The Facility Infection Risk Estimator™: A web application tool for comparing indoor risk mitigation strategies by estimating airborne transmission risk

Abstract

The COVID-19 pandemic created needs for (a) estimating the existing airborne risk of infection from SARS-CoV-2 in existing facilities and new designs and (b) estimating and comparing the impacts of engineering and behavioural strategies for contextually reducing that risk. This paper presents the development of a web application to meet these needs, the Facility Infection Risk Estimator™, and its underlying Wells–Riley based model. The model specifically estimates (a) the removal efficiencies of various settling, ventilation, filtration and virus inactivation strategies and (b) the associated probability of infection, given the room physical parameters and number of individuals infected present with either influenza or SARS-CoV-2. A review of the underlying calculations and associated literature is provided, along with the model's validation against two documented spreading events. The error between modelled and actual number of additional people infected, normalized by the number of uninfected people present, ranged from roughly –18.4% to +9.7%. The more certain one can be regarding the input parameters (such as for new designs or existing buildings with adequate field verification), the smaller these normalized errors will be, likely less than ±15%, making it useful for comparing the impacts of different risk mitigation strategies focused on airborne transmission.

New dose-response model and SARS-CoV-2 quanta emission rates for calculating the long-range airborne infection risk

“…The last search was conducted on 24 th August 2022. Finally, 21 articles [ [13] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] ] were considered eligible for inclusion in the present review study. Data from the included studies were extracted using a predesigned spreadsheet.…”

“…Altogether nine potential removal mechanisms were identified in the studies using the Wells-Riley model: ventilation [ [13] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] , [36] ], deposition [ [14] , [15] , [16] , [17] , [18] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [31] , [32] , [33] , [34] ], viral inactivation by relative humidity [ [13] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] ,...…”

A multi-robot deep Q-learning framework for priority-based sanitization of railway stations

“…Specifically, we exploit the periodic convolution of a Gaussian filter every ψ steps, where μ , σ 2 and ψ are suitable parameters that can be regulated depending on the meters/pixels ratio, the timestep, and the considered typology of spreading (in this work we assume a setting inspired by the aerial diffusion of the Covid-19 [ 41 ]). In our case, we set the μ and σ values according to the spreading parameters proposed in [ 42 , 43 ]. Notice that since the heatmap update is computed from clusters of people by applying a simulated process, we do not need exact real-time people tracking in the environment, but only an approximated estimate of clusters’ positions and movements in railway station areas.…”

Predicting the infection probability distribution of airborne and droplet transmissions

“…The Wells-Riley equation is widely used to predict the probability of infection caused by airborne transmission. 16,17,18 Rudnick and Milton extended the Wells-Riley equation 9,10 and proposed a method to determine the average number of quanta inhaled by occupants as suspended particles based on the CO 2 concentration of a target space. 19 They based this method on the principle that, as exhaled air is the only source of CO 2 , the CO 2 concentration can be used as a marker for particles that remain suspended in the air for a long time.…”