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.

scholarly journals A modified SEIR model to predict the behavior of the early stage in coronavirus and coronavirus-like outbreaks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wilfredo Angulo ◽  
José M. Ramírez ◽  
Dany De Cecchis ◽  
Juan Primera ◽  
Henry Pacheco ◽  
...  
Keyword(s):  
Molecular Structure ◽  
Risk Perceptions ◽  
Transmission Rate ◽  
Early Stage ◽  
Individual Risk ◽  
Seir Model ◽  
Proposed Model ◽  
Probable Site ◽  
Adjusted Model ◽  
The Impact

AbstractCOVID-19 is a highly infectious disease that emerged in China at the end of 2019. The COVID-19 pandemic is the first known pandemic caused by a coronavirus, namely, the new and emerging SARS-CoV-2 coronavirus. In the present work, we present simulations of the initial outbreak of this new coronavirus using a modified transmission rate SEIR model that takes into account the impact of government actions and the perception of risk by individuals in reaction to the proportion of fatal cases. The parameters related to these effects were fitted to the number of infected cases in the 33 provinces of China. The data for Hubei Province, the probable site of origin of the current pandemic, were considered as a particular case for the simulation and showed that the theoretical model reproduces the behavior of the data, thus indicating the importance of combining government actions and individual risk perceptions when the proportion of fatal cases is greater than $$4\%$$ 4 % . The results show that the adjusted model reproduces the behavior of the data quite well for some provinces, suggesting that the spread of the disease differs when different actions are evaluated. The proposed model could help to predict outbreaks of viruses with a biological and molecular structure similar to that of SARS-CoV-2.

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 Modelling the impact of curtailing antibiotic usage in food animals on antibiotic resistance in humans

2017 ◽  
Vol 4 (4) ◽  
pp. 161067 ◽  
Author(s):  
B. A. D. van Bunnik ◽  
M. E. J. Woolhouse
Keyword(s):  
Bacterial Infections ◽  
Antibiotic Consumption ◽  
Antibiotic Usage ◽  
Model Parameters ◽  
Simple Mathematical Model ◽  
Quantitative Evidence ◽  
Wide Range ◽  
Food Animals ◽  
The Impact ◽  
Equivalent Reduction

Consumption of antibiotics in food animals is increasing worldwide and is approaching, if not already surpassing, the volume consumed by humans. It is often suggested that reducing the volume of antibiotics consumed by food animals could have public health benefits. Although this notion is widely regarded as intuitively obvious there is a lack of robust, quantitative evidence to either support or contradict the suggestion. As a first step towards addressing this knowledge gap, we develop a simple mathematical model for exploring the generic relationship between antibiotic consumption by food animals and levels of resistant bacterial infections in humans. We investigate the impact of restricting antibiotic consumption by animals and identify which model parameters most strongly determine that impact. Our results suggest that, for a wide range of scenarios, curtailing the volume of antibiotics consumed by food animals has, as a stand-alone measure, little impact on the level of resistance in humans. We also find that reducing the rate of transmission of resistance from animals to humans may be more effective than an equivalent reduction in the consumption of antibiotics in food animals. Moreover, the response to any intervention is strongly determined by the rate of transmission from humans to animals, an aspect which is rarely considered.

scholarly journals Stationary Ca2+ nanodomains in the presence of buffers with two binding sites

2020 ◽  
Author(s):  
Y. Chen ◽  
V. Matveev
Keyword(s):  
Closed Form ◽  
Binding Sites ◽  
Interpolation Method ◽  
Secretory Vesicle ◽  
Model Parameters ◽  
Relevant Parameter ◽  
Wide Range ◽  
Vesicle Exocytosis ◽  
Cell Processes ◽  
The Impact

ABSTRACTWe examine closed-form approximations for the equilibrium Ca2+ concentration near a point Ca2+ source representing a Ca2+ channel, in the presence of a mobile Ca2+ buffer with 2:1 Ca2+ binding stoichiometry. We consider buffers with two Ca2+ binding sites activated in tandem and possessing distinct binding affinities and kinetics. This allows to model the impact on Ca2+ nanodomains of realistic endogenous Ca2+ buffers characterized by cooperative Ca2+ binding, such as calretinin. The approximations we present involve a combination or rational and exponential functions, whose parameters are constrained using the series interpolation method that we recently introduced for the case of 1:1 Ca2+ buffers. We conduct extensive parameter sensitivity analysis and show that the obtained closed-form approximations achieve reasonable qualitative accuracy for a wide range of buffer’s Ca2+ binding properties and other relevant model parameters. In particular, the accuracy of the newly derived approximants exceeds that of the rapid buffering approximation in large portions of the relevant parameter space.STATEMENT OF SIGNIFICANCEClosed-form approximations describing equilibrium distribution of Ca2+ in the vicinity of an open Ca2+ channel proved useful for the modeling of local Ca2+ signals underlying secretory vesicle exocytosis, muscle contraction and other cell processes. Such approximations provide an efficient method for estimating Ca2+ and buffer concentrations without computationally expensive numerical simulations. However, while most biological buffers have multiple Ca2+ binding sites, much of prior modeling work considered Ca2+ dynamics in the presence of Ca2+ buffers with a single Ca2+ binding site. Here we extend modeling work on equilibrium Ca2+ nanodomains to the case of Ca2+ buffers with two binding sites, allowing to gain deeper insight into the impact of more realistic Ca2+ buffers, including cooperative buffers, on cell Ca2+ dynamics.

VLE Determination of Carbon Dioxide Loaded Aqueous Alkanolamine Mixtures Using Modified Kent Eisenberg Model

2017 ◽  
Vol 231 (11-12) ◽  
Author(s):  
Humbul Suleman ◽  
Abdulhalim Shah Maulud ◽  
Zakaria Man
Keyword(s):  
Experimental Data ◽  
Carbon Dioxide ◽  
Model Parameters ◽  
Thermodynamic Models ◽  
Carbon Dioxide Absorption ◽  
Proposed Model ◽  
Wide Range ◽  
Experimental Values ◽  
Good Agreement

AbstractA computationally simple thermodynamic framework has been presented to correlate the vapour-liquid equilibria of carbon dioxide absorption in five representative types of alkanolamine mixtures. The proposed model is an extension of modified Kent Eisenberg model for the carbon dioxide loaded aqueous alkanolamine mixtures. The model parameters are regressed on a large experimental data pool of carbon dioxide solubility in aqueous alkanolamine mixtures. The model is applicable to a wide range of temperature (298–393 K), pressure (0.1–6000 kPa) and alkanolamine concentration (0.3–5 M). The correlated results are compared to the experimental values and found to be in good agreement with the average deviations ranging between 6% and 20%. The model results are comparable to other thermodynamic models.

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.

scholarly journals Generalized Behavioral Modelling Methodology of Switch-Diode Cell for Power Loss Prediction in Electromagnetic Transient Simulation

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1500
Author(s):  
Yanming Xu ◽  
Carl Ngai Man Ho ◽  
Avishek Ghosh ◽  
Dharshana Muthumuni
Keyword(s):  
Power Loss ◽  
Process Analysis ◽  
Operating Conditions ◽  
Transient Simulation ◽  
Oxide Semiconductor ◽  
Model Parameters ◽  
Electromagnetic Transient ◽  
Proposed Model ◽  
Wide Range ◽  
Electromagnetic Transient Simulation

Modern wide-bandgap (WBG) devices, such as silicon carbide (SiC) or gallium nitride (GaN) based devices, have emerged and been increasingly used in power electronics (PE) applications due to their superior switching feature. The power losses of these devices become the key of system efficiency improvement, especially for high-frequency applications. In this paper, a generalized behavioral model of a switch-diode cell (SDC) is proposed for power loss estimation in the electromagnetic transient simulation. The proposed model is developed based on the circuit level switching process analysis, which considers the effects of parasitics, the operating temperature, and the interaction of diode and switch. In addition, the transient waveforms of the SDC are simulated by the proposed model using dependent voltage and current sources with passive components. Besides, the approaches of obtaining model parameters from the datasheets are given and the modelling method is applicable to various semiconductors such Si insulated-gate bipolar transistor (IGBT), Si/SiC metal–oxide–semiconductor field-effect transistor (MOSFET), and GaN devices. Further, a multi-dimensional power loss table in a wide range of operating conditions can be obtained with fast speed and reasonable accuracy. The proposed approach is implemented in PSCAD/ Electromagnetic Transients including DC, EMTDC, (v4.6, Winnipeg, MB, Canada) and further verified by the hardware setups including different daughter boards for different devices.

scholarly journals Agent-based modelling of complex factors impacting malaria prevalence

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Miracle Amadi ◽  
Anna Shcherbacheva ◽  
Heikki Haario
Keyword(s):  
Malaria Transmission ◽  
Field Data ◽  
Control Measures ◽  
Model Parameters ◽  
Economic Factors ◽  
Agent Based ◽  
Wide Range ◽  
The Impact ◽  
Socio Economic Factors

Abstract Background Increasingly complex models have been developed to characterize the transmission dynamics of malaria. The multiplicity of malaria transmission factors calls for a realistic modelling approach that incorporates various complex factors such as the effect of control measures, behavioural impacts of the parasites to the vector, or socio-economic variables. Indeed, the crucial impact of household size in eliminating malaria has been emphasized in previous studies. However, increasing complexity also increases the difficulty of calibrating model parameters. Moreover, despite the availability of much field data, a common pitfall in malaria transmission modelling is to obtain data that could be directly used for model calibration. Methods In this work, an approach that provides a way to combine in situ field data with the parameters of malaria transmission models is presented. This is achieved by agent-based stochastic simulations, initially calibrated with hut-level experimental data. The simulation results provide synthetic data for regression analysis that enable the calibration of key parameters of classical models, such as biting rates and vector mortality. In lieu of developing complex dynamical models, the approach is demonstrated using most classical malaria models, but with the model parameters calibrated to account for such complex factors. The performance of the approach is tested against a wide range of field data for Entomological Inoculation Rate (EIR) values. Results The overall transmission characteristics can be estimated by including various features that impact EIR and malaria incidence, for instance by reducing the mosquito–human contact rates and increasing the mortality through control measures or socio-economic factors. Conclusion Complex phenomena such as the impact of the coverage of the population with long-lasting insecticidal nets (LLINs), changes in behaviour of the infected vector and the impact of socio-economic factors can be included in continuous level modelling. Though the present work should be interpreted as a proof of concept, based on one set of field data only, certain interesting conclusions can already be drawn. While the present work focuses on malaria, the computational approach is generic, and can be applied to other cases where suitable in situ data is available.

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