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06/09/2023
Leaping through tree space: continuous phylogenetic inference for rooted and unrooted trees
Phylogenetics is now fundamental in life sciences, providing insights in...
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05/31/2023
Deep learning and MCMC with aggVAE for shifting administrative boundaries: mapping malaria prevalence in Kenya
Model-based disease mapping remains a fundamental policy-informing tool ...
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05/01/2023
A comparison of short-term probabilistic forecasts for the incidence of COVID-19 using mechanistic and statistical time series models
Short-term forecasts of infectious disease spread are a critical compone...
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04/25/2023
Phylo2Vec: a vector representation for binary trees
Binary phylogenetic trees inferred from biological data are central to u...
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10/31/2022
The interaction of transmission intensity, mortality, and the economy: a retrospective analysis of the COVID-19 pandemic
The COVID-19 pandemic has caused over 6.4 million registered deaths to d...
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10/25/2022
The uncertainty of infectious disease outbreaks is underestimated
Uncertainty can be classified as either aleatoric (intrinsic randomness)...
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10/21/2022
Cox-Hawkes: doubly stochastic spatiotemporal Poisson processes
Hawkes processes are point process models that have been used to capture...
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10/20/2022
Estimating fine age structure and time trends in human contact patterns from coarse contact data: the Bayesian rate consistency model
Since the emergence of severe acute respiratory syndrome coronavirus 2 (...
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09/20/2022
Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference
Epidemic models are powerful tools in understanding infectious disease. ...
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10/24/2021
Epidemia: An R Package for Semi-Mechanistic Bayesian Modelling of Infectious Diseases using Point Processes
This article introduces epidemia, an R package for Bayesian, regression-...
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10/20/2021
Encoding spatiotemporal priors with VAEs for small-area estimation
Gaussian processes (GPs), implemented through multivariate Gaussian dist...
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07/12/2021
Unifying the effective reproduction number, incidence, and prevalence under a stochastic age-dependent branching process
Renewal equations are a popular approach used in modelling the number of...
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06/23/2021
Regularised B-splines projected Gaussian Process priors to estimate the age profile of COVID-19 deaths before and after vaccine roll-out
The COVID-19 pandemic has caused severe public health consequences in th...
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03/24/2021
Pyfectious: An individual-level simulator to discover optimal containment polices for epidemic diseases
Simulating the spread of infectious diseases in human communities is cri...
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02/22/2021
Gaussian Process Nowcasting: Application to COVID-19 Mortality Reporting
Updating observations of a signal due to the delays in the measurement p...
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12/01/2020
Semi-Mechanistic Bayesian Modeling of COVID-19 with Renewal Processes
We propose a general Bayesian approach to modeling epidemics such as COV...
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09/08/2020
Simulating normalising constants with referenced thermodynamic integration: application to COVID-19 model selection
Model selection is a fundamental part of Bayesian statistical inference;...
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07/13/2020
A unified machine learning approach to time series forecasting applied to demand at emergency departments
There were 25.6 million attendances at Emergency Departments (EDs) in En...
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06/30/2020
On the derivation of the renewal equation from an age-dependent branching process: an epidemic modelling perspective
Renewal processes are a popular approach used in modelling infectious di...
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06/16/2020
A joint bayesian space-time model to integrate spatially misaligned air pollution data in R-INLA
In air pollution studies, dispersion models provide estimates of concent...
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04/23/2020
Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in European countries: technical description update
Following the emergence of a novel coronavirus (SARS-CoV-2) and its spre...
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02/17/2020
Ï€VAE: Encoding stochastic process priors with variational autoencoders
Stochastic processes provide a mathematically elegant way model complex ...
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02/22/2019
Spatial Analysis Made Easy with Linear Regression and Kernels
Kernel methods are an incredibly popular technique for extending linear ...
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10/30/2018
Stochastic Optimal Control of Epidemic Processes in Networks
We approach the development of models and control strategies of suscepti...
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11/15/2017