Predicting the infection probability distribution of airborne and droplet transmissions

YAMAMOTO, Miguel; Kawamura, Akira; Tanabe, Shin‐ichi; Hori, Satoshi

doi:10.1177/1420326x221084869

Cited by 5 publications

(2 citation statements)

References 35 publications

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“…Yamamoto et al proposed a probabilistic mathematical model for the distribution of airborne and droplets to predict Covid-19 infection [ 22 ]. For the parameters needed they relied on known estimation methods, however no real measurement or images processing was performed.…”

Section: Related Workmentioning

confidence: 99%

Artificial intelligence tool for the study of COVID-19 microdroplet spread across the human diameter and airborne space

et al. 2023

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The 2019 novel coronavirus (SARS-CoV-2 / COVID-19), with a point of origin in Wuhan, China, has spread rapidly all over the world. It turned into a raging pandemic wrecking havoc on health care facilities, world economy and affecting everyone’s life to date. With every new variant, rate of transmission, spread of infections and the number of cases continues to rise at an international level and scale. There are limited reliable researches that study microdroplets spread and transmissions from human sneeze or cough in the airborne space. In this paper, we propose an intelligent technique to visualize, detect, measure the distance of spread in a real-world settings of microdroplet transmissions in airborne space, called “COVNET45”. In this paper, we investigate the microdroplet transmission and validate the measurements accuracy compared to published researches, by examining several microscopic and visual images taken to investigate the novel coronavirus (SARS-CoV-2 / COVID-19). The ultimate contribution is to calculate the spread of the microdroplets, measure it precisely and provide a graphical presentation. Additionally, the work employs machine learning and five algorithms for image optimization, detection and measurement.

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Section: Related Workmentioning

confidence: 99%

Artificial intelligence tool for the study of COVID-19 microdroplet spread across the human diameter and airborne space

et al. 2023

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“…9,10 Reasonable design of the location of air supply and exhaust outlets can optimize airflow distribution, which contributes to reducing the concentration of indoor contaminants. [11][12][13] Liu et al 14 studied the spatial and temporal distribution of bioaerosol particles under different mixed ventilation modes. They found that the removal efficiency of aerosol particles would be greatly improved if a directional airflow from the supply air inlet to the release source and then to the exhaust outlet could be formed in the ward.…”

Section: Introductionmentioning

confidence: 99%

Spatial and temporal distribution of bioaerosols produced by patients with different postures in a negative pressure isolation ward under different ventilation modes

Li,

Ma,

Liao

2024

Indoor and Built Environment

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The high concentration of viral bioaerosols within the negative pressure isolation wards could pose a challenge to preventing potential cross-infection amongst healthcare workers (HCWs) and patients. Using the Euler–Lagrange methodology, this study numerically simulated the spatial and temporal distribution characteristics of bioaerosols in a typical negative pressure isolation ward as well as determined the interaction of ventilation mode and patient posture on ward ventilation performance. The removal effect of particle groups produced by two respiratory behaviours (breathing and coughing) was quantitatively analyzed, and the effect of exhaust air ratio and air exchange rate on particle distribution was discussed. The results showed that the migration characteristics of bioaerosol particles were sensitive to both the ventilation pattern and patient posture, which showed significant interactions. On this basis, the ventilation pattern with the best ventilation performance was evaluated, showing a particle removal effect of 70–85%. Due to the initial momentum difference, the diffusion behaviour of cough and breath particles was not consistent, but optimizing the airflow distribution near the exhaust outlet could improve their removal efficiency in the meantime. Further studies found that equal exhaust air velocity ratio facilitated the removal of aerosol particles, and an appropriate increase in the air exchange rate could also reduce the particle content.

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Relating quanta conservation and compartmental epidemiological models of airborne disease outbreaks in buildings

Wood,

Craske,

Burridge

2023

Sci Rep

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We investigate the underlying assumptions and limits of applicability of several documented models for outbreaks of airborne disease inside buildings by showing how they may each be regarded as special cases of a system of equations which combines quanta conservation and compartmental epidemiological modelling. We investigate the behaviour of this system analytically, gaining insight to its behaviour at large time. We then investigate the characteristic timescales of an indoor outbreak, showing how the dilution rate of the space, and the quanta generation rate, incubation rate and removal rate associated with the illness may be used to predict the evolution of an outbreak over time, and may also be used to predict the relative performances of other indoor airborne outbreak models. The model is compared to a more commonly used model, in which it is assumed the environmental concentration of infectious aerosols adheres to a quasi-steady-state, so that the the dimensionless quanta concentration is equal to the the infectious fraction. The model presented here is shown to approach this limit exponentially to within an interval defined by the incubation and removal rates. This may be used to predict the maximum extent to which a case will deviate from the quasi steady state condition.

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Predicting the infection probability distribution of airborne and droplet transmissions

Cited by 5 publications

References 35 publications

Artificial intelligence tool for the study of COVID-19 microdroplet spread across the human diameter and airborne space

Artificial intelligence tool for the study of COVID-19 microdroplet spread across the human diameter and airborne space

Spatial and temporal distribution of bioaerosols produced by patients with different postures in a negative pressure isolation ward under different ventilation modes

Relating quanta conservation and compartmental epidemiological models of airborne disease outbreaks in buildings

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