Evaluating the Accuracy of Sampling-Based Approaches to the Calculation of Posterior Moments

Geweke, John

doi:10.21034/sr.148

Cited by 2,109 publications

(1,954 citation statements)

References 18 publications

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“…School closure during a pandemic has been discussed in modelling studies, 6,10,13 in epidemiological studies, 12,[14][15][16][17][18][19] and in work focusing more on economical, social, ethical, and public health features of the policy. 7,[20][21][22][23][24][25][26][27] There is now a need to take a multidisciplinary and holistic perspective and review the multiple aspects of school closure as a public health policy in a comprehensive way, and to discuss the implications in the context of the current H1N1 pandemic.…”

Section: Introductionmentioning

confidence: 99%

Closure of schools during an influenza pandemic

Cauchemez

Ferguson

Wachtel

et al. 2009

The Lancet Infectious Diseases

497

486

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In response to WHO raising the influenza pandemic alert level from phase five to phase six, health officials around the world are carefully reviewing pandemic mitigation protocols. School closure (also called class dismissal in North America) is a non-pharmaceutical intervention that is commonly suggested for mitigating influenza pandemics. Health officials taking the decision to close schools must weigh the potential health benefits of reducing transmission and thus case numbers against high economic and social costs, difficult ethical issues, and the possible disruption of key services such as health care. Also, if schools are expected to close as a deliberate policy option, or just because of high levels of staff absenteeism, it is important to plan to mitigate the negative features of closure. In this context, there is still debate about if, when, and how school closure policy should be used. In this Review, we take a multidisciplinary and holistic perspective and review the multiple aspects of school closure as a public health policy. Implications for the mitigation of the swine-origin influenza A H1N1 pandemic are also discussed.

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Section: Introductionmentioning

confidence: 99%

Closure of schools during an influenza pandemic

Cauchemez

Ferguson

Wachtel

et al. 2009

The Lancet Infectious Diseases

497

486

View full text Add to dashboard Cite

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“…Hence, four different management scenarios were modelled: -June 15 information state, adjusted for age-specific confirmation bias -June 15 information state, unadjusted for confirmation bias -July 15 information state, adjusted for age-specific confirmation bias -July 15 information state, unadjusted for confirmation bias All models were fitted using PyMC 2.3 [35], a software package for the Python programming language that fits Bayesian statistical models using Markov chain Monte Carlo [36] sampling. Each model was sampled for 50 000 iterations using a Metropolis -Hastings sampling algorithm, with the first 40 000 samples discarded conservatively as a warmup period, and the remaining sample was assessed for lack of convergence using the Geweke diagnostic [37]. Hence, all inference was based on the final 10 000 samples from each model run.…”

Section: Model Fittingmentioning

confidence: 99%

Measles outbreak response decision-making under uncertainty: a retrospective analysis

Fonnesbeck

Shea

Carran

et al. 2018

J. R. Soc. Interface.

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Resurgent outbreaks of vaccine-preventable diseases that have previously been controlled or eliminated have been observed in many settings. Reactive vaccination campaigns may successfully control outbreaks but must necessarily be implemented in the face of considerable uncertainty. Real-time surveillance may provide critical information about at-risk population and optimal vaccination targets, but may itself be limited by the specificity of disease confirmation. We propose an integrated modelling approach that synthesizes historical demographic and vaccination data with real-time outbreak surveillance via a dynamic transmission model and an age-specific disease confirmation model. We apply this framework to data from the 1996-1997 measles outbreak in São Paulo, Brazil. To simulate the information available to decision-makers, we truncated the surveillance data to what would have been available at 1 or 2 months prior to the realized interventions. We use the model, fitted to real-time observations, to evaluate the likelihood that candidate age-targeted interventions could control the outbreak. Using only data available prior to the interventions, we estimate that a significant excess of susceptible adults would prevent child-targeted campaigns from controlling the outbreak and that failing to account for age-specific confirmation rates would underestimate the importance of adult-targeted vaccination.

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“…The simulation was run for 100, 000 iterations, of which the first 40, 000 were discarded and the others were thinned by an interval of 3 to reduce sample autocorrelation. We applied Geweke (1992), Heidelberger-Welch (Heidelberger and Welch, 1983), and Raftery-Lewis (Raftery and Lewis, 1995) tests to detect lack of (marginal) convergence.…”

Section: Markov Chain Monte Carlo Implementationmentioning

confidence: 99%

A probabilistic network for the diagnosis of acute cardiopulmonary diseases

2017

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In this paper, the development of a probabilistic network for the diagnosis of acute cardiopulmonary diseases is presented in detail. A panel of expert physicians collaborated to specify the qualitative part, which is a directed acyclic graph defining a factorization of the joint probability distribution of domain variables into univariate conditional distributions. The quantitative part, which is a set of parametric models defining these univariate conditional distributions, was estimated following the Bayesian paradigm. In particular, we exploited an original reparameterization of Beta and categorical logistic regression models to elicit the joint prior distribution of parameters from medical experts, and updated it by conditioning on a dataset of hospital records via Markov chain Monte Carlo simulation. Refinement was iteratively performed until the probabilistic network provided satisfactory concordance index values for several acute diseases and reasonable diagnosis for six fictitious patient cases. The probabilistic network can be employed to perform medical diagnosis on a total of 63 diseases (38 acute and 25 chronic) on the basis of up to 167 patient findings.

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Evaluating the Accuracy of Sampling-Based Approaches to the Calculation of Posterior Moments

Cited by 2,109 publications

References 18 publications

Closure of schools during an influenza pandemic

Closure of schools during an influenza pandemic

Measles outbreak response decision-making under uncertainty: a retrospective analysis

A probabilistic network for the diagnosis of acute cardiopulmonary diseases

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