Transmission Probability of SARS-CoV-2 in Office Environment Using Artificial Neural Network

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 o...

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Bibliographic Details
Published in:IEEE access 2022, Vol.10, p.121204-121229
Main Authors: Kapoor, Nishant Raj, Kumar, Ashok, Kumar, Anuj, Kumar, Anil, Kumar, Krishna
Format: Article
Language:English
Subjects:
Aerosols
Air quality
ANN
Artificial neural networks
Buildings
Carbon dioxide
carbon dioxide concentration
Correlation coefficients
COVID-19
Curve fitting
Errors
Humidity
Indoor air quality
Model accuracy
natural ventilation
Neural networks
office building
Predictive models
public health
Public healthcare
real-time monitoring
Real-time systems
Relative humidity
Severe acute respiratory syndrome coronavirus 2
Statistical analysis
Ventilation
Wind speed
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Summary: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
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3222795