarchical Approach to Estimating Trends in the Use of Polluting and Clean Fuels for Cooking
Oliver Stoner, Gavin Shaddick, Theo Economou +5
In 2017 an estimated 3 billion people used polluting fuels and technologies as their primary cooking solution, with 3.8 million deaths annually attributed to household exposure to…
A Hierarchical Framework for Correcting Under-Reporting in Count Data
Oliver Stoner, Theo Economou, Gabriela Drummond
Tuberculosis poses a global health risk and Brazil is among the top twenty countries by absolute mortality. However, this epidemiological burden is masked by under-reporting, which…
Modelling reporting delays for disease surveillance data
Leonardo Bastos, Theodoros Economou, Marcelo Gomes +6
One difficulty for real-time tracking of epidemics is related to reporting delay. The reporting delay may be due to laboratory confirmation, logistic problems, infrastructure diffi…
Spatio-temporal modelling of extreme storms
Theodoros Economou, David B. Stephenson, Christopher A. T. Ferro
A flexible spatio-temporal model is implemented to analyse extreme extra-tropical cyclones objectively identified over the Atlantic and Europe in 6-hourly re-analyses from 1979-200…