Topics in Bayesian Inference and Model Assessment for Partially Observed Stochastic Epidemic Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Topics in Bayesian Inference and Model Assessment for Partially Observed Stochastic Epidemic Models write by Georgios Aristotelous. This book was released on 2020. Topics in Bayesian Inference and Model Assessment for Partially Observed Stochastic Epidemic Models available in PDF, EPUB and Kindle.
Bayesian Inference and Model Selection for Partially Observed Stochastic Epidemics
Bayesian Inference and Model Selection for Partially Observed Stochastic Epidemics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Bayesian Inference and Model Selection for Partially Observed Stochastic Epidemics write by Panayiota Touloupou. This book was released on 2016. Bayesian Inference and Model Selection for Partially Observed Stochastic Epidemics available in PDF, EPUB and Kindle.
Bayesian Inference for Partially Observed Stochastic Epidemics
Bayesian Inference for Partially Observed Stochastic Epidemics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Bayesian Inference for Partially Observed Stochastic Epidemics write by P. D. O'Neill. This book was released on 1997. Bayesian Inference for Partially Observed Stochastic Epidemics available in PDF, EPUB and Kindle.
Efficient Bayesian inference for partially observed stochastic epidemics and a new class of semi-parametric time series models
Efficient Bayesian inference for partially observed stochastic epidemics and a new class of semi-parametric time series models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Efficient Bayesian inference for partially observed stochastic epidemics and a new class of semi-parametric time series models write by Theodore Kypraios. This book was released on 2007. Efficient Bayesian inference for partially observed stochastic epidemics and a new class of semi-parametric time series models available in PDF, EPUB and Kindle.
Bayesian Modeling of Partially Observed Epidemic Count Data
Bayesian Modeling of Partially Observed Epidemic Count Data - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Bayesian Modeling of Partially Observed Epidemic Count Data write by Jonathan Fintzi. This book was released on 2018. Bayesian Modeling of Partially Observed Epidemic Count Data available in PDF, EPUB and Kindle. Epidemic count data reported by public health surveillance systems reflect the incidence or prevalence of an infectious agent as it spreads through a population. They are a primary source of information for shaping response strategies and for predicting how an outbreak will evolve. Incidence and prevalence counts are often the only source of information about historical outbreaks, or outbreaks in resource limited settings, which are of interest for researchers seeking to develop an understanding of disease transmission during ``peace time", with an eye on preparing for future outbreaks. The absence of subject--level information and the systematic underreporting of cases complicate the task of disentangling whether the data arose from a severe outbreak, observed with low fidelity, or a mild outbreak were most cases were detected. The magnitude of the missing data and the high dimensional state space of the latent epidemic process present challenges for fitting epidemic models that appropriately quantify the stochastic aspects of the transmission dynamics. In this dissertation, we develop computational algorithms for fitting stochastic epidemic models to partially observed incidence and prevalence data. Our algorithms are not specific to particular model dynamics, but rather apply to a broad class of commonly used stochastic epidemic models, including models that allow for time--inhomogeneous transmission dynamics. We use our methods to analyze data from an outbreak of influenza in a British boarding school, the 2014--2015 outbreak of Ebola in West Africa, and the 2009--2011 A(H1N1) influenza pandemic in Finland.
