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 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.