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 …
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
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 …
Control fast or control smart: When should invading pathogens be controlled?
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 develo** dynamic health policies
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 …
Dynamic health policies for controlling the spread of emerging infections: influenza as an example
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 …