TY - JOUR
T1 - Pathogen transfer through environment-host contact
T2 - An agent-based queueing theoretic framework
AU - Chen, Shi
AU - Lenhart, Suzanne
AU - Day, Judy D.
AU - Lee, Chihoon
AU - Dulin, Michael
AU - Lanzas, Cristina
N1 - Publisher Copyright:
© The authors 2017. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.
PY - 2018/9/11
Y1 - 2018/9/11
N2 - Queueing theory studies the properties of waiting queues and has been applied to investigate direct hostto- 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.
AB - Queueing theory studies the properties of waiting queues and has been applied to investigate direct hostto- 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.
KW - Agent-based model
KW - Environment-host contact
KW - Health care contact process
KW - Model framework
KW - Pathogen transfer and decay
KW - Queueing theory
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U2 - 10.1093/imammb/dqx014
DO - 10.1093/imammb/dqx014
M3 - Article
C2 - 29106583
AN - SCOPUS:85054877703
SN - 1477-8599
VL - 35
SP - 409
EP - 425
JO - Mathematical Medicine and Biology
JF - Mathematical Medicine and Biology
IS - 3
ER -