IEEE Access (Jan 2022)
Transmission Probability of SARS-CoV-2 in Office Environment Using Artificial Neural Network
Abstract
In this paper, curve-fitting and an artificial neural network (ANN) model were developed to predict R-Event. Expected number of new infections that arise in any event occurring over a total time in any space is termed as R-Event. Real-time data for the office environment was gathered in the spring of 2022 in a naturally ventilated office room in Roorkee, India, under composite climatic conditions. To ascertain the merit of the proposed ANN and curve-fitting models, the performances of the ANN approach were compared against the curve fitting model regarding conventional statistical indicators, i.e., correlation coefficient, root mean square error, mean absolute error, Nash-Sutcliffe efficiency index, mean absolute percentage error, and a20-index. Eleven input parameters namely indoor temperature ( $T_{In}$ ), indoor relative humidity ( $RH_{In}$ ), area of opening ( $A_{O}$ ), number of occupants ( $O$ ), area per person ( $A_{P}$ ), volume per person ( $V_{P}$ ), $CO_{2}$ concentration ( $CO_{2}$ ), air quality index ( $AQI$ ), outer wind speed ( $W_{S}$ ), outdoor temperature ( $T_{Out}$ ), outdoor humidity ( $RH_{Out}$ ) were used in this study to predict the R-Event value as an output. The primary goal of this research is to establish the link between $CO_{2}$ concentration and R-Event value; eventually providing a model for prediction purposes. In this case study, the correlation coefficient of the ANN model and curve-fitting model were 0.9992 and 0.9557, respectively. It shows the ANN model’s higher accuracy than the curve-fitting model in R-Event prediction. Results indicate the proposed ANN prediction performance (R = 0.9992, RMSE = 0.0018708, MAE = 0.0006675, MAPE = 0.8643816, NS = 0.9984365, and a20-index = 0.9984300) is reliable and highly accurate to predict the R-event for offices.
Keywords
