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
Introduction:The cost-effectiveness of vaccination strategies plays a crucial role in managing infectious diseases such as influenza within public health systems.This study evaluated the cost-effectiveness of vaccinat...Introduction:The cost-effectiveness of vaccination strategies plays a crucial role in managing infectious diseases such as influenza within public health systems.This study evaluated the cost-effectiveness of vaccination compliance strategies by comparing an“adherence”strategy,which promoted continuous vaccination uptake,with a“volunteer”strategy through model-based simulations.Methods:We developed a novel hybrid model that integrates continuous-time agent-based models(ABMs)with a Markov model to simulate vaccination behaviors and disease dynamics at the individual level.The model incorporated socioeconomic factors,vaccine efficacy,and population interactions to evaluate the long-term health outcomes and associated costs of different vaccination compliance strategies.Results:Simulation results demonstrated that the“adherence”strategy significantly enhanced vaccination coverage and reduced influenza cases,yielding an incremental cost-effectiveness ratio(ICER)of 33,847 CNY per quality-adjusted life year(QALY)gained,indicating superior cost-effectiveness compared to the“volunteer”strategy.Discussion:Our findings support implementing targeted influenza vaccination compliance strategies,presenting an innovative approach to strengthening public health interventions and enhancing vaccination program effectiveness.The hybrid model shows promise in informing public health policy and practice,warranting further investigation of its applications across diverse public health contexts.展开更多
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.
基金Supported by the National Natural Science Foundation of China(Project Nos.82473732 to Fang Shao,82404383 to Mengyi Lu,82173620 and 82373690 to Yang Zhao,and 82204156 to Dongfang You)Additional funding was provided through the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)The study also received partial support from the Bill&Melinda Gates Foundation(Project No.INV-006371 to Feng Chen).
文摘Introduction:The cost-effectiveness of vaccination strategies plays a crucial role in managing infectious diseases such as influenza within public health systems.This study evaluated the cost-effectiveness of vaccination compliance strategies by comparing an“adherence”strategy,which promoted continuous vaccination uptake,with a“volunteer”strategy through model-based simulations.Methods:We developed a novel hybrid model that integrates continuous-time agent-based models(ABMs)with a Markov model to simulate vaccination behaviors and disease dynamics at the individual level.The model incorporated socioeconomic factors,vaccine efficacy,and population interactions to evaluate the long-term health outcomes and associated costs of different vaccination compliance strategies.Results:Simulation results demonstrated that the“adherence”strategy significantly enhanced vaccination coverage and reduced influenza cases,yielding an incremental cost-effectiveness ratio(ICER)of 33,847 CNY per quality-adjusted life year(QALY)gained,indicating superior cost-effectiveness compared to the“volunteer”strategy.Discussion:Our findings support implementing targeted influenza vaccination compliance strategies,presenting an innovative approach to strengthening public health interventions and enhancing vaccination program effectiveness.The hybrid model shows promise in informing public health policy and practice,warranting further investigation of its applications across diverse public health contexts.
