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Atamer Balkan B, Chang Y, Sparnaaij M, Wouda B, Boschma D, Liu Y, Yuan Y, Daamen W, de Jong MCM, Teberg C, Schachtschneider K, Sikkema RS, van Veen L, Duives D, ten Bosch QA. The multi-dimensional challenges of controlling respiratory virus transmission in indoor spaces: Insights from the linkage of a microscopic pedestrian simulation and SARS-CoV-2 transmission model. PLoS Comput Biol 2024; 20:e1011956. [PMID: 38547311 PMCID: PMC11003685 DOI: 10.1371/journal.pcbi.1011956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 04/09/2024] [Accepted: 02/29/2024] [Indexed: 04/11/2024] Open
Abstract
SARS-CoV-2 transmission in indoor spaces, where most infection events occur, depends on the types and duration of human interactions, among others. Understanding how these human behaviours interface with virus characteristics to drive pathogen transmission and dictate the outcomes of non-pharmaceutical interventions is important for the informed and safe use of indoor spaces. To better understand these complex interactions, we developed the Pedestrian Dynamics-Virus Spread model (PeDViS), an individual-based model that combines pedestrian behaviour models with virus spread models incorporating direct and indirect transmission routes. We explored the relationships between virus exposure and the duration, distance, respiratory behaviour, and environment in which interactions between infected and uninfected individuals took place and compared this to benchmark 'at risk' interactions (1.5 metres for 15 minutes). When considering aerosol transmission, individuals adhering to distancing measures may be at risk due to the buildup of airborne virus in the environment when infected individuals spend prolonged time indoors. In our restaurant case, guests seated at tables near infected individuals were at limited risk of infection but could, particularly in poorly ventilated places, experience risks that surpass that of benchmark interactions. Combining interventions that target different transmission routes can aid in accumulating impact, for instance by combining ventilation with face masks. The impact of such combined interventions depends on the relative importance of transmission routes, which is hard to disentangle and highly context dependent. This uncertainty should be considered when assessing transmission risks upon different types of human interactions in indoor spaces. We illustrated the multi-dimensionality of indoor SARS-CoV-2 transmission that emerges from the interplay of human behaviour and the spread of respiratory viruses. A modelling strategy that incorporates this in risk assessments can help inform policy makers and citizens on the safe use of indoor spaces with varying inter-human interactions.
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Affiliation(s)
- Büsra Atamer Balkan
- Quantitative Veterinary Epidemiology, Wageningen University & Research, Wageningen, The Netherlands
| | - You Chang
- Quantitative Veterinary Epidemiology, Wageningen University & Research, Wageningen, The Netherlands
| | - Martijn Sparnaaij
- Department of Transport & Planning, Delft University of Technology, Delft, The Netherlands
| | - Berend Wouda
- Gamelab, Delft University of Technology, Delft, The Netherlands
| | - Doris Boschma
- Gamelab, Delft University of Technology, Delft, The Netherlands
| | - Yangfan Liu
- Quantitative Veterinary Epidemiology, Wageningen University & Research, Wageningen, The Netherlands
| | - Yufei Yuan
- Department of Transport & Planning, Delft University of Technology, Delft, The Netherlands
| | - Winnie Daamen
- Department of Transport & Planning, Delft University of Technology, Delft, The Netherlands
| | - Mart C. M. de Jong
- Quantitative Veterinary Epidemiology, Wageningen University & Research, Wageningen, The Netherlands
| | - Colin Teberg
- Steady State Scientific Computing, Chicago, Illinois, United States of America
| | | | | | - Linda van Veen
- Gamelab, Delft University of Technology, Delft, The Netherlands
| | - Dorine Duives
- Department of Transport & Planning, Delft University of Technology, Delft, The Netherlands
| | - Quirine A. ten Bosch
- Quantitative Veterinary Epidemiology, Wageningen University & Research, Wageningen, The Netherlands
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A review on indoor airborne transmission of COVID-19– modelling and mitigation approaches. JOURNAL OF BUILDING ENGINEERING 2023; 64:105599. [PMCID: PMC9699823 DOI: 10.1016/j.jobe.2022.105599] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 11/11/2022] [Accepted: 11/21/2022] [Indexed: 06/09/2023]
Abstract
In the past few years, significant efforts have been made to investigate the transmission of COVID-19. This paper provides a review of the COVID-19 airborne transmission modeling and mitigation strategies. The simulation models here are classified into airborne transmission infectious risk models and numerical approaches for spatiotemporal airborne transmissions. Mathematical descriptions and assumptions on which these models have been based are discussed. Input data used in previous simulation studies to assess the dispersion of COVID-19 are extracted and reported. Moreover, measurements performed to study the COVID-19 airborne transmission within indoor environments are introduced to support validations for anticipated future modeling studies. Transmission mitigation strategies recommended in recent studies have been classified to include modifying occupancy and ventilation operations, using filters and air purifiers, installing ultraviolet (UV) air disinfection systems, and personal protection compliance, such as wearing masks and social distancing. The application of mitigation strategies to various building types, such as educational, office, public, residential, and hospital, is reviewed. Recommendations for future works are also discussed based on the current apparent knowledge gaps covering both modeling and mitigation approaches. Our findings show that different transmission mitigation measures were recommended for various indoor environments; however, there is no conclusive work reporting their combined effects on the level of mitigation that may be achieved. Moreover, further studies should be conducted to understand better the balance between approaches to mitigating the viral transmissions in buildings and building energy consumption.
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Cui Z, Cai M, Xiao Y, Zhu Z, Yang M, Chen G. Forecasting the transmission trends of respiratory infectious diseases with an exposure-risk-based model at the microscopic level. ENVIRONMENTAL RESEARCH 2022; 212:113428. [PMID: 35568232 PMCID: PMC9095069 DOI: 10.1016/j.envres.2022.113428] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 03/30/2022] [Accepted: 05/02/2022] [Indexed: 05/03/2023]
Abstract
Respiratory infectious diseases (e.g., COVID-19) have brought huge damages to human society, and the accurate prediction of their transmission trends is essential for both the health system and policymakers. Most related studies focus on epidemic trend forecasting at the macroscopic level, which ignores the microscopic social interactions among individuals. Meanwhile, current microscopic models are still not able to sufficiently decipher the individual-based spreading process and lack valid quantitative tests. To tackle these problems, we propose an exposure-risk-based model at the microscopic level, including 4 modules: individual movement, virion-laden droplet movement, individual exposure risk estimation, and prediction of transmission trends. Firstly, the front two modules reproduce the movements of individuals and the droplets of infectors' expiratory activities, respectively. Then, the outputs are fed to the third module to estimate the personal exposure risk. Finally, the number of new cases is predicted in the final module. By predicting the new COVID- 19 cases in the United States, the performances of our model and 4 other existing macroscopic or microscopic models are compared. Specifically, the mean absolute error, root mean square error, and mean absolute percentage error provided by the proposed model are respectively 2454.70, 3170.51, and 3.38% smaller than the minimum results of comparison models. The quantitative results reveal that our model can accurately predict the transmission trends from a microscopic perspective, and it can benefit the further investigation of many microscopic disease transmission factors (e.g., non-walkable areas and facility layouts).
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Affiliation(s)
- Ziwei Cui
- School of Intelligent System Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China.
| | - Ming Cai
- School of Intelligent System Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China.
| | - Yao Xiao
- School of Intelligent System Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China.
| | - Zheng Zhu
- College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Mofeng Yang
- Maryland Transportation Institute, Department of Civil and Environmental Engineering, University of Maryland at College Park, Maryland, USA.
| | - Gongbo Chen
- Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China.
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Lau Z, Griffiths IM, English A, Kaouri K. Predicting the spatio-temporal infection risk in indoor spaces using an efficient airborne transmission model. Proc Math Phys Eng Sci 2022; 478:20210383. [PMID: 35310953 PMCID: PMC8924953 DOI: 10.1098/rspa.2021.0383] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 02/14/2022] [Indexed: 12/19/2022] Open
Abstract
We develop a spatially dependent generalization to the Wells–Riley model, which determines the infection risk due to airborne transmission of viruses. We assume that the infectious aerosol concentration is governed by an advection–diffusion–reaction equation with the aerosols advected by airflow, diffused due to turbulence, emitted by infected people, and removed due to ventilation, inactivation of the virus and gravitational settling. We consider one asymptomatic or presymptomatic infectious person breathing or talking, with or without a mask, and model a quasi-three-dimensional set-up that incorporates a recirculating air-conditioning flow. We derive a semi-analytic solution that enables fast simulations and compare our predictions to three real-life case studies—a courtroom, a restaurant, and a hospital ward—demonstrating good agreement. We then generate predictions for the concentration and the infection risk in a classroom, for four different ventilation settings. We quantify the significant reduction in the concentration and the infection risk as ventilation improves, and derive appropriate power laws. The model can be easily updated for different parameter values and can be used to make predictions on the expected time taken to become infected, for any location, emission rate, and ventilation level. The results have direct applicability in mitigating the spread of the COVID-19 pandemic.
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Affiliation(s)
- Zechariah Lau
- School of Mathematics, Cardiff University, CF24 4AG Cardiff, UK.,Mathematical Institute, University of Oxford, OX1 6GG Oxford, UK
| | - Ian M Griffiths
- Mathematical Institute, University of Oxford, OX1 6GG Oxford, UK
| | - Aaron English
- School of Mathematics, Cardiff University, CF24 4AG Cardiff, UK.,Department of Mechanical, Aerospace and Civil Engineering, University of Manchester, M13 9PL Manchester, UK
| | - Katerina Kaouri
- School of Mathematics, Cardiff University, CF24 4AG Cardiff, UK
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