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

Abstract

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.

Metapopulation Model from Pathogen’s Perspective: A Versatile Framework to Quantify Pathogen Transfer and Circulation between Environment and Hosts

“…Based on the results from our modeling framework (i.e., maximum amount of pathogen in hosts at time t ), we can evaluate the real-time infection risk for each individual host in the system. Moreover, this versatile modeling framework can handle variable host population size through time (number of hosts N is not fixed, e.g., representing a healthcare setting with high fluidity of patient flow 17 ). Once between-host transfer dynamics and within-host infection dynamics are coupled, we can infer host’s epidemiological state and quantify potential pathogen shedding to further parameterize the pathogen transfer rate from host to environment λ HE .…”

“…Healthcare-associated infections (HAI, also known as nosocomial infections) are also facilitated by contacting surfaces and medical devices contaminated with pathogens, such as pathogenic Clostridium difficile 9,10 , Vancomycin-resistant Enterococci (VRE) 11,12 , and Methicillin-resistant Staphylococcus aureus (MRSA) 13,14 , and cause a tremendous amount of health and economic burden for society. Therefore, various novel modeling techniques have been developed and discussed to highlight the role of environment in infectious disease transmission especially HAIs 15–17 .…”

“…Since pathogens transfer between heterogeneous hosts and environment, the metapopulation model could be extended to serve as a more appropriate modeling framework for capturing the explicit dynamics of pathogen movement, replication, and decay. Unlike more detailed agent-based models, which completely rely on simulation and do not have an explicit analytical form 1,17 , metapopulation models remain mathematically concise—usually expressed as an array of ordinary differential equations—and may be solved analytically without intensive computation.…”

Patch dynamics modeling framework from pathogens’ perspective: Unified and standardized approach for complicated epidemic systems

“…This is indeed the ABM version of the SIR-type model because of the discretization (dichotomy of patch occupancy) of a continuous pathogen quantity. For pathogens that are difficult to quantify but relatively easy to detect (e.g., SARS-CoV-2 in wastewater), this occupancy model (or equivalently the ABM version of SIR-type model) will be preferred [ 33 ]. If the occupancy model is exclusively for multiple fixed environments, then it can also be regarded as a cellular automata (CA) model [ 49 ].…”

“…To address these challenges, we propose an alternative modeling perspective and framework that focus on pathogens directly [ 32 , 33 ]. Pathogens are, in fact, microorganisms in their own ecological niche [ 34 , 35 ].…”