{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T23:14:01Z","timestamp":1762125241604,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T00:00:00Z","timestamp":1732233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union Next-GenerationEU","award":["Piano Nazionale di Ripresa e Resilienza (PNRR)\u2014Missione 4 Componente 2, Investimento 1.5\u2014D.D. 1058 23\/06\/2022, ECS_00000043"],"award-info":[{"award-number":["Piano Nazionale di Ripresa e Resilienza (PNRR)\u2014Missione 4 Componente 2, Investimento 1.5\u2014D.D. 1058 23\/06\/2022, ECS_00000043"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Microorganisms"],"abstract":"<jats:p>The dynamics of the SARS-CoV-2 pandemic showed that closed environments, such as hospitals and schools, are more likely to host infection clusters due to environmental variables like humidity, ventilation, and overcrowding. This study aimed to validate our local transmission model by reproducing the data on SARS-CoV-2 diffusion in a hospital ward. We implemented our model in a Monte Carlo procedure that simulates the contacts between patients and healthcare workers in Trieste\u2019s geriatric ward and calculates the number of infected individuals. We found the median number of infected workers to be 38.98 (IQR = 7.75), while all patients were infected in most of the simulation runs. More infections occurred in rooms with lower volumes. Higher ventilation and mask-wearing contribute to reduced infections; in particular, we obtained a median value of 35.06 (IQR = 9.21) for the simulation in which we doubled room ventilation and 26.12 (IQR = 10.33) in the simulation run in which workers wore surgical masks. We managed to reproduce the data on infections in the ward; using a sensitivity analysis, we identified the parameters that had the greatest impact on the probability of transmission and the size of the outbreak.<\/jats:p>","DOI":"10.3390\/microorganisms12122401","type":"journal-article","created":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T10:17:23Z","timestamp":1732270643000},"page":"2401","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Easy-to-Use Tool to Predict SARS-CoV-2 Risk of Infection in Closed Settings: Validation with the Use of an Individual-Based Monte Carlo Simulation"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7900-1783","authenticated-orcid":false,"given":"Benedetta","family":"Santoro","sequence":"first","affiliation":[{"name":"Physics Department, University of Trieste, 34127 Trieste, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7717-0417","authenticated-orcid":false,"given":"Francesca","family":"Larese Filon","sequence":"additional","affiliation":[{"name":"Occupational Medicine Department, University Hospital of Trieste, 34129 Trieste, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7348-9765","authenticated-orcid":false,"given":"Edoardo","family":"Milotti","sequence":"additional","affiliation":[{"name":"Physics Department, University of Trieste, 34127 Trieste, Italy"},{"name":"I. N. F. N.\u2014Sezione di Trieste, 34149 Trieste, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"063303","DOI":"10.1063\/5.0015044","article-title":"On respiratory droplets and face masks","volume":"32","author":"Dbouk","year":"2020","journal-title":"Phys. Fluids"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhdanov, V.P., and Kasemo, B. (2020). Virions and respiratory droplets in air: Diffusion, drift, and contact with the epithelium. Biosystems, 198.","DOI":"10.1016\/j.biosystems.2020.104241"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2311","DOI":"10.1093\/cid\/ciaa939","article-title":"It Is Time to Address Airborne Transmission of Coronavirus Disease 2019 (COVID-19)","volume":"71","author":"Morawska","year":"2020","journal-title":"Clin. Infect. Dis."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"110612","DOI":"10.1016\/j.envres.2020.110612","article-title":"A systematic review of possible airborne transmission of the COVID-19 virus (SARS-CoV-2) in the indoor air environment","volume":"193","author":"Noorimotlagh","year":"2021","journal-title":"Environ. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e170721187995","DOI":"10.2174\/1871526520666201116095934","article-title":"Transmission Modes of COVID-19: A Systematic Review","volume":"21","author":"Mehraeen","year":"2021","journal-title":"Infect. Disord. Drug Targets"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Setti, L., Passarini, F., De Gennaro, G., Barbieri, P., Perrone, M.G., Borelli, M., Palmisani, J., Di Gilio, A., Piscitelli, P., and Miani, A. (2020). Airborne Transmission Route of COVID-19: Why 2 Meters\/6 Feet of Inter-Personal Distance Could Not Be Enough. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17082932"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Nissen, K., Krambrich, J., Akaberi, D., Hoffman, T., and Ling, J. (2020). Long-distance airborne dispersal of SARS-CoV-2 in COVID-19 wards. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-76442-2"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1007\/s41207-021-00243-w","article-title":"Indoor versus outdoor transmission of SARS-COV-2: Environmental factors in virus spread and underestimated sources of risk","volume":"6","author":"Senatore","year":"2021","journal-title":"Euro-Mediterr. J. Environ. Integr."},{"key":"ref_9","first-page":"700","article-title":"A Contribution to the Mathematical Theory of Epidemics","volume":"115","author":"Kermack","year":"1927","journal-title":"Proc. R. Soc. London. Ser. A Contain. Pap. A Math. Phys. Character"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Caetano, C., Morgado, M.L., Patr\u00edcio, P., Pereira, J.F., and Nunes, B. (2021). Mathematical Modelling of the Impact of Non-Pharmacological Strategies to Control the COVID-19 Epidemic in Portugal. Mathematics, 9.","DOI":"10.3390\/math9101084"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1038\/s41591-020-0883-7","article-title":"Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy","volume":"26","author":"Giordano","year":"2020","journal-title":"Nat. Med."},{"key":"ref_12","first-page":"323","article-title":"A COVID-19 epidemic model with latency period","volume":"5","author":"Liu","year":"2020","journal-title":"Infect. Dis. Model."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xu, J., and Tang, Y. (2021). Bayesian Framework for Multi-Wave COVID-19 Epidemic Analysis Using Empirical Vaccination Data. Mathematics, 10.","DOI":"10.3390\/math10010021"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"111189","DOI":"10.1016\/j.envres.2021.111189","article-title":"Simple quantitative assessment of the outdoor versus indoor airborne transmission of viruses and COVID-19","volume":"198","author":"Rowe","year":"2021","journal-title":"Environ. Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1093\/oxfordjournals.aje.a112560","article-title":"Airborne Spread of Measles in a Suburban Elementary School","volume":"107","author":"Riley","year":"1978","journal-title":"Am. J. Epidemiol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"021904","DOI":"10.1063\/5.0040755","article-title":"Estimating COVID-19 exposure in a classroom setting: A comparison between mathematical and numerical models","volume":"33","author":"Foster","year":"2021","journal-title":"Phys. Fluids"},{"key":"ref_17","unstructured":"Santoro, B. (2024, November 15). COVID-19-Evaluation-Tool. Available online: https:\/\/github.com\/benedettasantoro22\/covid-19-evaluation-tool."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Santoro, B., Larese Filon, F., and Milotti, E. (2023). A User-Friendly Tool to compute Infection Probability of SARS-CoV-2 Indoor: The User Guide and its Application in Medical Practice. J. Mech. Med. Biol., 23.","DOI":"10.1142\/S0219519423400183"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.1177\/1420326X211039544","article-title":"The Facility Infection Risk Estimator\u2122: A web application tool for comparing indoor risk mitigation strategies by estimating airborne transmission risk","volume":"31","author":"Harmon","year":"2022","journal-title":"Indoor Built Environ."},{"key":"ref_20","unstructured":"Hallett, S., Toro, F., and Ashurst, J.V. (2022). Physiology, Tidal Volume, StatPearls Publishing."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1093\/infdis\/jiaa334","article-title":"Airborne SARS-CoV-2 Is Rapidly Inactivated by Simulated Sunlight","volume":"222","author":"Schuit","year":"2020","journal-title":"J. Infect. Dis."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"106112","DOI":"10.1016\/j.envint.2020.106112","article-title":"Quantitative assessment of the risk of airborne transmission of SARS-CoV-2 infection: Prospective and retrospective applications","volume":"145","author":"Buonanno","year":"2020","journal-title":"Environ. Int."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, Y., Jiang, X., Qiu, Y., Gao, F., Xin, H., Li, D., Qin, Y., and Li, Z. (2024). Latent and incubation periods of Delta, BA.1, and BA.2 variant cases and associated factors: A cross-sectional study in China. BMC Infect. Dis., 24.","DOI":"10.1186\/s12879-024-09158-7"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"105905","DOI":"10.1016\/j.jaerosci.2021.105905","article-title":"Outward and inward protection efficiencies of different mask designs for different respiratory activities","volume":"160","author":"Koh","year":"2022","journal-title":"J. Aerosol Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"114112","DOI":"10.1016\/j.enconman.2021.114112","article-title":"A review of sensitivity analysis practices in wind resource assessment","volume":"238","author":"Tsvetkova","year":"2021","journal-title":"Energy Convers. Manag."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"De Crane D\u2019Heysselaer, S., Parisi, G., Lisson, M., Bruy\u00e8re, O., Donneau, A.F., Fontaine, S., Gillet, L., Bureau, F., Darcis, G., and Thiry, E. (2023). Systematic Review of the Key Factors Influencing the Indoor Airborne Spread of SARS-CoV-2. Pathogens, 12.","DOI":"10.3390\/pathogens12030382"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Cotman, Z.J., Bowden, M.J., Richter, B.P., Phelps, J.H., and Dibble, C.J. (2021). Factors affecting aerosol SARS-CoV-2 transmission via HVAC systems; a modeling study. PLoS Comput. Biol., 17.","DOI":"10.1371\/journal.pcbi.1009474"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Buonanno, G., Ricolfi, L., Morawska, L., and Stabile, L. (2022). Increasing ventilation reduces SARS-CoV-2 airborne transmission in schools: A retrospective cohort study in Italy\u2019s Marche region. Front. Public Health, 10.","DOI":"10.3389\/fpubh.2022.1087087"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1080\/15459624.2020.1805117","article-title":"Assessment and mitigation of aerosol airborne SARS-CoV-2 transmission in laboratory and office environments","volume":"17","author":"Augenbraun","year":"2020","journal-title":"J. Occup. Environ. Hyg."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lelieveld, J., Helleis, F., Borrmann, S., Cheng, Y., Drewnick, F., Haug, G., Klimach, T., Sciare, J., Su, H., and P\u00f6schl, U. (2020). Model Calculations of Aerosol Transmission and Infection Risk of COVID-19 in Indoor Environments. Int. J. Environ. Res. Public Health, 17.","DOI":"10.1101\/2020.09.22.20199489"},{"key":"ref_31","first-page":"1192","article-title":"Modeling of the Transmission of Coronaviruses, Measles Virus, Influenza Virus, Mycobacterium Tuberc","volume":"99","author":"Zemouri","year":"2020","journal-title":"Legionella Pneumophila Dental Clinics. J. Dent. Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.cjtee.2023.05.001","article-title":"Effect of COVID-19 pneumonia infection control protocols on nosocomial infection incidence in trauma patients","volume":"26","author":"Ahadi","year":"2023","journal-title":"Chin. J. Traumatol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"102719","DOI":"10.1016\/j.scs.2021.102719","article-title":"Assessing and controlling infection risk with Wells-Riley model and spatial flow impact factor (SFIF)","volume":"67","author":"Guo","year":"2021","journal-title":"Sustain. Cities Soc."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Deol, A.K., Scarponi, D., Beckwith, P., Yates, T.A., Karat, A.S., Yan, A.W.C., Baisley, K.S., Grant, A.D., White, R.G., and McCreesh, N. (2021). Estimating ventilation rates in rooms with varying occupancy levels: Relevance for reducing transmission risk of airborne pathogens. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0253096"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"107617","DOI":"10.1016\/j.buildenv.2021.107617","article-title":"Modelling uncertainty in the relative risk of exposure to the SARS-CoV-2 virus by airborne aerosol transmission in well mixed indoor air","volume":"191","author":"Jones","year":"2021","journal-title":"Build. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"104866","DOI":"10.1016\/j.ssci.2020.104866","article-title":"Modelling aerosol transport and virus exposure with numerical simulations in relation to SARS-CoV-2 transmission by inhalation indoors","volume":"130","author":"Vuorinen","year":"2020","journal-title":"Saf. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2897","DOI":"10.2147\/RMHP.S309098","article-title":"Risk Analysis in Healthcare Organizations: Methodological Framework and Critical Variables","volume":"14","author":"Pascarella","year":"2021","journal-title":"Risk Manag. Healthc. Policy"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kudryashova, O.B., Muravlev, E.V., Antonnikova, A.A., and Titov, S.S. (2021). Propagation of viral bioaerosols indoors. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0244983"}],"container-title":["Microorganisms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-2607\/12\/12\/2401\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:37:47Z","timestamp":1760114267000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-2607\/12\/12\/2401"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,22]]},"references-count":38,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["microorganisms12122401"],"URL":"https:\/\/doi.org\/10.3390\/microorganisms12122401","relation":{},"ISSN":["2076-2607"],"issn-type":[{"type":"electronic","value":"2076-2607"}],"subject":[],"published":{"date-parts":[[2024,11,22]]}}}