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Pathogen transfer through environment–host contact: an agent-based queueing theoretic framework

机译:通过环境-宿主接触进行病原体转移:基于主体的排队理论框架

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摘要

Queueing theory studies the properties of waiting queues and has been applied to investigate direct host-to-host transmitted disease dynamics, but its potential in modelling environmentally transmitted pathogens has not been fully explored. In this study, we provide a flexible and customizable queueing theory modelling framework with three major subroutines to study the in-hospital contact processes between environments and hosts and potential nosocomial pathogen transfer, where environments are servers and hosts are customers. Two types of servers with different parameters but the same utilization are investigated. We consider various forms of transfer functions that map contact duration to the amount of pathogen transfer based on existing literature. We propose a case study of simulated in-hospital contact processes and apply stochastic queues to analyse the amount of pathogen transfer under different transfer functions, and assume that pathogen amount decreases during the inter-arrival time. Different host behaviour (feedback and non-feedback) as well as initial pathogen distribution (whether in environment and/or in hosts) are also considered and simulated. We assess pathogen transfer and circulation under these various conditions and highlight the importance of the nonlinear interactions among contact processes, transfer functions and pathogen demography during the contact process. Our modelling framework can be readily extended to more complicated queueing networks to simulate more realistic situations by adjusting parameters such as the number and type of servers and customers, and adding extra subroutines.
机译:排队论研究了等待队列的属性,并已被用于研究直接的宿主对宿主传播的疾病动态,但尚未充分探索其在环境传播的病原体建模中的潜力。在这项研究中,我们提供了一个灵活且可定制的排队理论建模框架,该框架包含三个主要子例程,用于研究环境与宿主之间的医院内接触过程以及潜在的医院病原体转移,其中环境是服务器,宿主是客户。研究了两种具有不同参数但利用率相同的服务器。我们根据现有文献考虑将接触持续时间映射到病原体转移量的各种转移函数形式。我们提出了一个模拟的医院内接触过程的案例研究,并应用随机队列来分析在不同传递函数下的病原体转移量,并假设病原体数量在到达间隔时间内减少。还考虑并模拟了不同的宿主行为(反馈和非反馈)以及初始病原体分布(无论在环境中和/或在宿主中)。我们评估了在这些不同条件下的病原体转移和循环,并强调了接触过程,转移函数和病原体人口统计之间的非线性相互作用的重要性。我们的建模框架可以轻松地扩展到更复杂的排队网络,以通过调整参数(例如服务器和客户的数量和类型,并添加额外的子例程)来模拟更现实的情况。

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