Stochastic models in population biology: from dynamic noise to Bayesian description and model comparison for given data sets

Mateus, Luís; Stollenwerk, Nico; Zambrini, Jean-Claude

doi:10.1080/00207160.2013.792924

Cited by 13 publications

(22 citation statements)

References 5 publications

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“…In this paper, vaccine efficacy in preventing hospitalized dengue cases was estimated using a Bayesian approach [34, 36]. Using the WHO recommendation to vaccinate persons age 9–45 years in dengue endemic countries with a high seroprevalence of dengue antibodies, an age-group-structured model was developed to evaluate the impact of vaccination campaigns with and without serological screening for past dengue infections.…”

Section: Discussionmentioning

confidence: 99%

The Impact of the Newly Licensed Dengue Vaccine in Endemic Countries

2016

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BackgroundWith approximately 3 billion people at risk of acquiring the infection, dengue fever is now considered the most important mosquito-borne viral disease in the world, with 390 million dengue infections occurring every year, of which 96 million manifest symptoms with any level of disease severity. Treatment of uncomplicated dengue cases is only supportive and severe dengue cases require hospital intensive care. A vaccine now licensed in several countries and developed by Sanofi Pasteur (CYD-TDV, named Dengvaxia), was able to protect, in the first 25 months of the two Phase III, 66% of a subset of 9–16 year old participants. However, a significantly lower efficacy (including negative vaccine efficacy) was noted for children younger than 9 years of age.Methodology/Principal FindingsAnalysis of year 3 results of phase III trials of Dengvaxia suggest high rates of protection of vaccinated partial dengue immunes but high rates of hospitalizations during breakthrough dengue infections of persons who were vaccinated when seronegative, with vaccine appearing to induce enhancing antibodies (ADE). An age structured model was developed based on Sanofi’s recommendation to vaccinate persons age 945 years in dengue endemic countries. The model was used to explore the clinical burden of two vaccination strategies: 1) Vaccinate 4 or 20% of individuals, ages 9–45 years, seropositives and seronegatives, and 2) vaccinate 4 or 20% of individuals, ages 9–45 years, who are dengue immune only.Conclusions/SignificanceOur results show that vaccinating dengue monotypic immune individuals prevents dengue hospitalizations, but at the same time dengue infections of vaccine-sensitized persons increases hospitalizations. When the vaccine is given only to partial immune individuals, after immunological screening of the population, disease burden decreases considerably.

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Section: Discussionmentioning

confidence: 99%

The Impact of the Newly Licensed Dengue Vaccine in Endemic Countries

2016

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“…For details on the derivation of master equations, see [10,11]. Equation (5) can be solved analytically [9] using the characteristic function…”

Section: The Linear Infection Modelmentioning

confidence: 99%

“…In this paper, we investigate an easily analytically treatable epidemiological process, the linear infection model [8], presented in Section 2, for which we have recently been able to compute not only time dependent solutions for the master equation but also likelihood functions and Bayesian posteriors [9]. In Section 3, we calculate the semiclassical approximation and solve Hamilton's equations of motion, which arise from a Hamilton-Jacobi equation via the method of characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…Ultimately, predictive models on the basis of already observed data are sought, which capture the essential future dynamics, and this in competition with other predictive models. A first study on model comparison based on the Bayes factor [9] revealed that the information arising from empirical data may not be able to capture all the features of the underlying process. The semiclassical approach, as well as other approximation schemes, can lead the way to a rigourous treatment of more complex models.…”

Section: Introductionmentioning

confidence: 99%

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Epidemiological models in semiclassical approximation: an analytically solvable model as a test case

Mateus

Masoero

Rocha

et al. 2016

Math Methods in App Sciences

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Communicated by E. VenturinoStochastic models that can describe real epidemiological processes can become very quickly quite complex. Approximation schemes are a useful tool to understand the qualitative behaviour of such processes. In this paper, we investigate the semiclassical approximation, performed in the context of the Hamilton-Jacobi formalism, for solutions of master equations of stochastic epidemiological systems. In a test case of a previously investigated process, the linear infection model, we can analytically solve Hamilton's equations of motion. This helps to understand generalizations to more complex epidemiological systems as needed to describe realistic cases like multi-strain systems applicable to dengue fever, for example. The connection between the semiclassical approach for epidemiological systems and the Wentzel-Kramers-Brillouin approximation in quantum mechanics is also discussed.

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“…in the biomedical EEG problem [4,5], in population biology and biofilm growth [3,10] and in particular in eddy current inspection. The latter is a technique used for, among others, the detection of cracks in tubes and magnetite clogging of the Tube Support Plate (TSP) in steam generators at nuclear power plants [2,11].…”

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis and variance reduction in a stochastic non-destructive testing problem

Staelen

Beddek

2014

International Journal of Computer Mathematics

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In this paper, we present a framework to deal with uncertainty quantification in case where the ranges of variability of the random parameters are ill-known. Namely the physical properties of the corrosion product (magnetite) which frequently clogs the tube support plate of steam generator, which is inaccessible in nuclear power plants. The methodology is based on Polynomial Chaos (PC) for the direct approach and on Bayesian inference for the inverse approach. The direct Non-Intrusive Spectral Projection (NISP) method is first employed by considering prior probability densities and therefore constructing a PC surrogate model of the large-scale NDT finite element model. To face the prohibitive computational cost underlying the high dimensional random space, an adaptive sparse grid technique is applied on NISP resulting in drastic time reduction. The PC surrogate model, with reduced dimensionality, is used as a forward model in the Bayesian procedure. The posterior probability densities are then identified by inferring from few noisy experimental data. We demonstrate effectiveness of the approach by identifying the most influential parameter in the clogging detection as well as a variability range reduction.

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Stochastic models in population biology: from dynamic noise to Bayesian description and model comparison for given data sets

Cited by 13 publications

References 5 publications

The Impact of the Newly Licensed Dengue Vaccine in Endemic Countries

The Impact of the Newly Licensed Dengue Vaccine in Endemic Countries

Epidemiological models in semiclassical approximation: an analytically solvable model as a test case

Sensitivity analysis and variance reduction in a stochastic non-destructive testing problem

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