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 proposedANNand curve-fitting models, the performances of theANNapproach 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 (TIn), indoor relative humidity (RHIn), area of opening (AO), number of occupants (O), area per person (AP), volume per person (VP), CO2 concentration (CO2), air quality index (AQI), outer wind speed (WS ), outdoor temperature (TOut), outdoor humidity (RHOut) 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 CO2 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 D 0.9992, RMSE D 0.0018708, MAE D 0.0006675, MAPE D 0.8643816, NS D 0.9984365, and a20-index D 0.9984300) is reliable and highly accurate to predict the R-event for offices.