Agent-based simulation of pedestrian dynamics for exposure time estimation in epidemic risk assessment

Harweg, Thomas; Bachmann, Daniel; Weichert, Frank

doi:10.1007/s10389-021-01489-y

Cited by 39 publications

(38 citation statements)

References 32 publications

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“…These surfaces are 2D manifolds that have a topology that connects all the different floors of a building into one model. Agent-based simulations can be run on BIM-based sand-boxes for evacuation planning [Marzouk and Al Daour, 2018, Sun and Turkan, 2020, Xu et al, 2020 and epidemic risk assessment [Harweg et al, 2020].…”

Section: Related Workmentioning

confidence: 99%

Point Cloud Based 3d Models for Agent Based Simulations in Social Distancing and Evacuation

Nikoohemat

Godoy²,

Valkhoff³

et al. 2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Point clouds serve as the raw material for various models, such as Building Information Models (BIM). In this work, we investigate the reconstruction steps needed to create models that can be utilized directly for agent-based simulations. The input data for the reconstruction is captured with an indoor mobile mapping system. To show the prominence of this idea, we run social distancing and evacuation simulations on the reconstructed models. The simulations are run with multiple agents using a vision-based pedestrian model and A*-based path finding algorithm. The limitations of this approach are discussed. The video of the simulation is shared with the audience.Link to the video: https://youtu.be/r2D3IxXt7Ls

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Section: Related Workmentioning

confidence: 99%

Point Cloud Based 3d Models for Agent Based Simulations in Social Distancing and Evacuation

Nikoohemat

Godoy²,

Valkhoff³

et al. 2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…For the time being, there remains a gap between the thriving experimental and computational studies focused on measuring the emission and propagation of respiratory droplets ( Morawska et al, 2009 , Bourouiba et al, 2014 , Asadi et al, 2019 , Chen et al, 2020 , Abkarian et al, 2020 , Bourouiba, 2020 , Bao et al, 2020 , Li et al, 2020a , Feng et al, 2020 ) and the investigations of disease spread at larger scales ( Ferguson et al, 2020 , Ferretti et al, 2020 , Harweg et al, 2021 ). The former have shed light on the complex, turbulent dynamics that take place inside the exhaled puff and called into question both the binary distinction between falling droplets and tinier airborne aerosols ( Bourouiba, 2020 , Chong et al, 2021 ), and the scientific basis of the 2-meter social distancing rule ( Morawska and Milton, 2020 , Jones et al, 2020 , Yang et al, 2020 , Chong et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

“…However, the translation of these results into epidemiological predictions relevant for policy-making ( Vuorinen et al, 2020 , Poydenot et al, 2021 ) is uneasy, and trailing. Poles apart from these microscopic studies, risk assessments at the scale of a facility or venue by means of agent-based pedestrian simulations ( Xiao et al, 2021 , Harweg et al, 2021 , Romero et al, 2020 ) or large-scale experiments ( Moritz et al, 2021 ) resort to particularly crude assumptions with regard to viral transmission. Often, an individual is considered exposed to the disease when he or she comes in a given radius (e.g., 2 m) around an infected person, regardless of their orientations, overlooking that their head orientations control the direction in which respiratory droplets are expelled.…”

Section: Introductionmentioning

confidence: 99%

Model-based assessment of the risks of viral transmission in non-confined crowds

et al. 2021

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This work assesses the risks of Covid-19 spread in diverse daily-life situations involving crowds of maskless pedestrians, mostly outdoors. More concretely, we develop a method to infer the global number of new infections from patchy observations, by coupling ad hoc spatial models for disease transmission via respiratory droplets to detailed field-data about pedestrian trajectories and head orientations. This allows us to rank the investigated situations by the infection risks that they present; importantly, the obtained hierarchy of risks is very largely conserved across transmission models: Street cafés present the largest average rate of new infections caused by an attendant, followed by busy outdoor markets, and then metro and train stations, whereas the risks incurred while walking on fairly busy streets are comparatively quite low. While our models only approximate the actual transmission risks, their converging predictions lend credence to these findings. In situations with a moving crowd, density is the main factor influencing the estimated infection rate. Finally, our study explores the efficiency of street and venue redesigns in mitigating the viral spread: While the benefits of enforcing one-way foot traffic in (wide) walkways are unclear, changing the geometry of queues substantially affects disease transmission risks.

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“…While this model simulated the spread on (sub)national levels, other models are focussed on small-scale transmission, such as in indoor spaces. Harweg et al (2020), Xiao et al (2020), Fang et al (2020), Romero et al (2020), Ronchi et al (2020) and Xu & Chraibi (2020) developed hybrid models that combine microscopic force-based models for simulating pedestrian dynamics with viral transmission models. Most often, the distance and duration of, what is considered an infectious contact, is predefined.…”

Section: Introductionmentioning

confidence: 99%

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

Duives

Chang

Sparnaaij

et al. 2021

Preprint

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Since its introduction in December of 2019, SARS-CoV-2, the virus that causes COVID-19 disease, has rapidly spread across the world. Whilst vaccines are being rolled out, non-pharmaceutical interventions remain the most important tools for mitigating the spread of SARS-CoV-2. Quantifying the impact of these measures as well as determining what settings are prone to instigating (super)spreading events is important for informed and safe reopening of spaces and the targeting of interventions. Mathematical models can help decipher the complex interactions that underlie virus transmission. Currently, most mathematical models developed during the COVID-19 epidemic evaluate interventions at national or subnational levels. Smaller scales of transmission, such as at the level of indoor spaces, have received less attention, despite the central role they play in both transmission and control. Models that do act on this scale use simplified descriptions of human behavior, impeding a valid quantitative analysis of the impact of interventions on transmission in indoor spaces, particularly those that aim for physical distancing. To more accurately predict the transmission of SARS-CoV-2 through a pedestrian environment, we introduce a model that links pedestrian movement and choice dynamics with SARS-CoV-2 spreading models. The objective of this paper is to investigate the spread of SARS-CoV-2 in indoor spaces as it arises from human interactions and assess the relative impact of non-pharmaceutical interventions thereon. We developed a world-wide unique combined Pedestrian Dynamics - Virus Spread model (PeDViS model), which combines insights from pedestrian modelling, epidemiology, and IT-design. In particular, an expert-driven activity assignment model is coupled with the microscopic simulation model (Nomad) and a virus spread model (QVEmod). We first describe the non-linear relationships between the risks of exposure to the virus and the duration, distance, and context of human interactions. We compared virus exposure relative to a benchmark contact (1.5meters for 15 minutes): a threshold often used by public health agencies to determine at risk contacts. We discuss circumstances under which individuals that adhere to common distancing measures may nevertheless be at risk. Specifically, we illustrate the stark increase in exposure at shorter distances, as well as longer contact durations. These risks increase when the infected individual was present in the space before the interaction occurred, as a result of buildup of virus in the environment. The latter is particularly true in poorly ventilated spaces and highlights the importance of good ventilation to prevent potential virus exposure through indirect transmission routes. Combining intervention tools that target different routes of transmission can aid in accumulating impact. We use face masks as an example, which are particularly effective at reducing virus spread that is not affected by ventilation. We then demonstrate the use of PeDViS using a simple restaurant case study, focussing on transmission between guests. In this setting the exposure risk to individuals that are not seated at the same table is limited, but guests seated at nearby tables are estimated to experience exposure risks that surpass that of the benchmark contact. These risks are larger in low ventilation scenarios. Lastly, we illustrate that the impact of intervention measures on the number of new infections heavily depends on the relative efficiency of the direct and indirect transmission routes considered. This uncertainty should be considered when assessing the risks of transmission upon different types of human interactions in indoor spaces. The PeDViS case study shows the multi-dimensionality of SARS-CoV-2 that emerges from the interplay of human behaviour and the spread of respiratory viruses in indoor spaces. A modelling strategy that incorporates this in risk assessments can be an important tool to inform policy makers and citizens. It can empower them to make better design and policy decisions pertaining to the most effective use of measures to limit the spread of SARS-CoV-2 and safely open up indoor spaces.

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Agent-based simulation of pedestrian dynamics for exposure time estimation in epidemic risk assessment

Cited by 39 publications

References 32 publications

Point Cloud Based 3d Models for Agent Based Simulations in Social Distancing and Evacuation

Point Cloud Based 3d Models for Agent Based Simulations in Social Distancing and Evacuation

Model-based assessment of the risks of viral transmission in non-confined crowds

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

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