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Event-Specific Transmission Forecasting of SARS-CoV-2 in a Mixed-Mode Ventilated Office Room Using an ANN

Kapoor, Nishant Raj ; Kumar, Ashok ; Kumar, Anuj ; Zebari, Dilovan Asaad ; Kumar, Krishna LU orcid ; Mohammed, Mazin Abed ; Al-Waisy, Alaa S. and Albahar, Marwan Ali (2022) In International Journal of Environmental Research and Public Health 19(24).
Abstract

The emerging novel variants and re-merging old variants of SARS-CoV-2 make it critical to study the transmission probability in mixed-mode ventilated office environments. Artificial neural network (ANN) and curve fitting (CF) models were created to forecast the R-Event. The R-Event is defined as the anticipated number of new infections that develop in particular events occurring over the course of time in any defined space. In the spring and summer of 2022, real-time data for an office environment were collected in India in a mixed-mode ventilated office space in a composite climate. The performances of the proposed CF and ANN models were compared with respect to traditional statistical indicators, such as the correlation coefficient,... (More)

The emerging novel variants and re-merging old variants of SARS-CoV-2 make it critical to study the transmission probability in mixed-mode ventilated office environments. Artificial neural network (ANN) and curve fitting (CF) models were created to forecast the R-Event. The R-Event is defined as the anticipated number of new infections that develop in particular events occurring over the course of time in any defined space. In the spring and summer of 2022, real-time data for an office environment were collected in India in a mixed-mode ventilated office space in a composite climate. The performances of the proposed CF and ANN models were compared with respect to traditional statistical indicators, such as the correlation coefficient, RMSE, MAE, MAPE, NS index, and a20-index, in order to determine the merit of the two approaches. Thirteen input features, namely the 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), fan air speed (FS), and air conditioning (AC), were selected to forecast the R-Event as the target. The main objective was to determine the relationship between the CO2 level and R-Event, ultimately producing a model for forecasting infections in office building environments. The correlation coefficients for the CF and ANN models in this case study were 0.7439 and 0.9999, respectively. This demonstrates that the ANN model is more accurate in R-Event prediction than the curve fitting model. The results show that the proposed ANN model is reliable and significantly accurate in forecasting the R-Event values for mixed-mode ventilated offices.

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author
; ; ; ; ; ; and
publishing date
type
Contribution to journal
publication status
published
subject
keywords
air-conditioned buildings, artificial neural network, carbon dioxide concentration, mixed-mode ventilation, office environment, public health, real-time monitoring, SARS-CoV-2
in
International Journal of Environmental Research and Public Health
volume
19
issue
24
article number
16862
publisher
MDPI AG
external identifiers
  • pmid:36554744
  • scopus:85144571062
ISSN
1661-7827
DOI
10.3390/ijerph192416862
language
English
LU publication?
no
additional info
Publisher Copyright: © 2022 by the authors.
id
f20d394b-5e63-40e0-9464-2cfd465fa1b8
date added to LUP
2024-04-15 13:07:21
date last changed
2024-04-20 03:08:35
@article{f20d394b-5e63-40e0-9464-2cfd465fa1b8,
  abstract     = {{<p>The emerging novel variants and re-merging old variants of SARS-CoV-2 make it critical to study the transmission probability in mixed-mode ventilated office environments. Artificial neural network (ANN) and curve fitting (CF) models were created to forecast the R-Event. The R-Event is defined as the anticipated number of new infections that develop in particular events occurring over the course of time in any defined space. In the spring and summer of 2022, real-time data for an office environment were collected in India in a mixed-mode ventilated office space in a composite climate. The performances of the proposed CF and ANN models were compared with respect to traditional statistical indicators, such as the correlation coefficient, RMSE, MAE, MAPE, NS index, and a20-index, in order to determine the merit of the two approaches. Thirteen input features, namely the indoor temperature (T<sub>In</sub>), indoor relative humidity (RH<sub>In</sub>), area of opening (A<sub>O</sub>), number of occupants (O), area per person (A<sub>P</sub>), volume per person (V<sub>P</sub>), CO<sub>2</sub> concentration (CO<sub>2</sub>), air quality index (AQI), outer wind speed (W<sub>S</sub>), outdoor temperature (T<sub>Out</sub>), outdoor humidity (RH<sub>Out</sub>), fan air speed (F<sub>S</sub>), and air conditioning (AC), were selected to forecast the R-Event as the target. The main objective was to determine the relationship between the CO<sub>2</sub> level and R-Event, ultimately producing a model for forecasting infections in office building environments. The correlation coefficients for the CF and ANN models in this case study were 0.7439 and 0.9999, respectively. This demonstrates that the ANN model is more accurate in R-Event prediction than the curve fitting model. The results show that the proposed ANN model is reliable and significantly accurate in forecasting the R-Event values for mixed-mode ventilated offices.</p>}},
  author       = {{Kapoor, Nishant Raj and Kumar, Ashok and Kumar, Anuj and Zebari, Dilovan Asaad and Kumar, Krishna and Mohammed, Mazin Abed and Al-Waisy, Alaa S. and Albahar, Marwan Ali}},
  issn         = {{1661-7827}},
  keywords     = {{air-conditioned buildings; artificial neural network; carbon dioxide concentration; mixed-mode ventilation; office environment; public health; real-time monitoring; SARS-CoV-2}},
  language     = {{eng}},
  number       = {{24}},
  publisher    = {{MDPI AG}},
  series       = {{International Journal of Environmental Research and Public Health}},
  title        = {{Event-Specific Transmission Forecasting of SARS-CoV-2 in a Mixed-Mode Ventilated Office Room Using an ANN}},
  url          = {{http://dx.doi.org/10.3390/ijerph192416862}},
  doi          = {{10.3390/ijerph192416862}},
  volume       = {{19}},
  year         = {{2022}},
}