Skip to main content
PMC home page
Springer Nature - PMC COVID-19 Collection logoLink to Springer Nature - PMC COVID-19 Collection
. 2021 Feb 27;14(6):1795–1809. doi: 10.1007/s12273-021-0770-2

A probabilistic model to evaluate the effectiveness of main solutions to COVID-19 spreading in university buildings according to proximity and time-based consolidated criteria

Marco D’Orazio 1, Gabriele Bernardini 1, Enrico Quagliarini 1,✉
PMCID: PMC7910197  PMID: 33680337

Abstract

University buildings are one of the most relevant closed environments in which the COVID-19 event clearly pointed out stakeholders’ needs toward safety issues, especially because of the possibility of day-to-day presences of the same users (i.e. students, teachers) and overcrowding causing long-lasting contacts with possible “infectors”. While waiting for the vaccine, as for other public buildings, policy-makers’ measures to limit virus outbreaks combine individual’s strategies (facial masks), occupants’ capacity and access control. But, up to now, no easy-to-apply tools are available for assessing the punctual effectiveness of such measures. To fill this gap, this work proposes a quick and probabilistic simulation model based on consolidated proximity and exposure-time-based rules for virus transmission confirmed by international health organizations. The building occupancy is defined according to university scheduling, identifying the main “attraction areas” in the building (classrooms, break-areas). Scenarios are defined in terms of occupants’ densities and the above-mentioned mitigation strategies. The model is calibrated on experimental data and applied to a relevant university building. Results demonstrate the model capabilities. In particular, it underlines that if such strategies are not combined, the virus spreading can be limited by only using high protection respiratory devices (i.e. FFP3) by almost every occupant. On the contrary, the combination between access control and building capacity limitation can lead to the adoption of lighter protective devices (i.e. surgical masks), thus improving the feasibility, users’ comfort and favorable reception. Simplified rules to combine acceptable mask filters-occupants’ density are thus provided to help stakeholders in organizing users’ presences in the building during the pandemic.

Electronic Supplementary Material (ESM)

supplementary material is available in the online version of this article at 10.1007/s12273-021-0770-2.

Keywords: simulation model, closed built environment, building occupancy, crowd models, proximity exposure, COVID-19

Electronic Supplementary Material

12273_2021_770_MOESM1_ESM.pdf (1.1MB, pdf)

A probabilistic model to evaluate the effectiveness of main solutions to COVID-19 spreading in university buildings according to proximity and time-based consolidated criteria

Funding note

Open access funding provided by Università Politecnica delle Marche.

References

  1. Adams RI, Bhangar S, Dannemiller KC, et al. Ten questions concerning the microbiomes of buildings. Building and Environment. 2016;109:224–234. doi: 10.1016/j.buildenv.2016.09.001. [DOI] [Google Scholar]
  2. Anderson RM, Heesterbeek H, Klinkenberg D, et al. How will country-based mitigation measures influence the course of the COVID-19 epidemic? The Lancet. 2020;395:931–934. doi: 10.1016/S0140-6736(20)30567-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Azimi P, Keshavarz Z, Cedeno Laurent JG, et al. (2020). Mechanistic Transmission Modeling of COVID-19 on the Diamond Princess Cruise Ship Demonstrates the Importance of Aerosol Transmission. medRxiv: 2020.07.13.20153049. [DOI] [PMC free article] [PubMed]
  4. Banos A, Lang C, Marilleau N. Agent-Based Spatial Simulation with NetLogo. London: ISTE Press; 2015. [Google Scholar]
  5. Bernardini G, Quagliarini E, D’Orazio M, et al. Towards the simulation of flood evacuation in urban scenarios: Experiments to estimate human motion speed in floodwaters. Safety Science. 2020;123:104563. doi: 10.1016/j.ssci.2019.104563. [DOI] [Google Scholar]
  6. Bruinen de Bruin Y, Lequarre A-S, McCourt J, et al. Initial impacts of global risk mitigation measures taken during the combatting of the COVID-19 pandemic. Safety Science. 2020;128:104773. doi: 10.1016/j.ssci.2020.104773. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Casareale C, Bernardini G, Bartolucci A, et al. Cruise ships like buildings: Wayfinding solutions to improve emergency evacuation. Building Simulation. 2017;10:989–1003. doi: 10.1007/s12273-017-0381-0. [DOI] [Google Scholar]
  8. Chen C, Xu L, Zhao D, et al. A new model for describing the urban resilience considering adaptability, resistance and recovery. Safety Science. 2020;128:104756. doi: 10.1016/j.ssci.2020.104756. [DOI] [Google Scholar]
  9. Cirrincione L, Plescia F, Ledda C, et al. COVID-19 pandemic: Prevention and protection measures to be adopted at the workplace. Sustainability. 2020;12:3603. doi: 10.3390/su12093603. [DOI] [Google Scholar]
  10. D’Orazio M, Quagliarini E, Bernardini G, et al. EPES—Earthquake pedestrians’ evacuation simulator: A tool for predicting earthquake pedestrians’ evacuation in urban outdoor scenarios. International Journal of Disaster Risk Reduction. 2014;10:153–177. doi: 10.1016/j.ijdrr.2014.08.002. [DOI] [Google Scholar]
  11. Dai H, Zhao B. Association of the infection probability of COVID-19 with ventilation rates in confined spaces. Building Simulation. 2020;13:1321–1327. doi: 10.1007/s12273-020-0703-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Dohaney J, de Róiste M, Salmon RA, et al. Benefits, barriers, and incentives for improved resilience to disruption in university teaching. International Journal of Disaster Risk Reduction. 2020;50:101691. doi: 10.1016/j.ijdrr.2020.101691. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Dong B, Yan D, Li Z, et al. Modeling occupancy and behavior for better building design and operation—A critical review. Building Simulation. 2018;11:899–921. doi: 10.1007/s12273-018-0452-x. [DOI] [Google Scholar]
  14. Fanelli D, Piazza F. Analysis and forecast of COVID-19 spreading in China, Italy and France. Chaos, Solitons & Fractals. 2020;134:109761. doi: 10.1016/j.chaos.2020.109761. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Fang Z, Huang Z, Li X, et al. (2020). How many infections of COVID-19 there will be in the “Diamond Princess”—Predicted by a virus transmission model based on the simulation of crowd flow. arXiv:2002.10616.
  16. Favale T, Soro F, Trevisan M, et al. Campus traffic and e-Learning during COVID-19 pandemic. Computer Networks. 2020;176:107290. doi: 10.1016/j.comnet.2020.107290. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Gao N, Niu J, Morawska L. Distribution of respiratory droplets in enclosed environments under different air distribution methods. Building Simulation. 2008;1:326–335. doi: 10.1007/s12273-008-8328-0. [DOI] [Google Scholar]
  18. Gao X, Wei J, Lei H, et al. Building ventilation as an effective disease intervention strategy in a dense indoor contact network in an ideal city. PLoS One. 2016;11:e0162481. doi: 10.1371/journal.pone.0162481. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Howard J, Huang A, Li Z, et al. (2020). Face masks against COVID-19: An evidence review. Preprints 2020040203. 10.20944/preprints202004.0203.v1
  20. Hu X, Ni W, Wang Z, et al. The distribution of SARS-CoV-2 contamination on the environmental surfaces during incubation period of COVID-19 patients. Ecotoxicology and Environmental Safety. 2021;208:111438. doi: 10.1016/j.ecoenv.2020.111438. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Knowles KA, Olatunji BO. Anxiety and safety behavior usage during the COVID-19 pandemic: The prospective role of contamination fear. Journal of Anxiety Disorders. 2021;77:102323. doi: 10.1016/j.janxdis.2020.102323. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Laskowski M, Demianyk BCP, Witt J, et al. Agent-based modeling of the spread of influenza-like illness in an emergency department: A Simulation study. IEEE Transactions on Information Technology in Biomedicine. 2011;15:877–889. doi: 10.1109/TITB.2011.2163414. [DOI] [PubMed] [Google Scholar]
  23. Lauer SA, Grantz KH, Bi Q, et al. The incubation period of coronavirus disease 2019 (COVID-19) from publicly reported confirmed cases: estimation and application. Annals of Internal Medicine. 2020;172:577–582. doi: 10.7326/M20-0504. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Lopez LR, Rodo X (2020). A modified SEIR model to predict the COVID-19 outbreak in Spain and Italy: simulating control scenarios and multi-scale epidemics. medRxiv 2020.03.27.20045005. [DOI] [PMC free article] [PubMed]
  25. Lu C, Deng Q, Li Y. The Science of the Total Environment. 2016. [Google Scholar]
  26. Lu C, Norbäck D, Zhang Y, et al. Common cold among young adults in China without a history of asthma or allergic rhinitis — associations with warmer climate zone, dampness and mould at home, and outdoor PM10 and PM2.5. Science of the Total Environment. 2020;749:141580. doi: 10.1016/j.scitotenv.2020.141580. [DOI] [PubMed] [Google Scholar]
  27. Mizumoto K, Chowell G. Transmission potential of the novel coronavirus (COVID-19) onboard the diamond Princess Cruises Ship, 2020. Infectious Disease Modelling. 2020;5:264–270. doi: 10.1016/j.idm.2020.02.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Murray OM, Bisset JM, Gilligan PJ, et al. Respirators and surgical facemasks for COVID-19: implications for MRI. Clinical Radiology. 2020;75:405–407. doi: 10.1016/j.crad.2020.03.029. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Pica N, Bouvier NM. Environmental factors affecting the transmission of respiratory viruses. Current Opinion in Virology. 2012;2:90–95. doi: 10.1016/j.coviro.2011.12.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Prem K, Liu Y, Russell T, et al. The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study. The Lancet Public Health. 2020;5:E261–E270. doi: 10.1016/S2468-2667(20)30073-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Prussin A, Belser JA, Bischoff W, et al. Viruses in the Built Environment (VIBE) meeting report. Microbiome. 2020;8:1. doi: 10.1186/s40168-019-0777-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Rengasamy S, Shaffer R, Williams B, Smit S. A comparison of facemask and respirator filtration test methods. Journal of Occupational and Environmental Hygiene. 2017;14:92–103. doi: 10.1080/15459624.2016.1225157. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Romero V, Stone WD, Ford JD. COVID-19 indoor exposure levels: An analysis of foot traffic scenarios within an academic building. Transportation Research Interdisciplinary Perspectives. 2020;7:100185. doi: 10.1016/j.trip.2020.100185. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Ronchi E, Lovreglio R. EXPOSED: An occupant exposure model for confined spaces to retrofit crowd models during a pandemic. Safety Science. 2020;130:104834. doi: 10.1016/j.ssci.2020.104834. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Saari A, Tissari T, Valkama E, Seppänen O. The effect of a redesigned floor plan, occupant density and the quality of indoor climate on the cost of space, productivity and sick leave in an office building-A case study. Building and Environment. 2006;41:1961–1972. doi: 10.1016/j.buildenv.2005.07.012. [DOI] [Google Scholar]
  36. Salecker J, Sciaini M, Meyer KM, Wiegand K. The nlrx r package: A next-generation framework for reproducible NetLogo model analyses. Methods in Ecology and Evolution. 2019;10:1854–1863. doi: 10.1111/2041-210X.13286. [DOI] [Google Scholar]
  37. Saltelli A, Ratto M, Andres T, et al. Global Sensitivity Analysis. The Primer. Chichester, UK: John Wiley & Sons; 2007. [Google Scholar]
  38. Saltelli A, Annoni P, Azzini I, et al. Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Computer Physics Communications. 2010;181:259–270. doi: 10.1016/j.cpc.2009.09.018. [DOI] [Google Scholar]
  39. Servick K. Science. 2020. [Google Scholar]
  40. Shiina A, Niitsu T, Kobori O, et al. Relationship between perception and anxiety about COVID-19 infection and risk behaviors for spreading infection: A national survey in Japan. Brain, Behavior, & Immunity — Health. 2020;6:100101. doi: 10.1016/j.bbih.2020.100101. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Sobol’ I. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Mathematics and Computers in Simulation. 2001;55:271–280. doi: 10.1016/S0378-4754(00)00270-6. [DOI] [Google Scholar]
  42. Wang H, Qian H, Zhou R, et al. A novel circulated air curtain system to confine the transmission of exhaled contaminants: A numerical and experimental investigation. Building Simulation. 2020;13:1425–1437. doi: 10.1007/s12273-020-0667-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Wilder-Smith A, Chiew CJ, Lee VJ. Can we contain the COVID-19 outbreak with the same measures as for SARS? The Lancet Infectious Diseases. 2020;20:E102–E107. doi: 10.1016/S1473-3099(20)30129-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Wilensky U (1999). NetLogo. Northwestern university, Evanston, IL, USA. Available at http://ccl.northwestern.edu/netlogo/.
  45. Yang Y, Peng F, Wang R, et al. The deadly coronaviruses: The 2003 SARS pandemic and the 2020 novel coronavirus epidemic in China. Journal of Autoimmunity. 2020;109:102434. doi: 10.1016/j.jaut.2020.102434. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Zhai Z. Facial mask: A necessity to beat COVID-19. Building and Environment. 2020;175:106827. doi: 10.1016/j.buildenv.2020.106827. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Zhang N, Huang H, Su B, et al. A human behavior integrated hierarchical model of airborne disease transmission in a large city. Building and Environment. 2018;127:211–220. doi: 10.1016/j.buildenv.2017.11.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Zheng X, Zhong T, Liu M. Modeling crowd evacuation of a building based on seven methodological approaches. Building and Environment. 2009;44:437–445. doi: 10.1016/j.buildenv.2008.04.002. [DOI] [Google Scholar]
  49. Zizzo M, Bollino R, Castro Ruiz C, et al. Surgical management of suspected or confirmed SARS-CoV-2 (COVID-19)-positive patients: a model stemming from the experience at Level III Hospital in Emilia-Romagna, Italy. European Journal of Trauma and Emergency Surgery. 2020;46:513–517. doi: 10.1007/s00068-020-01377-2. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

12273_2021_770_MOESM1_ESM.pdf (1.1MB, pdf)

A probabilistic model to evaluate the effectiveness of main solutions to COVID-19 spreading in university buildings according to proximity and time-based consolidated criteria

Articles from Building Simulation are provided here courtesy of Nature Publishing Group

RESOURCES