We consider state and parameter estimation for compartmental models having both timevarying and time-invariant parameters.In this manuscript,we first detail a general Bayesian computational framework as a continuation...We consider state and parameter estimation for compartmental models having both timevarying and time-invariant parameters.In this manuscript,we first detail a general Bayesian computational framework as a continuation of our previous work.Subsequently,this framework is specifically tailored to the susceptible-infectious-removed(SIR)model which describes a basic mechanism for the spread of infectious diseases through a system of coupled nonlinear differential equations.The SIR model consists of three states,namely,the susceptible,infectious,and removed compartments.The coupling among these states is controlled by two parameters,the infection rate and the recovery rate.The simplicity of the SIR model and similar compartmental models make them applicable to many classes of infectious diseases.However,the combined assumption of a deterministic model and time-invariance among the model parameters are two significant impediments which critically limit their use for long-term predictions.The tendency of certain model parameters to vary in time due to seasonal trends,non-pharmaceutical interventions,and other random effects necessitates a model that structurally permits the incorporation of such time-varying effects.Complementary to this,is the need for a robust mechanism for the estimation of the parameters of the resulting model from data.To this end,we consider an augmented state vector,which appends the time-varying parameters to the original system states whereby the time evolution of the time-varying parameters are driven by an artificial noise process in a standard manner.Distinguishing between time-varying and time-invariant parameters in this fashion limits the introduction of artificial dynamics into the system,and provides a robust,fully Bayesian approach for estimating the timeinvariant system parameters as well as the elements of the process noise covariance matrix.This computational framework is implemented by leveraging the robustness of the Markov chain Monte Carlo algorithm permits the estimation of time-invariant parameters while nested nonlinear filters concurrently perform the joint estimation of the system states and time-varying parameters.We demonstrate performance of the framework by first considering a series of examples using synthetic data,followed by an exposition on public health data collected in the province of Ontario.展开更多
This paper presents a compartmental model for bacterial infections in a population distributed over a network of individuals.Within each node,individuals interact,bacteria can be transmitted and the disease may be spr...This paper presents a compartmental model for bacterial infections in a population distributed over a network of individuals.Within each node,individuals interact,bacteria can be transmitted and the disease may be spread;moreover,the acquisition of bacterial antibiotic resistance is considered.In addition,nodes are connected through weighted edges,and consequently individuals from different nodes may interact.As a result,the infection may be propagated over the network.We perform an analysis on this propagation as well as numerical simulations in order to illustrate the validity of the model.展开更多
This paper presents a new hybrid compartmental model for studying the COVID-19 epidemic evolution in Italy since the beginning of the vaccination campaign started on 2020/12/27 and shows forecasts of the epidemic evol...This paper presents a new hybrid compartmental model for studying the COVID-19 epidemic evolution in Italy since the beginning of the vaccination campaign started on 2020/12/27 and shows forecasts of the epidemic evolution in Italy in the first six months.The proposed compartmental model subdivides the population into six compartments and extends the SEIRD model proposed in[E.L.Piccolomini and F.Zama,PLOS ONE,15(8):1e17,082020]by adding the vaccinated population and framing the global model as a hybridswitched dynamical system.Aiming to represent the quantities that characterize the epidemic behaviour from an accurate fit to the observed data,we partition the observation time interval into sub-intervals.The model parameters change according to a switching rule depending on the data behaviour and the infection rate continuity condition.In particular,we study the representation of the infection rate both as linear and exponential piecewise continuous functions.We choose the length of sub-intervals balancing the data fit with the model complexity through the Bayesian Information Criterion.We tested the model on italian data and on local data from Emilia-Romagna region.The calibration of the model shows an excellent representation of the epidemic behaviour in both cases.Thirty days forecasts have proven to well reproduce the infection spread,better for regional than for national data.Both models produce accurate predictions of infected,but the exponential-based one perform better in most of the cases.Finally,we discuss different possible forecast scenarios obtained by simulating an increased vaccination rate.展开更多
Deterministic compartment models(CMs)and stochastic models,including stochastic CMs and agent-based models,are widely utilized in epidemic modeling.However,the relationship between CMs and their corresponding stochast...Deterministic compartment models(CMs)and stochastic models,including stochastic CMs and agent-based models,are widely utilized in epidemic modeling.However,the relationship between CMs and their corresponding stochastic models is not well understood.The present study aimed to address this gap by conducting a comparative study using the susceptible,exposed,infectious,and recovered(SEIR)model and its extended CMs from the coronavirus disease 2019 modeling literature.We demonstrated the equivalence of the numerical solution of CMs using the Euler scheme and their stochastic counterparts through theoretical analysis and simulations.Based on this equivalence,we proposed an efficient model calibration method that could replicate the exact solution of CMs in the corresponding stochastic models through parameter adjustment.The advancement in calibration techniques enhanced the accuracy of stochastic modeling in capturing the dynamics of epidemics.However,it should be noted that discrete-time stochastic models cannot perfectly reproduce the exact solution of continuous-time CMs.Additionally,we proposed a new stochastic compartment and agent mixed model as an alternative to agent-based models for large-scale population simulations with a limited number of agents.This model offered a balance between computational efficiency and accuracy.The results of this research contributed to the comparison and unification of deterministic CMs and stochastic models in epidemic modeling.Furthermore,the results had implications for the development of hybrid models that integrated the strengths of both frameworks.Overall,the present study has provided valuable epidemic modeling techniques and their practical applications for understanding and controlling the spread of infectious diseases.展开更多
This study presents a mathematical modelling approach to analyze the impact of family planning interventions on population growth dynamics.Using a compartmental model,the population is divided into six groups:Suscepti...This study presents a mathematical modelling approach to analyze the impact of family planning interventions on population growth dynamics.Using a compartmental model,the population is divided into six groups:Susceptible,Informed,Sexually Active Non-Users,Contraceptive Users,Non-Users and General Population.The model incorporates differential equations to describe transitions among these compartments,influenced by factors such as sexual behavior,contraceptive adoption,and public health education.Analytical techniques,including equilibrium analysis and the computation of the basic reproductive number were used to evaluate the model’s behavior and stability.Numerical simulations conducted in MATLAB revealed that increased contraceptive usage and awareness significantly reduce the number of high-risk individuals while stabilizing overall population growth.The reproductive number was shown to decrease as contraceptive uptake increased,confirming the effectiveness of intervention strategies.The findings highlight the importance of reproductive health education and contraceptive access in managing population growth,providing valuable insights for policymakers and public health planners.This study demonstrates the potential of mathematical modelling as a predictive and policy-support tool in reproductive health and demographic planning.展开更多
In this article, we consider the construction of a SVIR (Susceptible, Vaccinated, Infected, Recovered) stochastic compartmental model of measles. We prove that the deterministic solution is asymptotically the average ...In this article, we consider the construction of a SVIR (Susceptible, Vaccinated, Infected, Recovered) stochastic compartmental model of measles. We prove that the deterministic solution is asymptotically the average of the stochastic solution in the case of small population size. The choice of this model takes into account the random fluctuations inherent to the epidemiological characteristics of rural populations of Niger, notably a high prevalence of measles in children under 5, coupled with a very low immunization coverage.展开更多
This paper presents a new modified SIR model which incorporates appropriate delay parameters leading to a more precise prediction of COVID-19 real time data. The efficacy of the newly developed SIR model is proven by ...This paper presents a new modified SIR model which incorporates appropriate delay parameters leading to a more precise prediction of COVID-19 real time data. The efficacy of the newly developed SIR model is proven by comparing its predictions to real data obtained from four counties namely Germany, Italy, Kuwait, and Oman. Two included delay periods for incubation and recovery within the SIR model produce a sensible and more accurate representation of the real time data. In the absence of the two-delay period (<img src="Edit_8ce6d5c5-9b59-4640-9c0e-334e3948d11c.png" width="67" height="20" alt="" /><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">)</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> the dynamical behavior of the model will not correspond to today’s picture and lag the detection of the epidemic peak. The reproductive number <i></i></span></span></span><i><span><span><i><span style="font-family:Verdana;">R</span></i></span></span><span><span><span style="font-family:;" "=""><i><span style="font-family:Verdana;"><sub>0</sub></span></i><span style="font-family:Verdana;"></span></span></span></span></i> <span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">is defined for the model for values of recovery time delay <i></i></span></span></span><i><span style="font-family:Verdana;"><span style="font-family:Verdana;"><i><span style="font-family:Verdana;"><img src="Edit_882b068a-f7fa-478e-9fb9-4d78388010f3.png" width="25" height="20" alt="" /></span></i></span></span></i><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><sub></sub></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> of the infective case. The effect of recovery time <img src="Edit_882b068a-f7fa-478e-9fb9-4d78388010f3.png" width="25" height="20" alt="" /></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">may produce second wave, and/or an oscillation which could destabilize the behavior of the system and a periodic oscillation can arise due to Hopf bifurcation phenomenon.</span></span></span>展开更多
In this study, we investigate the dynamics of the COVID-19 epidemic in Northern Ireland from 1<sup>st</sup> March 2020 up to 25<sup>th</sup> December 2020, using sever</span><span>&...In this study, we investigate the dynamics of the COVID-19 epidemic in Northern Ireland from 1<sup>st</sup> March 2020 up to 25<sup>th</sup> December 2020, using sever</span><span><span style="font-family:Verdana;">al copies of a Susceptible-Exposed-Infectious-Recovered (<i></span><i><span style="font-family:Verdana;">SEIR</span></i><span style="font-family:Verdana;"></i>) compart</span></span><span style="font-family:Verdana;">mental model, and compare it to </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">a </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">detailed publicly available dataset. We split the data into 10 time intervals and fit the models on the consecutive intervals to the cumulative number of confirmed positive cases on each interval. Using the fitted parameter estimates, we also provide estimates of the reproduction number.</span><span style="font-family:Verdana;"> We also discuss the limitations and possible extensions of the employed model.展开更多
The two compartment model with variable extracellular volume is presented and solved by using both perturbation and analytical method. The computation for both creatinine and urea show that the perturbation solution ...The two compartment model with variable extracellular volume is presented and solved by using both perturbation and analytical method. The computation for both creatinine and urea show that the perturbation solution is not only simple but also accurate enough and is a good substitute for the more exact analytical solution.展开更多
Burundi, a country in East Africa with a temperate climate, has experienced in recent years a worrying growth of the Malaria epidemic. In this paper, a deterministic model of the transmission dynamics of malaria paras...Burundi, a country in East Africa with a temperate climate, has experienced in recent years a worrying growth of the Malaria epidemic. In this paper, a deterministic model of the transmission dynamics of malaria parasite in mosquito and human populations was formulated. The mathematical model was developed based on the SEIR model. An epidemiological threshold, <em>R</em><sub>0</sub>, called the basic reproduction number was calculated. The disease-free equilibrium point was locally asymptotically stable if <em>R</em><sub>0</sub> < 1 and unstable if <em>R</em><sub>0</sub> > 1. Using a Lyapunov function, we proved that this disease-free equilibrium point was globally asymptotically stable whenever the basic reproduction number is less than unity. The existence and uniqueness of endemic equilibrium were examined. With the Lyapunov function, we proved also that the endemic equilibrium is globally asymptotically stable if <em>R</em><sub>0</sub> > 1. Finally, the system of equations was solved numerically according to Burundi’s data on malaria. The result from our model shows that, in order to reduce the spread of Malaria in Burundi, the number of mosquito bites on human per unit of time (<em>σ</em>), the vector population of mosquitoes (<em>N<sub>v</sub></em>), the probability of being infected for a human bitten by an infectious mosquito per unit of time (<em>b</em>) and the probability of being infected for a mosquito per unit of time (<em>c</em>) must be reduced by applying optimal control measures.展开更多
A novel coronavirus disease (COVID-19) is an infectious viral disease caused by SARS-CoV-2. The disease was first reported in Wuhan, China, in December 2019, and it has been epidemic in more than 110 countries. The fi...A novel coronavirus disease (COVID-19) is an infectious viral disease caused by SARS-CoV-2. The disease was first reported in Wuhan, China, in December 2019, and it has been epidemic in more than 110 countries. The first case of COVID-19 was found in Nepal on 23 January, 2020. Now the number of confirmed cases is increasing day by day. Thus, the disease has become a major public health concern in Nepal. The propose of this study is to describe the development of outbreak of the disease and to predict the outbreak in Nepal. In the present work, the transmission dynamics of the disease in Nepal is analyzed mathematically with the help of SIR compartmental model. Reported data from June 1<sup>st</sup> to June 17<sup>th</sup> 2020 of Nepal are used to identify the model parameters. The basic reproduction number of COVID-19 outbreak in Nepal is estimated. Predictions of the peak epidemic time and the final size of the epidemic are made using the model. Our work predicts that, after 125 days from June 1 the infection will reach the peak. In this work, a good correlation between the reported data and the estimation given by our model is observed.展开更多
Published clinical data of Prazosin were reevaluated pharmacokinetically using explicit solutions to drug concentration as a function of total time for IV bolus injection, intermittent intravenous infusion and oral ro...Published clinical data of Prazosin were reevaluated pharmacokinetically using explicit solutions to drug concentration as a function of total time for IV bolus injection, intermittent intravenous infusion and oral routes of administration in an open two-compartment model. In a novel way, the apparent volume of distribution was estimated from a two-compartment model and found to be close to the total body water suggesting that Prazosin is distributed in all tissues both extracellularly and intracellularly. In addition, extracting the value of the apparent volume of distribution from a two-compartment model allowed comparative simulations in the one-compartment model. It is shown that dosage calculations of Prazosin intermittent infusion can be safely performed using the simpler one-compartment model equations. Lastly, several additional time-dependent pharmacokinetic parameters e.g., the peak time in the central and peripheral compartment and non-steady state and steady state peak concentration and AUC were determined using series equations for all three routes of administration, as a function of dose number and total time upon multiple drug administrations in the two-compartment model. It is also the first time that steady-state plasma drug concentration equations were derived in a two-compartment mammillary model.展开更多
Pharmacokinetic compartment models are the only models that can extract pharmacokinetic parameters from data collected in clinical studies but their estimates lack accuracy, explanations and physiological significance...Pharmacokinetic compartment models are the only models that can extract pharmacokinetic parameters from data collected in clinical studies but their estimates lack accuracy, explanations and physiological significance. The objective of this work was to develop particular solutions to drug concentration and AUC in the form of mathematical series and Heaviside functions for repetitive intermittent infusions in the one- and two-compartment models, as a function of dose number and total time using differential calculus. It was demonstrated that the central and peripheral compartment volumes determined from regression analysis of the aminoglycoside antibiotic Sisomicin concentration in plasma represent the actual physiological body fluid volumes accessible by the drug. The drug peak time and peak concentration in the peripheral compartment were also calculated as a function of dose number. It is also shown that the time of intercompartmental momentary distribution equilibrium can be used to determine the drug’s apparent volume of distribution within any dosing interval in multi-compartment models. These estimates were used to carry out simulations of plasma drug concentration with time in the one-compartment model. In conclusion, the two-compartment open mammillary pharmacokinetic model was fully explained for the aminoglycoside antibiotic sisomicin through the new concept of the apparent volume of distribution.展开更多
Nowadays, isotope environmental technique tends to be used as a reconnaissance tool , both qualitative and quantitative, to calculate the aquifer parameters particularly in carbonate rock aquifers. But, the hetero...Nowadays, isotope environmental technique tends to be used as a reconnaissance tool , both qualitative and quantitative, to calculate the aquifer parameters particularly in carbonate rock aquifers. But, the heterogeneous flow is still problematic when Lumped parameter Models are usually used to calculate the residence times and hydraulic parameters. However, Discrete State Compartment Model can provide a powerful model to heterogeneous medium. One such study was carried on in Dazha valley, where the environmental tritium was used as a tracer for determining hydrogeological parameters based on a discrete state compartment model展开更多
In this paper, we analyze the quasi-stationary distribution of the stochastic <em>SVIR</em> (Susceptible, Vaccinated, Infected, Recovered) model for the measles. The quasi-stationary distributions, as disc...In this paper, we analyze the quasi-stationary distribution of the stochastic <em>SVIR</em> (Susceptible, Vaccinated, Infected, Recovered) model for the measles. The quasi-stationary distributions, as discussed by Danoch and Seneta, have been used in biology to describe the steady state behaviour of population models which exhibit discernible stationarity before to become extinct. The stochastic <em>SVIR</em> model is a stochastic <em>SIR</em> (Susceptible, Infected, Recovered) model with vaccination and recruitment where the disease-free equilibrium is reached, regardless of the magnitude of the basic reproduction number. But the mean time until the absorption (the disease-free) can be very long. If we assume the effective reproduction number <em>R</em><em><sub>p</sub></em> < 1 or <img src="Edit_67da0b97-83f9-42ef-8a00-a13da2d59963.bmp" alt="" />, the quasi-stationary distribution can be closely approximated by geometric distribution. <em>β</em> and <em>δ</em> stands respectively, for the disease transmission coefficient and the natural rate.展开更多
This paper analyzed the material flow situation in argo-animal husbandry ecosystem by compartment model. This model was an important mean for investigating the whole structural characteristics in ecosystem. Based on t...This paper analyzed the material flow situation in argo-animal husbandry ecosystem by compartment model. This model was an important mean for investigating the whole structural characteristics in ecosystem. Based on this analysis, characteristics of material cycle and integrity in the system were mastered. As an example of natural conditions in Yonghe Village, Shuangcheng Township, Shuangeheng Municipal, Heilongjang Province, the system of linear differential equations in system was established by extracting each compartment and investigating material flow and stability of this model was proved by Lyapunov linear theory. The result showed that this system could not be interfered by initial value in the state of present, input and output.展开更多
The first biphasic open one-compartment pharmacokinetic model is described. Its analytical solutions to drug concentration were developed from parameters of an open two-compartment pharmacokinetic model. The model is ...The first biphasic open one-compartment pharmacokinetic model is described. Its analytical solutions to drug concentration were developed from parameters of an open two-compartment pharmacokinetic model. The model is used to explain the unusually large compartment volumes and apparent volumes of distribution of lipophilic drugs, as well as to identify which of the pharmacokinetic parameters of the classical compartment models are biologically relevant.展开更多
Purpose: To review some of the basic models, differential equations and solutions, both analytic and numerical, which produce time courses for the fractions of Susceptible (S), Infectious (I) and Recovered (R) fractio...Purpose: To review some of the basic models, differential equations and solutions, both analytic and numerical, which produce time courses for the fractions of Susceptible (S), Infectious (I) and Recovered (R) fractions of the population during the epidemic and/or endemic conditions. Methods: Two and three-compartment models with analytic solutions to the proposed linear differential equations as well as models based on the non-linear differential equations first proposed by Kermack and McKendrick (KM) [1] a century ago are considered. The equations reviewed include the ability to slide between so-called Susceptible-Infected-Recovered (SIR), Susceptible-Infectious-Susceptible (SIS), Susceptible-Infectious (SI) and Susceptible-Infectious-Recovered-Susceptible (SIRS) models, effectively moving from epidemic to endemic characterizations of infectious disease. Results: Both the linear and KM model yield typical “curves” of the infected fraction being sought “to flatten” with the effects of social distancing/masking efforts and/or pharmaceutical interventions. Demonstrative applications of the solutions to fit real COVID-19 data, including linear and KM SIR fit data from the first 100 days following “lockdown” in the authors’ locale and to the total number of cases in the USA over the course of 1 year with SI and SIS models are provided. Conclusions: COVID-19 took us all by surprise, all wondering how to help. Spreading a basic understanding of some of the mathematics used by epidemiologists to model infectious diseases seemed like a good place to start and served as the primary purpose for this tutorial.展开更多
A comprehensive mathematical framework modelling transmission dynamics of typhoid fever exists for Far North Cameroon where unsanitary conditions significantly exacerbate Salmonella Typhi spread rapidly.Analysis incor...A comprehensive mathematical framework modelling transmission dynamics of typhoid fever exists for Far North Cameroon where unsanitary conditions significantly exacerbate Salmonella Typhi spread rapidly.Analysis incorporates a deterministic model rooted in ordinary differential equations and a stochastic methodology factoring in uncertainties somewhat randomly.Dual modelling strategy highlights dominant role of water-related factors and climatic variables in shaping epidemic trajectory quite significantly over time.Seasonal disease pattern exhibits two pronounced incidence peaks in April-May and July-August corresponding respectively to drinking water scarcity periods and increased surface runoff facilitating pathogen dissemination.Advanced Bayesian techniques particularly Markov Chain Monte Carlo algorithm and variational inference enable estimation of key epidemiological parameters accurately with Markov processes.Analysis reveals that the basic reproduction number exceeds epidemic threshold during critical periods remarkably often under certain conditions.Simulations of multiple scenarios pretty effectively demonstrate efficacy of targeted control measures like vaccination programs and public awareness crusades nationwide.Such interventions drastically curtail transmission rates and stabilise epidemic trends somewhat effectively meanwhile.Findings contribute valuable insights into epidemiological dynamics of typhoid fever amidst climate variability and offer a robust foundation for public health risk management strategies.Strategic integration of real-time epidemiological data and water-quality surveillance systems holds great promise for enhancing sustainable control of this nasty waterborne disease.展开更多
Background The World Health Organization(WHO)targets a 65%reduction in hepatitis B-related deaths by 2030 compared to 2015 to eliminate viral hepatitis as a major public health threat.It is unknown whether and how Chi...Background The World Health Organization(WHO)targets a 65%reduction in hepatitis B-related deaths by 2030 compared to 2015 to eliminate viral hepatitis as a major public health threat.It is unknown whether and how China can achieve this target despite significant intervention achievements.We aimed to predict the hepatitis B-related deaths in China and identify key developments needed to achieve the target.Methods An age-and time-dependent dynamic hepatitis B virus(HBV)transmission compartmental model was developed to predict the trend of hepatitis B-related deaths under base-case and subsequent scenarios from 2015 to 2040.In base-case scenario,we assumed the diagnosis and treatment(D&T)rate would reach 72%in 2030,as proposed by WHO.Subsequent scenarios were set based on the results of base-case and one-way sensitivity analysis.Results Compared with 2015,hepatitis B-related deaths would be reduced by 23.89%in 2030 and 51.79%in 2040,respectively,and the WHO's impact target of 65%reduction would not be achieved until 2038 at the earliest under base-case scenario.HBV clearance rate and current treatment effectiveness were the most sensitive parameters that significantly influenced the decline of hepatitis B-related deaths from 2015 to 2040.In the subsequent scenario,when D&T rate improving to 90%by 2030,with the current treatment effectiveness and HBV clearance rate being optimized from 2016,the WHO's impact target would be achieved in 2038.Increasing the clearance rate further from 2%to 2.8%during 2016–2030 linearly,the impact target would be achieved on time.Conclusions It is difficult for China to achieve the WHO's impact target of 65%reduction in hepatitis B-related deaths by 2030 even we assumed the D&T rate would reach 72%in 2030 and beyond.A comprehensive scale-up of available strategies,especially innovative drugs and technologies will ensure that China achieves the target on schedule.展开更多
基金the funding from the New Frontiers in Research Fund(NFRF)2022 Special Call e Research for Postpandemic Recovery(Grant no:NFRFR-2022-00395).
文摘We consider state and parameter estimation for compartmental models having both timevarying and time-invariant parameters.In this manuscript,we first detail a general Bayesian computational framework as a continuation of our previous work.Subsequently,this framework is specifically tailored to the susceptible-infectious-removed(SIR)model which describes a basic mechanism for the spread of infectious diseases through a system of coupled nonlinear differential equations.The SIR model consists of three states,namely,the susceptible,infectious,and removed compartments.The coupling among these states is controlled by two parameters,the infection rate and the recovery rate.The simplicity of the SIR model and similar compartmental models make them applicable to many classes of infectious diseases.However,the combined assumption of a deterministic model and time-invariance among the model parameters are two significant impediments which critically limit their use for long-term predictions.The tendency of certain model parameters to vary in time due to seasonal trends,non-pharmaceutical interventions,and other random effects necessitates a model that structurally permits the incorporation of such time-varying effects.Complementary to this,is the need for a robust mechanism for the estimation of the parameters of the resulting model from data.To this end,we consider an augmented state vector,which appends the time-varying parameters to the original system states whereby the time evolution of the time-varying parameters are driven by an artificial noise process in a standard manner.Distinguishing between time-varying and time-invariant parameters in this fashion limits the introduction of artificial dynamics into the system,and provides a robust,fully Bayesian approach for estimating the timeinvariant system parameters as well as the elements of the process noise covariance matrix.This computational framework is implemented by leveraging the robustness of the Markov chain Monte Carlo algorithm permits the estimation of time-invariant parameters while nested nonlinear filters concurrently perform the joint estimation of the system states and time-varying parameters.We demonstrate performance of the framework by first considering a series of examples using synthetic data,followed by an exposition on public health data collected in the province of Ontario.
基金DK was partially supported by grants from CONICET, ANPCyT and SECYTUNC.
文摘This paper presents a compartmental model for bacterial infections in a population distributed over a network of individuals.Within each node,individuals interact,bacteria can be transmitted and the disease may be spread;moreover,the acquisition of bacterial antibiotic resistance is considered.In addition,nodes are connected through weighted edges,and consequently individuals from different nodes may interact.As a result,the infection may be propagated over the network.We perform an analysis on this propagation as well as numerical simulations in order to illustrate the validity of the model.
文摘This paper presents a new hybrid compartmental model for studying the COVID-19 epidemic evolution in Italy since the beginning of the vaccination campaign started on 2020/12/27 and shows forecasts of the epidemic evolution in Italy in the first six months.The proposed compartmental model subdivides the population into six compartments and extends the SEIRD model proposed in[E.L.Piccolomini and F.Zama,PLOS ONE,15(8):1e17,082020]by adding the vaccinated population and framing the global model as a hybridswitched dynamical system.Aiming to represent the quantities that characterize the epidemic behaviour from an accurate fit to the observed data,we partition the observation time interval into sub-intervals.The model parameters change according to a switching rule depending on the data behaviour and the infection rate continuity condition.In particular,we study the representation of the infection rate both as linear and exponential piecewise continuous functions.We choose the length of sub-intervals balancing the data fit with the model complexity through the Bayesian Information Criterion.We tested the model on italian data and on local data from Emilia-Romagna region.The calibration of the model shows an excellent representation of the epidemic behaviour in both cases.Thirty days forecasts have proven to well reproduce the infection spread,better for regional than for national data.Both models produce accurate predictions of infected,but the exponential-based one perform better in most of the cases.Finally,we discuss different possible forecast scenarios obtained by simulating an increased vaccination rate.
基金supported by the National Natural Science Foundation of China(Grant Nos.82173620 to Yang Zhao and 82041024 to Feng Chen)partially supported by the Bill&Melinda Gates Foundation(Grant No.INV-006371 to Feng Chen)Priority Academic Program Development of Jiangsu Higher Education Institutions.
文摘Deterministic compartment models(CMs)and stochastic models,including stochastic CMs and agent-based models,are widely utilized in epidemic modeling.However,the relationship between CMs and their corresponding stochastic models is not well understood.The present study aimed to address this gap by conducting a comparative study using the susceptible,exposed,infectious,and recovered(SEIR)model and its extended CMs from the coronavirus disease 2019 modeling literature.We demonstrated the equivalence of the numerical solution of CMs using the Euler scheme and their stochastic counterparts through theoretical analysis and simulations.Based on this equivalence,we proposed an efficient model calibration method that could replicate the exact solution of CMs in the corresponding stochastic models through parameter adjustment.The advancement in calibration techniques enhanced the accuracy of stochastic modeling in capturing the dynamics of epidemics.However,it should be noted that discrete-time stochastic models cannot perfectly reproduce the exact solution of continuous-time CMs.Additionally,we proposed a new stochastic compartment and agent mixed model as an alternative to agent-based models for large-scale population simulations with a limited number of agents.This model offered a balance between computational efficiency and accuracy.The results of this research contributed to the comparison and unification of deterministic CMs and stochastic models in epidemic modeling.Furthermore,the results had implications for the development of hybrid models that integrated the strengths of both frameworks.Overall,the present study has provided valuable epidemic modeling techniques and their practical applications for understanding and controlling the spread of infectious diseases.
文摘This study presents a mathematical modelling approach to analyze the impact of family planning interventions on population growth dynamics.Using a compartmental model,the population is divided into six groups:Susceptible,Informed,Sexually Active Non-Users,Contraceptive Users,Non-Users and General Population.The model incorporates differential equations to describe transitions among these compartments,influenced by factors such as sexual behavior,contraceptive adoption,and public health education.Analytical techniques,including equilibrium analysis and the computation of the basic reproductive number were used to evaluate the model’s behavior and stability.Numerical simulations conducted in MATLAB revealed that increased contraceptive usage and awareness significantly reduce the number of high-risk individuals while stabilizing overall population growth.The reproductive number was shown to decrease as contraceptive uptake increased,confirming the effectiveness of intervention strategies.The findings highlight the importance of reproductive health education and contraceptive access in managing population growth,providing valuable insights for policymakers and public health planners.This study demonstrates the potential of mathematical modelling as a predictive and policy-support tool in reproductive health and demographic planning.
文摘In this article, we consider the construction of a SVIR (Susceptible, Vaccinated, Infected, Recovered) stochastic compartmental model of measles. We prove that the deterministic solution is asymptotically the average of the stochastic solution in the case of small population size. The choice of this model takes into account the random fluctuations inherent to the epidemiological characteristics of rural populations of Niger, notably a high prevalence of measles in children under 5, coupled with a very low immunization coverage.
文摘This paper presents a new modified SIR model which incorporates appropriate delay parameters leading to a more precise prediction of COVID-19 real time data. The efficacy of the newly developed SIR model is proven by comparing its predictions to real data obtained from four counties namely Germany, Italy, Kuwait, and Oman. Two included delay periods for incubation and recovery within the SIR model produce a sensible and more accurate representation of the real time data. In the absence of the two-delay period (<img src="Edit_8ce6d5c5-9b59-4640-9c0e-334e3948d11c.png" width="67" height="20" alt="" /><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">)</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> the dynamical behavior of the model will not correspond to today’s picture and lag the detection of the epidemic peak. The reproductive number <i></i></span></span></span><i><span><span><i><span style="font-family:Verdana;">R</span></i></span></span><span><span><span style="font-family:;" "=""><i><span style="font-family:Verdana;"><sub>0</sub></span></i><span style="font-family:Verdana;"></span></span></span></span></i> <span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">is defined for the model for values of recovery time delay <i></i></span></span></span><i><span style="font-family:Verdana;"><span style="font-family:Verdana;"><i><span style="font-family:Verdana;"><img src="Edit_882b068a-f7fa-478e-9fb9-4d78388010f3.png" width="25" height="20" alt="" /></span></i></span></span></i><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><sub></sub></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> of the infective case. The effect of recovery time <img src="Edit_882b068a-f7fa-478e-9fb9-4d78388010f3.png" width="25" height="20" alt="" /></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">may produce second wave, and/or an oscillation which could destabilize the behavior of the system and a periodic oscillation can arise due to Hopf bifurcation phenomenon.</span></span></span>
文摘In this study, we investigate the dynamics of the COVID-19 epidemic in Northern Ireland from 1<sup>st</sup> March 2020 up to 25<sup>th</sup> December 2020, using sever</span><span><span style="font-family:Verdana;">al copies of a Susceptible-Exposed-Infectious-Recovered (<i></span><i><span style="font-family:Verdana;">SEIR</span></i><span style="font-family:Verdana;"></i>) compart</span></span><span style="font-family:Verdana;">mental model, and compare it to </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">a </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">detailed publicly available dataset. We split the data into 10 time intervals and fit the models on the consecutive intervals to the cumulative number of confirmed positive cases on each interval. Using the fitted parameter estimates, we also provide estimates of the reproduction number.</span><span style="font-family:Verdana;"> We also discuss the limitations and possible extensions of the employed model.
文摘The two compartment model with variable extracellular volume is presented and solved by using both perturbation and analytical method. The computation for both creatinine and urea show that the perturbation solution is not only simple but also accurate enough and is a good substitute for the more exact analytical solution.
文摘Burundi, a country in East Africa with a temperate climate, has experienced in recent years a worrying growth of the Malaria epidemic. In this paper, a deterministic model of the transmission dynamics of malaria parasite in mosquito and human populations was formulated. The mathematical model was developed based on the SEIR model. An epidemiological threshold, <em>R</em><sub>0</sub>, called the basic reproduction number was calculated. The disease-free equilibrium point was locally asymptotically stable if <em>R</em><sub>0</sub> < 1 and unstable if <em>R</em><sub>0</sub> > 1. Using a Lyapunov function, we proved that this disease-free equilibrium point was globally asymptotically stable whenever the basic reproduction number is less than unity. The existence and uniqueness of endemic equilibrium were examined. With the Lyapunov function, we proved also that the endemic equilibrium is globally asymptotically stable if <em>R</em><sub>0</sub> > 1. Finally, the system of equations was solved numerically according to Burundi’s data on malaria. The result from our model shows that, in order to reduce the spread of Malaria in Burundi, the number of mosquito bites on human per unit of time (<em>σ</em>), the vector population of mosquitoes (<em>N<sub>v</sub></em>), the probability of being infected for a human bitten by an infectious mosquito per unit of time (<em>b</em>) and the probability of being infected for a mosquito per unit of time (<em>c</em>) must be reduced by applying optimal control measures.
文摘A novel coronavirus disease (COVID-19) is an infectious viral disease caused by SARS-CoV-2. The disease was first reported in Wuhan, China, in December 2019, and it has been epidemic in more than 110 countries. The first case of COVID-19 was found in Nepal on 23 January, 2020. Now the number of confirmed cases is increasing day by day. Thus, the disease has become a major public health concern in Nepal. The propose of this study is to describe the development of outbreak of the disease and to predict the outbreak in Nepal. In the present work, the transmission dynamics of the disease in Nepal is analyzed mathematically with the help of SIR compartmental model. Reported data from June 1<sup>st</sup> to June 17<sup>th</sup> 2020 of Nepal are used to identify the model parameters. The basic reproduction number of COVID-19 outbreak in Nepal is estimated. Predictions of the peak epidemic time and the final size of the epidemic are made using the model. Our work predicts that, after 125 days from June 1 the infection will reach the peak. In this work, a good correlation between the reported data and the estimation given by our model is observed.
文摘Published clinical data of Prazosin were reevaluated pharmacokinetically using explicit solutions to drug concentration as a function of total time for IV bolus injection, intermittent intravenous infusion and oral routes of administration in an open two-compartment model. In a novel way, the apparent volume of distribution was estimated from a two-compartment model and found to be close to the total body water suggesting that Prazosin is distributed in all tissues both extracellularly and intracellularly. In addition, extracting the value of the apparent volume of distribution from a two-compartment model allowed comparative simulations in the one-compartment model. It is shown that dosage calculations of Prazosin intermittent infusion can be safely performed using the simpler one-compartment model equations. Lastly, several additional time-dependent pharmacokinetic parameters e.g., the peak time in the central and peripheral compartment and non-steady state and steady state peak concentration and AUC were determined using series equations for all three routes of administration, as a function of dose number and total time upon multiple drug administrations in the two-compartment model. It is also the first time that steady-state plasma drug concentration equations were derived in a two-compartment mammillary model.
文摘Pharmacokinetic compartment models are the only models that can extract pharmacokinetic parameters from data collected in clinical studies but their estimates lack accuracy, explanations and physiological significance. The objective of this work was to develop particular solutions to drug concentration and AUC in the form of mathematical series and Heaviside functions for repetitive intermittent infusions in the one- and two-compartment models, as a function of dose number and total time using differential calculus. It was demonstrated that the central and peripheral compartment volumes determined from regression analysis of the aminoglycoside antibiotic Sisomicin concentration in plasma represent the actual physiological body fluid volumes accessible by the drug. The drug peak time and peak concentration in the peripheral compartment were also calculated as a function of dose number. It is also shown that the time of intercompartmental momentary distribution equilibrium can be used to determine the drug’s apparent volume of distribution within any dosing interval in multi-compartment models. These estimates were used to carry out simulations of plasma drug concentration with time in the one-compartment model. In conclusion, the two-compartment open mammillary pharmacokinetic model was fully explained for the aminoglycoside antibiotic sisomicin through the new concept of the apparent volume of distribution.
文摘Nowadays, isotope environmental technique tends to be used as a reconnaissance tool , both qualitative and quantitative, to calculate the aquifer parameters particularly in carbonate rock aquifers. But, the heterogeneous flow is still problematic when Lumped parameter Models are usually used to calculate the residence times and hydraulic parameters. However, Discrete State Compartment Model can provide a powerful model to heterogeneous medium. One such study was carried on in Dazha valley, where the environmental tritium was used as a tracer for determining hydrogeological parameters based on a discrete state compartment model
文摘In this paper, we analyze the quasi-stationary distribution of the stochastic <em>SVIR</em> (Susceptible, Vaccinated, Infected, Recovered) model for the measles. The quasi-stationary distributions, as discussed by Danoch and Seneta, have been used in biology to describe the steady state behaviour of population models which exhibit discernible stationarity before to become extinct. The stochastic <em>SVIR</em> model is a stochastic <em>SIR</em> (Susceptible, Infected, Recovered) model with vaccination and recruitment where the disease-free equilibrium is reached, regardless of the magnitude of the basic reproduction number. But the mean time until the absorption (the disease-free) can be very long. If we assume the effective reproduction number <em>R</em><em><sub>p</sub></em> < 1 or <img src="Edit_67da0b97-83f9-42ef-8a00-a13da2d59963.bmp" alt="" />, the quasi-stationary distribution can be closely approximated by geometric distribution. <em>β</em> and <em>δ</em> stands respectively, for the disease transmission coefficient and the natural rate.
文摘This paper analyzed the material flow situation in argo-animal husbandry ecosystem by compartment model. This model was an important mean for investigating the whole structural characteristics in ecosystem. Based on this analysis, characteristics of material cycle and integrity in the system were mastered. As an example of natural conditions in Yonghe Village, Shuangcheng Township, Shuangeheng Municipal, Heilongjang Province, the system of linear differential equations in system was established by extracting each compartment and investigating material flow and stability of this model was proved by Lyapunov linear theory. The result showed that this system could not be interfered by initial value in the state of present, input and output.
文摘The first biphasic open one-compartment pharmacokinetic model is described. Its analytical solutions to drug concentration were developed from parameters of an open two-compartment pharmacokinetic model. The model is used to explain the unusually large compartment volumes and apparent volumes of distribution of lipophilic drugs, as well as to identify which of the pharmacokinetic parameters of the classical compartment models are biologically relevant.
文摘Purpose: To review some of the basic models, differential equations and solutions, both analytic and numerical, which produce time courses for the fractions of Susceptible (S), Infectious (I) and Recovered (R) fractions of the population during the epidemic and/or endemic conditions. Methods: Two and three-compartment models with analytic solutions to the proposed linear differential equations as well as models based on the non-linear differential equations first proposed by Kermack and McKendrick (KM) [1] a century ago are considered. The equations reviewed include the ability to slide between so-called Susceptible-Infected-Recovered (SIR), Susceptible-Infectious-Susceptible (SIS), Susceptible-Infectious (SI) and Susceptible-Infectious-Recovered-Susceptible (SIRS) models, effectively moving from epidemic to endemic characterizations of infectious disease. Results: Both the linear and KM model yield typical “curves” of the infected fraction being sought “to flatten” with the effects of social distancing/masking efforts and/or pharmaceutical interventions. Demonstrative applications of the solutions to fit real COVID-19 data, including linear and KM SIR fit data from the first 100 days following “lockdown” in the authors’ locale and to the total number of cases in the USA over the course of 1 year with SI and SIS models are provided. Conclusions: COVID-19 took us all by surprise, all wondering how to help. Spreading a basic understanding of some of the mathematics used by epidemiologists to model infectious diseases seemed like a good place to start and served as the primary purpose for this tutorial.
文摘A comprehensive mathematical framework modelling transmission dynamics of typhoid fever exists for Far North Cameroon where unsanitary conditions significantly exacerbate Salmonella Typhi spread rapidly.Analysis incorporates a deterministic model rooted in ordinary differential equations and a stochastic methodology factoring in uncertainties somewhat randomly.Dual modelling strategy highlights dominant role of water-related factors and climatic variables in shaping epidemic trajectory quite significantly over time.Seasonal disease pattern exhibits two pronounced incidence peaks in April-May and July-August corresponding respectively to drinking water scarcity periods and increased surface runoff facilitating pathogen dissemination.Advanced Bayesian techniques particularly Markov Chain Monte Carlo algorithm and variational inference enable estimation of key epidemiological parameters accurately with Markov processes.Analysis reveals that the basic reproduction number exceeds epidemic threshold during critical periods remarkably often under certain conditions.Simulations of multiple scenarios pretty effectively demonstrate efficacy of targeted control measures like vaccination programs and public awareness crusades nationwide.Such interventions drastically curtail transmission rates and stabilise epidemic trends somewhat effectively meanwhile.Findings contribute valuable insights into epidemiological dynamics of typhoid fever amidst climate variability and offer a robust foundation for public health risk management strategies.Strategic integration of real-time epidemiological data and water-quality surveillance systems holds great promise for enhancing sustainable control of this nasty waterborne disease.
基金supported by the National Science and Technology Key Project of the Ministry of Science and Technology of the People’s Republic of China(No.2018ZX10721202).
文摘Background The World Health Organization(WHO)targets a 65%reduction in hepatitis B-related deaths by 2030 compared to 2015 to eliminate viral hepatitis as a major public health threat.It is unknown whether and how China can achieve this target despite significant intervention achievements.We aimed to predict the hepatitis B-related deaths in China and identify key developments needed to achieve the target.Methods An age-and time-dependent dynamic hepatitis B virus(HBV)transmission compartmental model was developed to predict the trend of hepatitis B-related deaths under base-case and subsequent scenarios from 2015 to 2040.In base-case scenario,we assumed the diagnosis and treatment(D&T)rate would reach 72%in 2030,as proposed by WHO.Subsequent scenarios were set based on the results of base-case and one-way sensitivity analysis.Results Compared with 2015,hepatitis B-related deaths would be reduced by 23.89%in 2030 and 51.79%in 2040,respectively,and the WHO's impact target of 65%reduction would not be achieved until 2038 at the earliest under base-case scenario.HBV clearance rate and current treatment effectiveness were the most sensitive parameters that significantly influenced the decline of hepatitis B-related deaths from 2015 to 2040.In the subsequent scenario,when D&T rate improving to 90%by 2030,with the current treatment effectiveness and HBV clearance rate being optimized from 2016,the WHO's impact target would be achieved in 2038.Increasing the clearance rate further from 2%to 2.8%during 2016–2030 linearly,the impact target would be achieved on time.Conclusions It is difficult for China to achieve the WHO's impact target of 65%reduction in hepatitis B-related deaths by 2030 even we assumed the D&T rate would reach 72%in 2030 and beyond.A comprehensive scale-up of available strategies,especially innovative drugs and technologies will ensure that China achieves the target on schedule.
