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. 2023 Apr 1:64:105706.
doi: 10.1016/j.jobe.2022.105706. Epub 2022 Dec 10.

Influences of obstacle factors on the transmission trends of respiratory infectious diseases in indoor public places

Affiliations

Influences of obstacle factors on the transmission trends of respiratory infectious diseases in indoor public places

Ziwei Cui et al. J Build Eng. .

Abstract

Public facilities are important transmission places for respiratory infectious diseases (e.g., COVID-19), due to the frequent crowd interactions inside. Usually, changes of obstacle factors can affect the movements of human crowds and result in different epidemic transmissions among individuals. However, most related studies only focus on the specific scenarios, but the common rules are usually ignored for the impacts of obstacles' spatial elements on epidemic transmission. To tackle these problems, this study aims to evaluate the impacts of three spatial factors of obstacles (i.e., size, quantity, and placement) on infection spreading trends in two-dimension, which can provide scientific and concise spatial design guidelines for indoor public places. Firstly, we used the obstacle area proportion as the indicator of the size factor, gave the mathematical expression of the quantity factor, and proposed the walkable-space distribution indicator to represent the placement factor by introducing the Space Syntax. Secondly, two spreading epidemic indicators (i.e., daily new cases and people's average exposure risk) were estimated based on the fundamental model named exposure risk with the virion-laden particles, which accurately forecasted the disease spreading between individuals. Thirdly, 120 indoor scenarios were built and simulated, based on which the value of independent and dependent variables can be measured. Besides, structural equation modeling was employed to examine the effects of obstacle factors on epidemic transmissions. Finally, three obstacle-related guidelines were provided for policymakers to mitigate the disease spreading: minimizing the size of obstacles, dividing the obstacle into more sub-ones, and placing obstacles evenly distributed in space.

Keywords: Indoor environment; Pedestrian-based epidemic spreading model; Space syntax; Spatial design; Structural equation modeling.

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Conflict of interest statement

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Yao Xiao reports financial support was provided by 10.13039/501100001809National Natural Science Foundation of China. Yao Xiao reports financial support was provided by Shenzhen Science and Technology Program. Yao Xiao reports financial support was provided by Fundamental Research Funds for the Central Universities, 10.13039/501100002402Sun Yat-sen University. Gongbo Chen reports financial support was provided by Basic and Applied Basic Research Project of Guangzhou Municipal Science and Technology Bureau.

Figures

Fig. 1
Fig. 1
There is the framework of our study.
Fig. 2
Fig. 2
There are examples of the (a) convex and (b) non-convex spaces.
Fig. 3
Fig. 3
In (a) a scenario, (b) there are four spaces that can be used as the first workable convex space in Hillier's method, and (c) a unique one can be determined as the first in our method. Then, a unique walkable convex map is obtained in (d) based on our method.
Fig. 4
Fig. 4
In (a) a scenario, the unique walkable convex map is obtained in (b) based on our method.
Fig. 5
Fig. 5
Diagram of the social force model.
Fig. 6
Fig. 6
(a) 0.40 s and (b) 2.00 s after the cough, particles are integrated on the y-z plane in the computational domain.
Fig. 7
Fig. 7
There is a sketch map of the simulation people and room in the case.
Fig. 8
Fig. 8
The division results are based on the horizontal-vertical division rule when Snon=36m2.
Fig. 9
Fig. 9
When Snon=36m2, Hcut=3, and Vcut=2, there are two layouts with different positions of obstacle.
Fig. 10
Fig. 10
There are 24 scenarios when SAllObs is 36 m2.
Fig. 11
Fig. 11
(a) CNew and (b) EAve vary with RAllObs and DCon; (c) CNew and (d) EAve change with NObs and DCon.
Fig. 12
Fig. 12
The hypothetical relations in (a) Model A and (b) Model B.
Fig. 13
Fig. 13
The analysis results of (a) Model A and (b) Model B. Note: *** p <0.01.
Fig. 14
Fig. 14
Different layouts with Dcut changes from 0.2 m to 6.0 m with a step of 0.2 m based on scenario #100- [1,2]-(0,0).
Fig. 15
Fig. 15
(a) CNew and (b) EAve change with the placement indicator DCon in 30 scenarios with different Dcut.
Fig. 16
Fig. 16
(a) CNew and (b) EAve vary with the total size of obstacles SObs.
Fig. 17
Fig. 17
When (a) an obstacle is divided into (b) two sub-ones, there are more overlapping areas between infection areas and obstacles.
Fig. 18
Fig. 18
The sketch map of the “door” linking adjacent spaces in the case of (a) Fig. 3 and (b) Fig. 4.
Fig. 19
Fig. 19
Environmental factors can be explored as potential independent variables in combination with the ERP model.
Fig. 20
Fig. 20
The hypothetical relations in (a) Model A and (b) Model B after adding environmental factors.
Fig. A1
Fig. A1
Schematic diagram of (a) the computational domain with infector and susceptible manikins, and (b) the numerical simulation computational domain. (reproduced from Ref. [10]).
Fig. B1
Fig. B1
There are scenarios with the obstacle size of 64 m2, 100 m2, 144 m2, and 196 m2.
Fig. B1
Fig. B1
There are scenarios with the obstacle size of 64 m2, 100 m2, 144 m2, and 196 m2.
Fig. B1
Fig. B1
There are scenarios with the obstacle size of 64 m2, 100 m2, 144 m2, and 196 m2.

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