46 citations · 72 across the 6 of their papers we have counts for
6 papers
Predicting Malaria Incidence Using Artifical Neural Networks and Disaggregation Regression
Jack A. Hall, Tim C. D. Lucas
Disaggregation modelling is a method of predicting disease risk at high resolution using aggregated response data. High resolution disease mapping is an important public health too…
A simulation study of disaggregation regression for spatial disease mapping
Rohan Arambepola, Tim C D Lucas, Anita K Nandi +2
Disaggregation regression has become an important tool in spatial disease mapping for making fine-scale predictions of disease risk from aggregated response data. By including high…
Graphical outputs and Spatial Cross-validation for the R-INLA package using INLAutils
Tim Lucas, Andre Python, David Redding
Statistical analyses proceed by an iterative process of model fitting and checking. The R-INLA package facilitates this iteration by fitting many Bayesian models much faster than a…
disaggregation: An R Package for Bayesian Spatial Disaggregation Modelling
Anita K. Nandi, Tim C. D. Lucas, Rohan Arambepola +2
Disaggregation modelling, or downscaling, has become an important discipline in epidemiology. Surveillance data, aggregated over large regions, is becoming more common, leading to…
Mapping malaria seasonality: a case study from Madagascar
Michele Nguyen, Rosalind E. Howes, Tim C. D. Lucas +17
Many malaria-endemic areas experience seasonal fluctuations in case incidence as Anopheles mosquito and Plasmodium parasite life cycles respond to changing environmental conditions…
Variational Learning on Aggregate Outputs with Gaussian Processes
Ho Chung Leon Law, Dino Sejdinovic, Ewan Cameron +4
While a typical supervised learning framework assumes that the inputs and the outputs are measured at the same levels of granularity, many applications, including global mapping of…