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CHAPTER

Chapter 6. Summary and Future Work

6.1 Summary

The transmission and infection mechanism of viruses such as SARS-CoV-2 that can cause

respiratory diseases should not be underestimated, which is always dangerous to human health. The

mechanism of transmission is the result of the complex journey of virus-carrying droplets from the

indoor scale to the human body, and the mechanism of infection can expand from the human scale to

the cellular scale. To improve the accuracy of infection risk assessment predictions, we propose a

numerical framework that seamlessly estimates viral deposition and replication in humans by

combining CFPD in an indoor environment with a HCD model that simulates human respiratory tract

exposure and infection dynamics. Using SARS-CoV-2 as an example, CFPD was used to model the

distribution of stable and unsteady deposition of virus-containing droplets in the upper respiratory

tract. We combined it with the HCD model to predict the infection dynamics of SARS-CoV-2 in the

nasopharyngeal region. Further, based on mucociliary clearance movement to improve the HCD

model, the relevant parameters were optimized and the influencing factors were discussed, and the

process of upper respiratory tract virus infection was visualized.

The main body of the study is summarized as follows:

Chapter 1 provides a literature review, stating the research objectives and significance, and outlines

the overall structure of the paper. To gain a better understanding of the pathogenesis of SARS-CoV2 and visualize its infection process in the URT, the literature review collected clinical and

experimental data, introduced methods for analyzing particle deposition in the URT, and explained

the HCD model for describing virus dynamics.

Chapter 2 numerically analyzes the deposition distribution of virus-laden droplets released through

coughing by the infected person. This analysis serves as a solid foundation for establishing the initial

viral load distribution. Thus, this chapter assumes high-risk scenarios for respiratory infection and

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uses two CSPs to study the risk of droplet deposition in the URT during coughing and breathing

activities. After comparing and analyzing respiratory patterns, physical distance, and particle

evaporation conditions, the results considering a physical distance of 1m without droplet evaporation

are primarily used as one of the initial conditions for the visualization of viral dynamics in Chapter 4.

Chapter 3 introduces the concept of visualizing viral dynamics, providing a combination of CFPD

and HCD. Taking into account mucociliary clearance movement, this chapter proposes the

combination of the HCD model and a 3D-shell model with a mucus layer to study virus dynamics.

Based on a simple target cell-limited model, convection and diffusion terms are added to the CFD

calculation and visualization analysis as reference parameters. This chapter improves the HCD model

but finds a discrepancy between the infection rate and other parameters in the literature and clinical

data, indicating the need for further optimization of initial conditions and parameters to predict the

dynamics of SARS-CoV-2 in the URT.

Chapter 4 involves the parameter optimization of the HCD model to better visualize the infection

dynamics of SARS-CoV-2 based on the results from Chapters 2 and 3. Building upon the

characteristics of the URT's mucus layer, this chapter further improves the existing model from

Chapter 3 and proposes a multi-compartment model concept to describe virus dynamics in simplified

one-layer and two-layer mucus. Additionally, it uses SARS-CoV-2 human challenge data from

individuals who did not receive drug treatment and had a known virus inoculation date as a fitting

dataset, with parameter fitting performed using Monolix. The results demonstrate that by utilizing

parameters obtained from a two-layer, multi-compartment, low-velocity model, combined with the

CFD method and a 3D-shell model, the infection dynamics of SARS-CoV-2 in the nasal cavitynasopharynx region can be visualized, aiding in the understanding of certain nasal symptoms and

enabling targeted prevention and treatment of COVID-19. Furthermore, using this approach, the

deposition data of droplets at a physical distance of 2m from Chapter 2 were used for parameter fitting

and CFD calculations, but it was found that this method may not be applicable to smaller droplet

depositions. Therefore, the impact of the initial droplet distribution on the prediction results should

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be independently analyzed.

Chapter 5 analyzes the impact of droplet deposition distribution in the nasal cavity-nasopharynx

region on predictions of virus dynamics. To avoid randomness, this chapter uniformly generates an

equal number of infectious droplets throughout the entire geometric model, the vestibule, and the

nasopharynxl region as initial conditions for predicting the viral dynamics. It then discusses the

prerequisites to consider when using this method, as outlined in Chapter 4.

Indeed, these chapters are interconnected and progressively build upon each other with the aim of

accurately predicting the infection dynamics of SARS-CoV-2 in the nasal cavity-nasopharynx region.

By incorporating clinical and experimental data, analyzing particle deposition, and developing

computational models such as CFPD and HCD, the research strives to provide a comprehensive

understanding of how the virus spreads and infects the upper respiratory tract. This knowledge can

be instrumental in formulating targeted strategies for preventing and treating COVID-19 and other

respiratory illnesses.

6.2 Future work

At a crucial moment when the author was about to summarize this study, unfortunately, I was

infected with COVID-19. Symptoms such as nasal congestion, sore throat, cough, and fever were

inevitable, which further strengthened my interest in this research topic. The mechanism of viral

infection is complex and variable, and there is significant individual variation, which requires more

future work to address the limitations of the current research.

 Improve the analysis of independent initial conditions: Further investigate the influence of factors

such as initial viral load concentration thresholds on the infection dynamics. This will help

improve the accuracy and reliability of the model.

 Establish a numerical framework for studying the viral dynamics in the complete respiratory

tract: Expand the scope of the study to include the entire respiratory tract, including the upper

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and lower respiratory tracts such as the lungs. Precisely set boundary conditions based on the

direction of mucociliary clearance and the properties of mucus. Given the complexity of the

bronchial system in the lungs, this will require significant time and effort for in-depth research.

 Incorporate the immune responses: The immune response plays a critical role in the host's

interaction with viruses, impacting the course and outcome of viral infections. Understanding

and incorporating these factors into predictive models can enhance their accuracy and the efficacy

of devised strategies.

 Classify different respiratory viruses: Classify respiratory viruses according to their pathogenic

mechanisms and infectivity, not limited to just SARS-CoV-2. Consider studying viruses such as

influenza-A that can be self-resolving in a short period to obtain more general conclusions that

will contribute to the prevention and treatment of respiratory diseases.

 Explore non-spherical pathogens: If sufficient time and resources are available, focus on further

investigating non-spherical pathogens such as Mycobacterium tuberculosis (causing

tuberculosis). This would be an interesting research direction.

[Previously published documents related the present research]

[1] Hanyu Li, Kazuki Kuga, Kazuhide Ito. SARS-CoV-2 Dynamics in the Mucus Layer of the

Human Upper Respiratory Tract Based on Host-Cell Dynamics. Sustainability, 2022, 14 (7),

3896. (DOI: 10.3390/su14073896)

[2] Hanyu Li, Kazuki Kuga, Kazuhide Ito, Visual prediction and parameter optimization of viral

dynamics in the mucus milieu of the upper airway based on CFPD-HCD analysis, Computer

Methods and Programs in Biomedicine, 238 (2023) 107622.

(DOI:10.1016/j.cmpb.2023.107622)

[3] Takumi Nishihara, Hanyu Li, Kazuki Kuga, Kazuhide Ito. Seamless numerical analysis of

transient virus-laden droplet dispersion and resultant respiratory infection-in silico study.

Building and Environment. Submitted.

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