scholarly journals Modeling Chickenpox Dynamics with a Discrete Time Bayesian Stochastic Compartmental Model

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
A. Corberán-Vallet ◽  
F. J. Santonja ◽  
M. Jornet-Sanz ◽  
R.-J. Villanueva
Keyword(s):  
Discrete Time ◽  
Compartmental Model ◽  
Predictive Distribution ◽  
Model Parameters ◽  
Immunization Program ◽  
Posterior Predictive Distribution ◽  
Vaccination Strategies ◽  
Proposed Model ◽  
Routine Immunization Program ◽  
The Impact

We present a Bayesian stochastic susceptible-exposed-infectious-recovered model in discrete time to understand chickenpox transmission in the Valencian Community, Spain. During the last decades, different strategies have been introduced in the routine immunization program in order to reduce the impact of this disease, which remains a public health’s great concern. Under this scenario, a model capable of explaining closely the dynamics of chickenpox under the different vaccination strategies is of utter importance to assess their effectiveness. The proposed model takes into account both heterogeneous mixing of individuals in the population and the inherent stochasticity in the transmission of the disease. As shown in a comparative study, these assumptions are fundamental to describe properly the evolution of the disease. The Bayesian analysis of the model allows us to calculate the posterior distribution of the model parameters and the posterior predictive distribution of chickenpox incidence, which facilitates the computation of point forecasts and prediction intervals.

Analyzing the Impact of Using Biodiesel in the Parameters of a 30 kW Micro-Turbine Control Model

2006 ◽  
Author(s):  
Manuel A. Rendo´n ◽  
Marco A. R. Do Nascimento ◽  
Pedro P. C. Mendes
Keyword(s):  
Electrical Power ◽  
Chemical Properties ◽  
Control Model ◽  
Model Parameters ◽  
Proposed Model ◽  
Complete Study ◽  
Micro Turbine ◽  
Turbine Speed ◽  
Physical And Chemical ◽  
The Impact

This work presents the modifications in a 30 kW gas micro-turbine speed control model, when it was supplied with castor bean biodiesel in several proportions. The concern about using biodiesel as an alternative fuel is increasing in the Brazilian distributed generation market. For this analytics, a complete study was developed considering the effects of using this new fuel. Characteristics like chemical composition, physical and chemical properties of the different mixtures were analyzed, especially focusing on the kinematic viscosity of the fuel. The tests results performed with the micro-turbine, originally projected for diesel, are shown. Mixtures of 5, 10, 15, 20, 25, 30, 50 e 100% of biodesel were used, and several variables were measured in the whole range of power. The influence of the biodiesel characteristics in the model parameters are commented in the conclusions. The possible application of the proposed model in studies of electrical power network is suggested in the end of the article.

scholarly journals End-to-end speech emotion recognition using a novel context-stacking dilated convolution neural network

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Duowei Tang ◽  
Peter Kuppens ◽  
Luc Geurts ◽  
Toon van Waterschoot
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Speech Signal ◽  
State Of The Art ◽  
Speech Emotion Recognition ◽  
Model Parameters ◽  
Proposed Model ◽  
End To End ◽  
The Impact ◽  
Temporal Dependencies

AbstractAmongst the various characteristics of a speech signal, the expression of emotion is one of the characteristics that exhibits the slowest temporal dynamics. Hence, a performant speech emotion recognition (SER) system requires a predictive model that is capable of learning sufficiently long temporal dependencies in the analysed speech signal. Therefore, in this work, we propose a novel end-to-end neural network architecture based on the concept of dilated causal convolution with context stacking. Firstly, the proposed model consists only of parallelisable layers and is hence suitable for parallel processing, while avoiding the inherent lack of parallelisability occurring with recurrent neural network (RNN) layers. Secondly, the design of a dedicated dilated causal convolution block allows the model to have a receptive field as large as the input sequence length, while maintaining a reasonably low computational cost. Thirdly, by introducing a context stacking structure, the proposed model is capable of exploiting long-term temporal dependencies hence providing an alternative to the use of RNN layers. We evaluate the proposed model in SER regression and classification tasks and provide a comparison with a state-of-the-art end-to-end SER model. Experimental results indicate that the proposed model requires only 1/3 of the number of model parameters used in the state-of-the-art model, while also significantly improving SER performance. Further experiments are reported to understand the impact of using various types of input representations (i.e. raw audio samples vs log mel-spectrograms) and to illustrate the benefits of an end-to-end approach over the use of hand-crafted audio features. Moreover, we show that the proposed model can efficiently learn intermediate embeddings preserving speech emotion information.

Multivariate generalized linear mixed models for continuous bounded outcomes: Analyzing the body fat percentage data

2021 ◽  
pp. 096228022110432
Author(s):  
Ricardo R Petterle ◽  
Henrique A Laureano ◽  
Guilherme P da Silva ◽  
Wagner H Bonat
Keyword(s):  
Maximum Likelihood ◽  
Body Fat ◽  
The Body ◽  
Model Parameters ◽  
Body Fat Percentage ◽  
Fat Percentage ◽  
Data Set ◽  
Proposed Model ◽  
Computational Implementation ◽  
The Impact

We propose a multivariate regression model to handle multiple continuous bounded outcomes. We adopted the maximum likelihood approach for parameter estimation and inference. The model is specified by the product of univariate probability distributions and the correlation between the response variables is obtained through the correlation matrix of the random intercepts. For modeling continuous bounded variables on the interval [Formula: see text] we considered the beta and unit gamma distributions. The main advantage of the proposed model is that we can easily combine different marginal distributions for the response variable vector. The computational implementation is performed using Template Model Builder, which combines the Laplace approximation with automatic differentiation. Therefore, the proposed approach allows us to estimate the model parameters quickly and efficiently. We conducted a simulation study to evaluate the computational implementation and the properties of the maximum likelihood estimators under different scenarios. Moreover, we investigate the impact of distribution misspecification in the proposed model. Our model was motivated by a data set with multiple continuous bounded outcomes, which refer to the body fat percentage measured at five regions of the body. Simulation studies and data analysis showed that the proposed model provides a general and rich framework to deal with multiple continuous bounded outcomes.

scholarly journals Kinetic models for epidemic dynamics with social heterogeneity

2021 ◽  
Vol 83 (1) ◽  
Author(s):  
G. Dimarco ◽  
B. Perthame ◽  
G. Toscani ◽  
M. Zanella
Keyword(s):  
Compartmental Model ◽  
Numerical Solutions ◽  
Population Based ◽  
Social Contacts ◽  
Epidemic Dynamics ◽  
Proposed Model ◽  
Rapid Spread ◽  
Social Heterogeneity ◽  
Contact Distribution ◽  
The Impact

AbstractWe introduce a mathematical description of the impact of the number of daily contacts in the spread of infectious diseases by integrating an epidemiological dynamics with a kinetic modeling of population-based contacts. The kinetic description leads to study the evolution over time of Boltzmann-type equations describing the number densities of social contacts of susceptible, infected and recovered individuals, whose proportions are driven by a classical SIR-type compartmental model in epidemiology. Explicit calculations show that the spread of the disease is closely related to moments of the contact distribution. Furthermore, the kinetic model allows to clarify how a selective control can be assumed to achieve a minimal lockdown strategy by only reducing individuals undergoing a very large number of daily contacts. We conduct numerical simulations which confirm the ability of the model to describe different phenomena characteristic of the rapid spread of an epidemic. Motivated by the COVID-19 pandemic, a last part is dedicated to fit numerical solutions of the proposed model with infection data coming from different European countries.

scholarly journals Functional dependence of COVID-19 growth rate on lockdown conditions and rate of vaccination.

2021 ◽  
Author(s):  
Felix Mairanowski ◽  
Denis Below
Keyword(s):  
Analytical Model ◽  
Functional Dependence ◽  
Effective Rate ◽  
Mass Vaccination ◽  
Model Parameters ◽  
Sequencing Data ◽  
Functional Relationships ◽  
Proposed Model ◽  
The Impact ◽  
Good Agreement

Abstract It is shown that derived from the solution of differential equations analytical model adequately describes development epidemics with changes in both lockdown conditions and the effective rate of mass vaccination of the population. As in previous studies, the control calculations are in good agreement with observations at all stages of epidemic growth. One of the two model coefficients is uniquely related to the lockdown efficiency parameter. We obtained an approximate correlation between this parameter and the main conditions of lockdown, in particular, physical distancing, reduction in social contacts and strictness of the mask regime. The calculation of the incident over a seven-day period using the proposed model is in good agreement with the observational data. Analysis of both curves shows that a better agreement can be obtained by taking into account the lag time of the epidemic response of about 10 days. From the reverse calculation a time-varying curve of the infection rate associated with the "new" virus strain under mutation conditions is obtained, which is qualitatively confirmed by the sequencing data. Based on these studies, it is possible to conclude that the ASILV analytical model developed here can be used to reliably and promptly predict epidemic development under conditions of lockdown and mass vaccination without the use of numerical methods. The functional relationships identified allow us to conduct a rapid analysis of the impact of each of the model parameters on the overall process of the epidemic. In contrast to previous studies, the calculations of the proposed model were performed using EXCEL, rather than a standard calculator. This is due to the need to account for multiple changes in lockdown conditions and vaccination rates.

scholarly journals A Novel Compartmental Model for Analysis and Projection of COVID-19 Dynamics in Bangladesh

2020 ◽  
pp. 14-28
Author(s):  
Md. Mijanur Rahman ◽  
Md. Sadekur Rahman Rani
Keyword(s):  
Compartmental Model ◽  
False Negative ◽  
False Negative Rate ◽  
Model Parameters ◽  
Contact Rate ◽  
Rate Analysis ◽  
Negative Rate ◽  
Exposed Population ◽  
Effective Contact ◽  
The Impact

A novel compartmental model is proposed to project the COVID-19 dynamics in Bangladesh. The exposed population is divided into two classes: tested and not tested. Model parameters are estimated by fitting the output with empirical COVID-19 data of Bangladesh from 7 April 2020 to 15 June 2020. It is found that even if 90% of exposed individuals are tested, number of unidentified cases (recovered or dead) is 3 to 4 times than that of identified cases. As of 15 June 2020, Bangladesh is using the Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) based test to detect the novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The impact of false negative rate of this test on unidentified infection is analyzed. It is found that the year-end total recoveries (deaths) surges 700 (800) times if the false negative rate is doubled. Periodic lockdown and relaxation intervals are incorporated by defining the effective contact rate (β) as a periodic function of time. Impact of lockdown is perspicuous from the periodic fluctuation of the basic reproduction number ( ). It is observed that a 90-day-lockdwon reduces the final outcome by 3% while a 30-day-lockdwon increases it by 2%. On other hand, casualties are 10 to 100 times worse in case of no lockdown even with less than half effective contact rate. Analysis of strictness of isolation reveals that a 12.5% increase in the strictness coefficient reduces the exposed population 2.5 times whereas a 37.5% decrease in it intensifies the outcome nearly 9 times. Projections up to 6 April 2021 suggests that the epidemic will reach its peak in Bangladesh in August 2020.

scholarly journals A COVID-19 model with a nonlinear incidence rate and intervention exit strategies for Kenya

2021 ◽  
Author(s):  
Rachel Waema Mbogo ◽  
Farai Nyabadza
Keyword(s):  
Large Scale ◽  
Model Parameters ◽  
Clear Understanding ◽  
Nonlinear Incidence ◽  
Intervention Measures ◽  
Proposed Model ◽  
Rapid Spread ◽  
Nonlinear Incidence Function ◽  
Individual Self ◽  
The Impact

Abstract The coronavirus disease (COVID-19) is a novel infection caused by SARS-CoV-2, a corona virus type that has previously not been seen in humans. The speedy spread of COVID-19 globally has greatly affected the socio-economic environments and health systems. To effectively address this rapid spread, it is imperative to have a clear understanding of the COVID-19 transmission dynamics. In this study we evaluate a COVID-19 epidemic model with a nonlinear incidence function and a saturating. We propose an SLIHRD data driven COVID 19 model which incorporates individual self initiated behavior change of the susceptible individuals. The proposed model allows the evaluation of the impact of easing intervention measures at specific times. To estimate the model parameters, the model was fitted to the daily reported COVID-19 cases in Kenya. Self initiated behavioral responses by individuals and large scale persistent testing proved to be the most effective measures to flatten the epidemic infection curve.The model illustrates the effect of mass testing on COVID-19 as well as individual self initiated behavioral change when the number of infected individuals increases. The results have significant impact on the management of COVID-19 and implementation of prevention policies.

scholarly journals Bifurcation analysis of a SEIR epidemic system with governmental action and individual reaction

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Abdelhamid Ajbar ◽  
Rubayyi T. Alqahtani
Keyword(s):  
Transmission Rate ◽  
Dynamical Behavior ◽  
Step Function ◽  
Model Parameters ◽  
Periodic Behavior ◽  
Proposed Model ◽  
Wide Range ◽  
Individual Reaction ◽  
The Impact ◽  
Governmental Action

Abstract In this paper, the dynamical behavior of a SEIR epidemic system that takes into account governmental action and individual reaction is investigated. The transmission rate takes into account the impact of governmental action modeled as a step function while the decreasing contacts among individuals responding to the severity of the pandemic is modeled as a decreasing exponential function. We show that the proposed model is capable of predicting Hopf bifurcation points for a wide range of physically realistic parameters for the COVID-19 disease. In this regard, the model predicts periodic behavior that emanates from one Hopf point. The model also predicts stable oscillations connecting two Hopf points. The effect of the different model parameters on the existence of such periodic behavior is numerically investigated. Useful diagrams are constructed that delineate the range of periodic behavior predicted by the model.

scholarly journals Quantum Dynamics in Continuum for Proton Transport I: Basic Formulation

2013 ◽  
Vol 13 (1) ◽  
pp. 285-324 ◽  
Author(s):  
Duan Chen ◽  
Guo-Wei Wei
Keyword(s):  
Proton Transport ◽  
Quantum Dynamics ◽  
Density Functional ◽  
Molecular Surface ◽  
Electrostatic Energy ◽  
Model Parameters ◽  
Proposed Model ◽  
Wide Range ◽  
Mathematical Algorithms ◽  
The Impact

AbstractProton transport is one of the most important and interesting phenomena in living cells. The present work proposes a multiscale/multiphysics model for the understanding of the molecular mechanism of proton transport in transmembrane proteins. We describe proton dynamics quantum mechanically via a density functional approach while implicitly model other solvent ions as a dielectric continuum to reduce the number of degrees of freedom. The densities of all other ions in the solvent are assumed to obey the Boltzmann distribution. The impact of protein molecular structure and its charge polarization on the proton transport is considered explicitly at the atomic level. We formulate a total free energy functional to put proton kinetic and potential energies as well as electrostatic energy of all ions on an equal footing. The variational principle is employed to derive nonlinear governing equations for the proton transport system. Generalized Poisson-Boltzmann equation and Kohn-Sham equation are obtained from the variational framework. Theoretical formulations for the proton density and proton conductance are constructed based on fundamental principles. The molecular surface of the channel protein is utilized to split the discrete protein domain and the continuum solvent domain, and facilitate the multiscale discrete/continuum/quantum descriptions. A number of mathematical algorithms, including the Dirichlet to Neumann mapping, matched interface and boundary method, Gummel iteration, and Krylov space techniques are utilized to implement the proposed model in a computationally efficient manner. The Gramicidin A (GA) channel is used to demonstrate the performance of the proposed proton transport model and validate the efficiency of proposed mathematical algorithms. The electrostatic characteristics of the GA channel is analyzed with a wide range of model parameters. The proton conductances are studied over a number of applied voltages and reference concentrations. A comparison with experimental data verifies the present model predictions and validates the proposed model.

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