Evaluating infection risks in buses based on passengers' dynamic temporal and typical spatial scenarios : A case study of COVID-19
Copyright © 2024 Elsevier B.V. All rights reserved..
Conventional buses, as an indispensable part of the urban public transport system, impose cross-infection risks on passengers. To assess differential risks due to dynamic staying durations and locations, this study considered four spatial distributions (i = 1-4) and six temporal scenarios (j = 1-6) of buses. Based on field measurements and a risk assessment approach combining both short-range and room-scale effects, risks are evaluated properly. The results showed that temporal asynchrony between infected and susceptible individuals significantly affects disease transmission rates. The Control Case assumes that infected and susceptible individuals enter and leave synchronously. However, ignoring temporal asynchrony scenarios, i.e., the Control Case, resulted in overestimation (+30.7 % to +99.6 %) or underestimation (-15.2 % to -69.9 %) of the actual risk. Moreover, the relative difference ratios of room-scale risks between the Control Case and five temporal scenarios are impacted by ventilation. Short-range risk exists only if infected and susceptible individuals have temporal overlap on the bus. Considering temporal and spatial asynchrony, a more realistic total reproduction number (R) can be obtained. Subsequently, the total R was assessed under five temporal scenarios. On average, for the Control Case, the total R was estimated to be +27.3 % higher than j = 1, -9.3 % lower than j = 2, +12.8 % higher than j = 3, +33.0 % lower than j = 4, and + 77.6 % higher than j = 5. This implies the need for a combination of active prevention and real-time risk monitoring to enable rigid travel demand and control the spread of the epidemic.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:922 |
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| Enthalten in: |
The Science of the total environment - 922(2024) vom: 20. März, Seite 171373 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Feng, Yinshuai [VerfasserIn] |
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| Links: |
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| Themen: |
Differential risk assessment |
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| Anmerkungen: |
Date Completed 20.03.2024 Date Revised 20.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.scitotenv.2024.171373 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM369184920 |
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| 520 | |a Copyright © 2024 Elsevier B.V. All rights reserved. | ||
| 520 | |a Conventional buses, as an indispensable part of the urban public transport system, impose cross-infection risks on passengers. To assess differential risks due to dynamic staying durations and locations, this study considered four spatial distributions (i = 1-4) and six temporal scenarios (j = 1-6) of buses. Based on field measurements and a risk assessment approach combining both short-range and room-scale effects, risks are evaluated properly. The results showed that temporal asynchrony between infected and susceptible individuals significantly affects disease transmission rates. The Control Case assumes that infected and susceptible individuals enter and leave synchronously. However, ignoring temporal asynchrony scenarios, i.e., the Control Case, resulted in overestimation (+30.7 % to +99.6 %) or underestimation (-15.2 % to -69.9 %) of the actual risk. Moreover, the relative difference ratios of room-scale risks between the Control Case and five temporal scenarios are impacted by ventilation. Short-range risk exists only if infected and susceptible individuals have temporal overlap on the bus. Considering temporal and spatial asynchrony, a more realistic total reproduction number (R) can be obtained. Subsequently, the total R was assessed under five temporal scenarios. On average, for the Control Case, the total R was estimated to be +27.3 % higher than j = 1, -9.3 % lower than j = 2, +12.8 % higher than j = 3, +33.0 % lower than j = 4, and + 77.6 % higher than j = 5. This implies the need for a combination of active prevention and real-time risk monitoring to enable rigid travel demand and control the spread of the epidemic | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Differential risk assessment | |
| 650 | 4 | |a Disease transmission | |
| 650 | 4 | |a Public transportation | |
| 650 | 4 | |a Spatial variation | |
| 650 | 4 | |a Temporal asynchrony | |
| 700 | 1 | |a Luo, Xiaoyu |e verfasserin |4 aut | |
| 700 | 1 | |a Wei, Jianjian |e verfasserin |4 aut | |
| 700 | 1 | |a Fan, Yifan |e verfasserin |4 aut | |
| 700 | 1 | |a Ge, Jian |e verfasserin |4 aut | |
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