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A USER-FRIENDLY TOOL TO COMPUTE INFECTION PROBABILITY OF SARS-COV-2 INDOOR: THE USER GUIDE AND ITS APPLICATION IN MEDICAL PRACTICE

Physics Department, University of Trieste, via Alfonso Valerio 2, Trieste (TS) 34127, Italy

Corresponding author.

Unit of Occupational Medicine, ASUGI (Azienda Sanitaria Universitaria Giuliano Isontina), Department of Medical Sciences, University of Trieste, via della Piet, 2/2, Trieste (TS) 34129, Italy

E-mail Address: [email protected]

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EDOARDO MILOTTI

Physics Department, University of Trieste, National Institute of Nuclear Physics, Section of Trieste, via Alfonso Valerio 2, Trieste (TS) 34127, Italy

E-mail Address: [email protected]

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https://doi.org/10.1142/S0219519423400183Cited by:1 (Source: Crossref)

This article is part of the issue:

Special Issue: XXII International Conference on Mechanics in Medicine and Biology (ICMMB-22)
Guest Editor: Ivan Corazza

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Abstract

After some initial hesitancy at the beginning of the COVID-19 pandemic, the academic community agreed that the infection process is mostly airborne and generally associated with closed environments. Therefore, assessing the indoor infection probability is mandatory to contain the spread of the disease, especially in those environments, like school classrooms, hospital wards or public transportation, with higher risk of overcrowding. For this reason, we developed a software tool in Python to compute infection probability and determine those mechanisms that contribute to reduce its diffusion in closed settings. In this paper, we will briefly illustrate the model we used and focus our attention on the description of the main features of the software and give some examples of how it can be used in clinical practice to predict the spread of the disease in the rooms of a generic ward, optimize room occupancy or drive healthcare workers activity schedule. Finally, some limitations and further implementations of our work will be reported.

Keywords:

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