Practical heteroscedastic Gaussian process modeling for large simulation experiments
We present a unified view of likelihood based Gaussian progress regression for simulation
experiments exhibiting input-dependent noise. Replication plays an important role in that …
experiments exhibiting input-dependent noise. Replication plays an important role in that …
[HTML][HTML] Optimal timing of one-shot interventions for epidemic control
The interventions and outcomes in the ongoing COVID-19 pandemic are highly varied. The
disease and the interventions both impose costs and harm on society. Some interventions …
disease and the interventions both impose costs and harm on society. Some interventions …
Tracking epidemics with Google flu trends data and a state-space SEIR model
In this article, we use Google Flu Trends data together with a sequential surveillance model
based on state-space methodology to track the evolution of an epidemic process over time …
based on state-space methodology to track the evolution of an epidemic process over time …
A review on quantile regression for stochastic computer experiments
L Torossian, V Picheny, R Faivre, A Garivier - Reliability Engineering & …, 2020 - Elsevier
We report on an empirical study of the main strategies for quantile regression in the context
of stochastic computer experiments. To ensure adequate diversity, six metamodels are …
of stochastic computer experiments. To ensure adequate diversity, six metamodels are …
Multi-objective model-based reinforcement learning for infectious disease control
Severe infectious diseases such as the novel coronavirus (COVID-19) pose a huge threat to
public health. Stringent control measures, such as school closures and stay-at-home orders …
public health. Stringent control measures, such as school closures and stay-at-home orders …
[HTML][HTML] Control fast or control smart: When should invading pathogens be controlled?
RN Thompson, CA Gilligan… - PLoS computational …, 2018 - journals.plos.org
The intuitive response to an invading pathogen is to start disease management as rapidly as
possible, since this would be expected to minimise the future impacts of disease. However …
possible, since this would be expected to minimise the future impacts of disease. However …
Generalized Markov models of infectious disease spread: A novel framework for developing dynamic health policies
R Yaesoubi, T Cohen - European journal of operational research, 2011 - Elsevier
We propose a class of mathematical models for the transmission of infectious diseases in
large populations. This class of models, which generalizes the existing discrete-time Markov …
large populations. This class of models, which generalizes the existing discrete-time Markov …
[HTML][HTML] Phenomenological forecasting of disease incidence using heteroskedastic Gaussian processes: A dengue case study
In 2015 the US federal government sponsored a dengue forecasting competition using
historical case data from Iquitos, Peru and San Juan, Puerto Rico. Competitors were …
historical case data from Iquitos, Peru and San Juan, Puerto Rico. Competitors were …
Context matters: using reinforcement learning to develop human-readable, state-dependent outbreak response policies
WJM Probert, S Lakkur… - … of the Royal …, 2019 - royalsocietypublishing.org
The number of all possible epidemics of a given infectious disease that could occur on a
given landscape is large for systems of real-world complexity. Furthermore, there is no …
given landscape is large for systems of real-world complexity. Furthermore, there is no …
[HTML][HTML] Dynamic health policies for controlling the spread of emerging infections: influenza as an example
R Yaesoubi, T Cohen - PloS one, 2011 - journals.plos.org
The recent appearance and spread of novel infectious pathogens provide motivation for
using models as tools to guide public health decision-making. Here we describe a modeling …
using models as tools to guide public health decision-making. Here we describe a modeling …