Using Dynamic Bayesian Networks for Agent-Based Modelling: Application in Tuberculosis Control
Date Issued
2014
Date
2014
Author(s)
Ku, Chu-Chang
Abstract
The simulation models in epidemiology were developed to answer the questions which were not easy to solve by observational studies in the real world.
In particular, Agent-based models (ABMs) were usually employed to deal with the complex system of disease transmission by simulating computational agents in the virtual world.
However, the fitting scheme of ABMs is less developed than the applications..
With the aim of investigating disease dynamics and creating an interface for statistical analysis, we proposed a class of ABMs with Continuous-time Bayesian network, a temporal multivariate probability model.
While retaining the strength of existing procedure for simulation model fitting based on sequential Monte Carlo, we set up an improved framework for fitting ABMs.
We further synthesized the numerical mutation in genetic algorithm and the parameters augmentation in blocking Gibbs sampling in order to overcome the challenges of multidimensional parameters and multi-sources data.
Using an example of Susceptible-Infectious-Recovery model for contact tracing in tuberculosis control, we briefly presented the properties of our proposed model and demonstrated its potential applications in the future.
By including model construction, fitting, and forecasting, we formalized an empirical scheme for individual based models in simulating disease dynamics.
Subjects
個體化模擬模型
傳染病數理模型
動態貝氏網路
數值突變
肺結核
接觸者追蹤
SDGs
Type
thesis
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