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Analysis of COVID19 Mitigation Strategies using CFD and Agent Based Models

Evaluating the efficacy of mitigation measures to reduce risk from COVID19 is crucial if public policy and resource allocation are to be optimised to meet societal goals. While the sound knowledge base required to construct these effective mitigation strategies remains elusive, mathematical tools are emerging that offer a means to evaluate options robustly. The UK emerged from its 1st and 2nd lockdowns into a world without vaccinations, where COVID19 still posed a serious threat to life. Mitigation measures such as one-way pedestrian flows within supermarkets and railway stations, reduced occupancy levels, 2m physical distancing, partial separation screens, enhanced ventilation and air filtration systems, and face coverings were implemented with little quantification of their efficacy in general, relative to each other, or in specific applications. The actual level of protection they offered, if any, was uncertain, so it was not possible to determine whether the associated costs justified the benefits, or which mitigation measures were preferable. As the UK emerges from its third lockdown into a world where COVID19 and its variants still pose a serious potential threat, vaccination is the key mitigation hope this time. However, the fact that the programme is far from complete, and vaccinations only offer partial protection (with uncertain efficacy for variants such as B.1.617.2), means that some of the original mitigation measures may need to be used in conjunction with vaccination to manage both personal and societal infection risks. Mathematical tools, particularly CFD and Agent Based Modelling (ABM), offer a potential means to assess these mitigation measures and quantify their effectiveness. CFD models can simulate the movement of air in complex spaces, taking into account both forced and natural ventilation, and the dispersion of respired droplets and aerosols. ABM can simulate the movement and behaviour of large numbers of people in complex spaces. By modifying the behaviour rules within ABM appropriately, it is possible to simulate the movement of crowds, as individuals attempt to maintain a specified distance apart (SDA). FSEG has pioneered and developed CFD fire simulation models (SMARTFIRE) and ABM for evacuation and pedestrian dynamics (EXODUS) for over 30 years. FSEG developed the concept of coupling ABM and CFD fire models for advanced fire safety analysis. FSEG are now modifying their SMARTFIRE and buildingEXODUS models for use in COVID19 related applications to evaluate mitigation strategies. The behaviour rules within EXODUS have been modified to allow agents to maintain a SDA by enabling them to divert their path slightly. Using this approach, we can explore the impact of imposing SDA (e.g. 2.0m) and other physical separation methods on people flows, throughput and operational efficiency. The water mist model within SMARTFIRE has been modified to represent respired droplets and aerosols. The wake produced by moving people and its impact on aerosol dispersion is represented within SMARTFIRE using the Immersed Boundary Method. Finally, the SMARTFIRE software has been coupled with the Wells-Riley model to evaluate local infection probability. The modified SMARTFIRE software is used to investigate COVID-19 infection probabilities reported for passengers travelling on long distance trains in China [1]. This approach generates estimated infection probabilities that are in good agreement with the reported data [2]. The study demonstrates that the quanta concentration distribution in the train saloon is extremely non-uniform. The model is then used to investigate the effectiveness of various mitigation strategies, such as varying ventilation rates, improving filtration efficiency, seat blocking, wearing masks and inoculation. The observations also have implications for the efficacy of simpler analysis involving Wells-Riley modelling [3] and ABM. The reference of this presentation is: Galea E.R., ‘FSEG COVID19 Mitigation Analysis using CFD and Agent Based Models’, New Models of Spatial and Social Behaviour in a Pandemic, Isaac Newton Institute Cambridge University, 26-27 May 2021. https://gateway.newton.ac.uk/presenta... Cited references: [1] Maogui Hu, Hui Lin , Jinfeng Wang , Chengdong Xu , Andrew J Tatem , Bin Meng , Xin Zhang ,Yi f eng Liu Pengd a Wang , Gui z hen Wu , Hai yong Xie , Shengjie, "The risk of COVID-19 transmission in train passengers: an epidemiological and modelling study," Clinical Infectious Deseases, no. https://doi.org/10.1093/cid/ciaa1057, 2020. [2] Wang Z, Galea ER, Grandison A, Ewer J, Jia F,”Simulation of COVID-19 infection probabilities for Chinese long-distance trains”, In preparation, University of Greenwich, UK, 2021. [3] Wang Z, Galea ER, Grandison A, Ewer J, Jia F, "Inflight transmission of COVID-19 based on experimental aerosol dispersion data," Journal of Travel Medicine, https://doi.org/10.1093/jtm/taab023, 2021.