84 citations · 115 across the 12 of their papers we have counts for
8 papers · 1 filter
Evaluating distributional regression strategies for modelling self-reported sexual age-mixing
Timothy M Wolock, Seth R Flaxman, Kathryn A Risher +3
The age dynamics of sexual partnership formation determine patterns of sexually transmitted disease transmission and have long been a focus of researchers studying human immunodefi…
Gaussian Process Nowcasting: Application to COVID-19 Mortality Reporting
Iwona Hawryluk, Henrique Hoeltgebaum, Swapnil Mishra +7
Updating observations of a signal due to the delays in the measurement process is a common problem in signal processing, with prominent examples in a wide range of fields. An impor…
Semi-Mechanistic Bayesian Modeling of COVID-19 with Renewal Processes
Samir Bhatt, Neil Ferguson, Seth Flaxman +3
We propose a general Bayesian approach to modeling epidemics such as COVID-19. The approach grew out of specific analyses conducted during the pandemic, in particular an analysis c…
Referenced Thermodynamic Integration for Bayesian Model Selection: Application to COVID-19 Model Selection
Iwona Hawryluk, Swapnil Mishra, Seth Flaxman +2
Model selection is a fundamental part of the applied Bayesian statistical methodology. Metrics such as the Akaike Information Criterion are commonly used in practice to select mode…
A unified machine learning approach to time series forecasting applied to demand at emergency departments
Michaela A. C. Vollmer, Ben Glampson, Thomas A. Mellan +7
There were 25.6 million attendances at Emergency Departments (EDs) in England in 2019 corresponding to an increase of 12 million attendances over the past ten years. The steadily r…
Scalable Bayesian inference for self-excitatory stochastic processes applied to big American gunfire data
Andrew J. Holbrook, Charles E. Loeffler, Seth R. Flaxman +1
The Hawkes process and its extensions effectively model self-excitatory phenomena including earthquakes, viral pandemics, financial transactions, neural spike trains and the spread…