scholarly journals Reinforcement learning and Bayesian data assimilation for model‐informed precision dosing in oncology

2021 ◽  
Author(s):  
Corinna Maier ◽  
Niklas Hartung ◽  
Charlotte Kloft ◽  
Wilhelm Huisinga ◽  
Jana de Wiljes
Keyword(s):  
Reinforcement Learning ◽  
Data Assimilation ◽  
Bayesian Data Assimilation

scholarly journals Bayesian data assimilation provides rapid decision support for vector-borne diseases

2015 ◽  
Vol 12 (108) ◽  
pp. 20150367 ◽  
Author(s):  
Chris P. Jewell ◽  
Richard G. Brown
Keyword(s):  
Decision Support ◽  
Data Assimilation ◽  
Population Data ◽  
Disease Status ◽  
Host Population ◽  
Significant Advance ◽  
Model Parameters ◽  
Vector Borne Diseases ◽  
Vector Borne ◽  
Bayesian Data Assimilation

Predicting the spread of vector-borne diseases in response to incursions requires knowledge of both host and vector demographics in advance of an outbreak. Although host population data are typically available, for novel disease introductions there is a high chance of the pathogen using a vector for which data are unavailable. This presents a barrier to estimating the parameters of dynamical models representing host–vector–pathogen interaction, and hence limits their ability to provide quantitative risk forecasts. The Theileria orientalis (Ikeda) outbreak in New Zealand cattle demonstrates this problem: even though the vector has received extensive laboratory study, a high degree of uncertainty persists over its national demographic distribution. Addressing this, we develop a Bayesian data assimilation approach whereby indirect observations of vector activity inform a seasonal spatio-temporal risk surface within a stochastic epidemic model. We provide quantitative predictions for the future spread of the epidemic, quantifying uncertainty in the model parameters, case infection times and the disease status of undetected infections. Importantly, we demonstrate how our model learns sequentially as the epidemic unfolds and provide evidence for changing epidemic dynamics through time. Our approach therefore provides a significant advance in rapid decision support for novel vector-borne disease outbreaks.

scholarly journals Bayesian data assimilation in shape registration

2013 ◽  
Vol 29 (4) ◽  
pp. 045011 ◽  
Author(s):  
C J Cotter ◽  
S L Cotter ◽  
F-X Vialard
Keyword(s):  
Data Assimilation ◽  
Shape Registration ◽  
Bayesian Data Assimilation

scholarly journals Estimation of land-use change using a Bayesian data assimilation approach

2017 ◽  
Author(s):  
Peter Levy ◽  
Marcel Van Oijen ◽  
Gwen Buys ◽  
Sam Tomlinson
Keyword(s):  
Land Use ◽  
Data Assimilation ◽  
Land Use Change ◽  
Census Data ◽  
Likelihood Function ◽  
Skewed Distribution ◽  
Data Sources ◽  
Modelling Uncertainty ◽  
Bayesian Data Assimilation ◽  
Posterior Estimate

Abstract. We present a method for estimating land-use change using a Bayesian data assimilation approach. The approach provides a general framework for combining multiple disparate data sources with a simple model. This allows us to constrain estimates of gross land-use change with reliable national-scale census data, whilst retaining the detailed information available from several other sources. Eight different data sources, with three different data structures, were combined in our posterior estimate of land-use and land-use change, and other data sources could easily be added in future. The tendency for observations to underestimate gross land-use change is accounted for by allowing for a skewed distribution in the likelihood function. The data structure produced has high temporal and spatial resolution, and is appropriate for dynamic process-based modelling. Uncertainty is propagated appropriately into the output, so we have a full posterior distribution of output and parameters. The data are available in the widely used netCDF file format from http://eidc.ceh.ac.uk/ (doi pending).

scholarly journals Data assimilation as a deep learning tool to infer ODE representations of dynamical models

2019 ◽  
Author(s):  
Marc Bocquet ◽  
Julien Brajard ◽  
Alberto Carrassi ◽  
Laurent Bertino
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Assimilation ◽  
Physical Model ◽  
High Dimensional ◽  
Learning Techniques ◽  
Dimensional Models ◽  
Using Data ◽  
Learning Software ◽  
Bayesian Data Assimilation

Abstract. Recent progress in machine learning has shown how to forecast and, to some extent, learn the dynamics of a model from its output, resorting in particular to neural networks and deep learning techniques. We will show how the same goal can be directly achieved using data assimilation techniques without leveraging on machine learning software libraries, with a view to high-dimensional models. The dynamics of a model are learned from its observation and an ordinary differential equation (ODE) representation of this model is inferred using a recursive nonlinear regression. Because the method is embedded in a Bayesian data assimilation framework, it can learn from partial and noisy observations of a state trajectory of the physical model. Moreover, a space-wise local representation of the ODE system is introduced and is key to cope with high-dimensional models. It has recently been suggested that neural network architectures could be interpreted as dynamical systems. Reciprocally, we show that our ODE representations are reminiscent of deep learning architectures. Furthermore, numerical analysis considerations on stability shed light on the assets and limitations of the method. The method is illustrated on several chaotic discrete and continuous models of various dimensions, with or without noisy observations, with the goal to identify or improve the model dynamics, build a surrogate or reduced model, or produce forecasts from mere observations of the physical model.

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