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
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Microorganisms |
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| Online Access: | https://www.mdpi.com/2076-2607/12/12/2401 |
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| author | Benedetta Santoro Francesca Larese Filon Edoardo Milotti |
| author_facet | Benedetta Santoro Francesca Larese Filon Edoardo Milotti |
| author_sort | Benedetta Santoro |
| collection | DOAJ |
| description | 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’s 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. |
| format | Article |
| id | doaj-art-649e18bc5fc244c983c80df274e37ace |
| institution | DOAJ |
| issn | 2076-2607 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Microorganisms |
| spelling | doaj-art-649e18bc5fc244c983c80df274e37ace2025-08-20T02:43:42ZengMDPI AGMicroorganisms2076-26072024-11-011212240110.3390/microorganisms12122401An 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 SimulationBenedetta Santoro0Francesca Larese Filon1Edoardo Milotti2Physics Department, University of Trieste, 34127 Trieste, ItalyOccupational Medicine Department, University Hospital of Trieste, 34129 Trieste, ItalyPhysics Department, University of Trieste, 34127 Trieste, ItalyThe 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’s 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.https://www.mdpi.com/2076-2607/12/12/2401SARS-CoV-2closed settingdiffusionmodel validationMonte Carlorisk evaluation |
| spellingShingle | Benedetta Santoro Francesca Larese Filon Edoardo Milotti 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 Microorganisms SARS-CoV-2 closed setting diffusion model validation Monte Carlo risk evaluation |
| 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | SARS-CoV-2 closed setting diffusion model validation Monte Carlo risk evaluation |
| url | https://www.mdpi.com/2076-2607/12/12/2401 |
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