The use of epidemic modelling in connection with spread of diseases plays an important role in understanding dynamics and providing forecasts for informed analysis and decision-making. In this regard, it is crucial to quantify the effects of uncertainty in the modelling and in model-based predictions to trustfully communicate results and limitations. We propose to do efficient uncertainty quantification in compartmental epidemic models using the generalized Polynomial Chaos (gPC) framework. This framework uses a suitable polynomial basis that can be tailored to the underlying distribution for the parameter uncertainty to do forward propagation through efficient sampling via a mathematical model to quantify the effect on the output. By evalu...
When applying models to patient-specific situations, the impact of model input uncertainty on the mo...
The basic reproduction number, simply denoted by $R_0$, plays a fundamental role in the analysis of ...
When applying models to patient-specific situations, the impact of model input uncertainty on the mo...
The use of epidemic modelling in connection with spread of diseases plays an important role in under...
Mathematical models based on ordinary differential equations are a useful tool to study the processe...
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, p...
Population dynamics models consisting of nonlinear difference equations allow us to get a better und...
[EN] Population dynamics models consisting of nonlinear difference equations allow us to get a bette...
AbstractUncertainty in the model input parameters are to be taken into account in order to assess th...
This work was supported by EPSRC, United Kingdom grant no. EP/R014604/1. RR was funded by STFC, Unit...
Uncertainties are ubiquitous and unavoidable in process design and modeling. Because they can signif...
Thesis (Ph.D.)--University of Washington, 2020Uncertainties exist in both physics-based and data-dri...
DDA, JB and PB are funded by MRC (Unit Programme number MC/UU/00002/11); DDA is also supported by th...
There are many different models to help predict the likely course an epidemic will take. However, th...
The modern world features a plethora of social, technological and biological epidemic phenomena. The...
When applying models to patient-specific situations, the impact of model input uncertainty on the mo...
The basic reproduction number, simply denoted by $R_0$, plays a fundamental role in the analysis of ...
When applying models to patient-specific situations, the impact of model input uncertainty on the mo...
The use of epidemic modelling in connection with spread of diseases plays an important role in under...
Mathematical models based on ordinary differential equations are a useful tool to study the processe...
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, p...
Population dynamics models consisting of nonlinear difference equations allow us to get a better und...
[EN] Population dynamics models consisting of nonlinear difference equations allow us to get a bette...
AbstractUncertainty in the model input parameters are to be taken into account in order to assess th...
This work was supported by EPSRC, United Kingdom grant no. EP/R014604/1. RR was funded by STFC, Unit...
Uncertainties are ubiquitous and unavoidable in process design and modeling. Because they can signif...
Thesis (Ph.D.)--University of Washington, 2020Uncertainties exist in both physics-based and data-dri...
DDA, JB and PB are funded by MRC (Unit Programme number MC/UU/00002/11); DDA is also supported by th...
There are many different models to help predict the likely course an epidemic will take. However, th...
The modern world features a plethora of social, technological and biological epidemic phenomena. The...
When applying models to patient-specific situations, the impact of model input uncertainty on the mo...
The basic reproduction number, simply denoted by $R_0$, plays a fundamental role in the analysis of ...
When applying models to patient-specific situations, the impact of model input uncertainty on the mo...