the multi dimensional challenges of controlling sars cov 2 transmission in indoor CORD-Papers-2021-10-25 (Version 1)

Title: The multi-dimensional challenges of controlling SARS-CoV-2 transmission in indoor spaces: Insights from the linkage of a microscopic pedestrian simulation and virus transmission models
Abstract: 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.
Published: 4/19/2021
DOI: 10.1101/2021.04.12.21255349
DOI_URL: http://doi.org/10.1101/2021.04.12.21255349
Author Name: Duives, D
Author link: https://covid19-data.nist.gov/pid/rest/local/author/duives_d
Author Name: Chang, Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/chang_y
Author Name: Sparnaaij, M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/sparnaaij_m
Author Name: Wouda, B
Author link: https://covid19-data.nist.gov/pid/rest/local/author/wouda_b
Author Name: Boschma, D
Author link: https://covid19-data.nist.gov/pid/rest/local/author/boschma_d
Author Name: Liu, Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liu_y
Author Name: Yuan, Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/yuan_y
Author Name: Daamen, W
Author link: https://covid19-data.nist.gov/pid/rest/local/author/daamen_w
Author Name: de Jong, M C M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/de_jong_m_c_m
Author Name: Teberg, C
Author link: https://covid19-data.nist.gov/pid/rest/local/author/teberg_c
Author Name: Schachtschneider, K
Author link: https://covid19-data.nist.gov/pid/rest/local/author/schachtschneider_k
Author Name: Sikkema, R S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/sikkema_r_s
Author Name: van Veen, L
Author link: https://covid19-data.nist.gov/pid/rest/local/author/van_veen_l
Author Name: ten Bosch, Q
Author link: https://covid19-data.nist.gov/pid/rest/local/author/ten_bosch_q
sha: 41a24aa1e84680d79e9663e14b98337a071a6a3e
license: medrxiv
source_x: MedRxiv; WHO
source_x_url: https://www.who.int/
url: http://medrxiv.org/cgi/content/short/2021.04.12.21255349v1?rss=1 https://doi.org/10.1101/2021.04.12.21255349
has_full_text: TRUE
Keywords Extracted from Text Content: PeDViS medRxiv pedestrian expert-driven COVID-19 SARS-CoV-2 human spaces IT-design Vukadinovic Well-Riley countertops hand-and blue solid lines 1-2 ACH mucous layer P(draw Schadschneider (2002 human cone virus particles IT-design fly inhaled equi-cost lines pedestrians cell red dashed line Endo individuals Red dashed lines Daamen restaurants 10um see equations 1 -6 Sallah 0.1x0.1m COVID-19 Hanseler ACH=6 Shatu MNL Figure 4 Figure 15Da ketchup SNAPSHOT HEATMAPS toilets P-FD travellers P. Z. respiratory tract cells people's D∆c+S Hill (1984) brown rectangles QVEmodl Flaxman Campanella (2016) . lungs Viruses Prem viral-laden aerosols v) like iv) heart-to-heart cholera toilet Sarmady (2010) , Figure 8 .A-8.D eq. 18 SSO upper respiratory tract Superspreading Face seminal PeDVis Figure 9 cough- citynetwork people Nicas (1996) where P surface layer ∂C/∂t brown circles SamenSlimOpen force-base line Bb pedestrian blue toilets Jessurum (2016) simulate visitor's Danelet facial mucous layers, iii spaces viral-laden particles centre viral-laden droplets sections 3.4.3.1-3.4.3.7 virus-laden droplets fomites Cellular Bandini One-minute medRxiv preprint Ueki SamenSlimOpen App c parks pulmonary Spicknall Fomites Virus Pedestrian sections Fouda Harweg https://doi.org/10.1101/2021.04.12.21255349 doi b Velocity-based sections 3.4.1-3.4.2 SSO app forcebased layer Tau green arrows persons Mousssaid Duives Nicas PeDViS. low-touch CA's SIR Guo ID=16 SARS-CoV-2 B Blue & Adler (1998 Chau . appendix 1 cityscale networks Figure 7 .A NPIs Stubenschrott vertices PeDViS cells green Viral-laden droplets C and D droplets recipients left https://doi.org/10.1101 https://doi.org/10 face medRxiv Jebrane expert-driven humans wood nodes droplet supermarkets Figure 1 red line surface High-touch Dd Human SSO app Rineke SamenSlimOpen Yilin Huang COVID-19 Programma SARS-CoV-2 ZonMw Els van Daalen
Extracted Text Content in Record: First 5000 Characters: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 . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) Since its introduction in December of 2019, SARS-CoV-2, the virus that causes COVID-19 disease, has rapidly spread across the world. With over 98,794,942 registered cases and 2,124,193 reported deaths worldwide as of 25th January 2021, the impact of this virus is unprecedented (WHO, 2021) . In the absence of effective therapeutics and vaccines, control has largely relied on far reaching non-pharmaceutical interventions (NPI's). Many of these restrictions aim to limit interactions in indoor spaces, where most infection events occur (CDC (2020) , RIVM (2021) , WHO (2021) , Fouda et al., (2021) ). Schools and higher educational facilities have been closed, restaurants and cafes shut down, and visits to previously busy public spaces (e.g. theaters and shopping malls) discouraged and/or restricted. NPIs are currently the most important tools for m
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