A Markov chain model of particle deposition in the lung

Sonnenberg, Adam; Herrmann, Jacob; Grinstaff, Mark W.; Suki, Bélâ

doi:10.1038/s41598-020-70171-2

Cited by 14 publications

(8 citation statements)

References 26 publications

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“…The MPPD model uses semi-empirical relationships in the upper airway and solves flow and deposition in the lower respiratory tract made of cylindrical airways. Other approaches include stochastic models accounting for the asymmetry of the airway tree ( Koblinger and Hofmann, 1990 ) and more recently a model incorporating a Markov chain formulation of particle motion ( Sonnenberg et al, 2020 ).…”

Section: The Fate Of Inhaled Aerosols In the Respiratory Systemmentioning

confidence: 99%

Aerosol Transport Modeling: The Key Link Between Lung Infections of Individuals and Populations

Darquenne

Borojeni²,

Colebank³

et al. 2022

Front. Physiol.

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The recent COVID-19 pandemic has propelled the field of aerosol science to the forefront, particularly the central role of virus-laden respiratory droplets and aerosols. The pandemic has also highlighted the critical need, and value for, an information bridge between epidemiological models (that inform policymakers to develop public health responses) and within-host models (that inform the public and health care providers how individuals develop respiratory infections). Here, we review existing data and models of generation of respiratory droplets and aerosols, their exhalation and inhalation, and the fate of infectious droplet transport and deposition throughout the respiratory tract. We then articulate how aerosol transport modeling can serve as a bridge between and guide calibration of within-host and epidemiological models, forming a comprehensive tool to formulate and test hypotheses about respiratory tract exposure and infection within and between individuals.

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Section: The Fate Of Inhaled Aerosols In the Respiratory Systemmentioning

confidence: 99%

Aerosol Transport Modeling: The Key Link Between Lung Infections of Individuals and Populations

Darquenne

Borojeni²,

Colebank³

et al. 2022

Front. Physiol.

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“…Based on picking up physical construction information and establishing a virtual model, it is necessary to fuse the information of time and space dimensions and establish a data analysis model for the safety assessment. Markov chain [31] is a tool for the immediate transfer process and an important branch of machine learning. In this mode, the structural state of the next period is only related to the state of this period, while each period before this period is irrelevant.…”

Section: Space-time Information Fusion In Construction Processmentioning

confidence: 99%

Intelligent Safety Assessment of Prestressed Steel Structures Based on Digital Twins

et al. 2021

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In the development process of intelligent construction, the safety assessment of prestressed steel structures as an important research direction has become more and more attractive in academia. Digital twins (DTs) is the key technology to realize intelligent construction. The virtual and real interaction of the DTs can provide an efficient management and control mechanism for the construction process. This research proposes an intelligent safety assessment method of prestressed steel structures based on DTs. In this research method, the structural safety assessment is divided into two aspects: performance analysis and maintenance. By analyzing the characteristics of the construction safety assessment, a DTs framework for construction safety assessment is built. Driven by the DTs framework, a physical space model and a virtual space model are constructed. On the basis of virtual and actual interaction, multidimensional information fusion of time and space is carried out to realize the analysis of structural safety performance. On this basis, the paper establishes a Bow-tie model for the maintenance modeling of unsafe construction events. Moreover, the theoretical method formed is applied to the construction of a symmetrical structure (wheel–spoke cable truss). The validity of the method is verified by comparing the cable force calculated by the theoretical method and measured on site. The assessment method driven by the DTs ensures the structural safety and improves the intelligence level of safety management and control of the structure construction.

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“…Particulate matter has comprehensively been recognized as one of the major threats to human health (Kan et al 2007;Zhang et al 2021). Particles emitted from contaminant sources such as vehicles and biomass-burning may be inhaled by both adults and children (Buonanno et al 2013;Díaz-Robles et al 2014;Deng et al 2018), and further deposition can be easily observed in human respiratory systems (Lin et al 1994;Oravisjärvi et al 2011;Sonnenberg et al 2020). Since nowadays people spend near 90% of their lifetime indoors (Klepeis et al 2001), the personal exposure level of particulate pollutants is directly determined by the quality of air within buildings.…”

Section: Introductionmentioning

confidence: 99%

Predicting indoor particle dispersion under dynamic ventilation modes with high-order Markov chain model

2021

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Mechanical and natural ventilations are effective measures to remove indoor airborne contaminants, thereby creating improved indoor air quality (IAQ). Among various simulation techniques, Markov chain model is a relatively new and efficient method in predicting indoor airborne pollutants. The existing Markov chain model (for indoor airborne pollutants) is basically assumed as first-order, which however is difficult to deal with airborne particles with non-negligible inertial. In this study, a novel weight-factor-based high-order (second-order and third-order) Markov chain model is developed to simulate particle dispersion and deposition indoors under fixed and dynamic ventilation modes. Flow fields under various ventilation modes are solved by computational fluid dynamics (CFD) tools in advance, and then the basic first-order Markov chain model is implemented and validated by both simulation results and experimental data from literature. Furthermore, different groups of weight factors are tested to estimate appropriate weight factors for both second-order and third-order Markov chain models. Finally, the calculation process is properly designed and controlled, so that the proposed high-order (second-order) Markov chain model can be used to perform particle-phase simulation under consecutively changed ventilation modes. Results indicate that the proposed second-order model does well in predicting particle dispersion and deposition under fixed ventilation mode as well as consecutively changed ventilation modes. Compared with traditional first-order Markov chain model, the proposed high-order model performs with more reasonable accuracy but without significant computing cost increment. The most suitable weight factors of the simulation case in this study are found to be (λ1 = 0.7, λ2 = 0.3, λ3 = 0) for second-order Markov chain model, and (λ1 = 0.8, λ2 = 0.1, λ3 = 0.1) for third-order Markov chain model in terms of reducing errors in particle deposition and escape prediction. With the improvements of the efficiency of state transfer matrix construction and flow field data acquisition/processing, the proposed high-order Markov chain model is expected to provide an alternative choice for fast prediction of indoor airborne particulate (as well as gaseous) pollutants under transient flows.

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A Markov chain model of particle deposition in the lung

Cited by 14 publications

References 26 publications

Aerosol Transport Modeling: The Key Link Between Lung Infections of Individuals and Populations

Aerosol Transport Modeling: The Key Link Between Lung Infections of Individuals and Populations

Intelligent Safety Assessment of Prestressed Steel Structures Based on Digital Twins

Predicting indoor particle dispersion under dynamic ventilation modes with high-order Markov chain model

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