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

Author

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  • Nishant Raj Kapoor

    (Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
    Architecture and Planning Department, CSIR-Central Building Research Institute, Roorkee 247667, India)

  • Ashok Kumar

    (Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
    Architecture and Planning Department, CSIR-Central Building Research Institute, Roorkee 247667, India)

  • Anuj Kumar

    (Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
    Building Energy Efficiency Division, CSIR-Central Building Research Institute, Roorkee 247667, India)

  • Dilovan Asaad Zebari

    (Department of Computer Science, College of Science, Nawroz University, Duhok 42001, Iraq)

  • Krishna Kumar

    (Department of Hydro and Renewable Energy, Indian Institute of Technology, Roorkee 247667, India)

  • Mazin Abed Mohammed

    (College of Computer Science and Information Technology, University of Anbar, Anbar 31001, Iraq)

  • Alaa S. Al-Waisy

    (Computer Technologies Engineering Department, Information Technology College, Imam Ja’afar Al-Sadiq University, Baghdad 10064, Iraq)

  • Marwan Ali Albahar

    (School of Computer Science, Umm Al-Qura University, Mecca 24382, Saudi Arabia)

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, 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 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 ), fan air speed ( F S ), 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 2 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.

Suggested Citation

  • Nishant Raj Kapoor & Ashok Kumar & Anuj Kumar & Dilovan Asaad Zebari & Krishna Kumar & Mazin Abed Mohammed & Alaa S. Al-Waisy & Marwan Ali Albahar, 2022. "Event-Specific Transmission Forecasting of SARS-CoV-2 in a Mixed-Mode Ventilated Office Room Using an ANN," IJERPH, MDPI, vol. 19(24), pages 1-27, December.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:24:p:16862-:d:1004470
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    References listed on IDEAS

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    1. Nishant Raj Kapoor & Ashok Kumar & Tabish Alam & Anuj Kumar & Kishor S. Kulkarni & Paolo Blecich, 2021. "A Review on Indoor Environment Quality of Indian School Classrooms," Sustainability, MDPI, vol. 13(21), pages 1-43, October.
    2. Aman Kumar & Harish Chandra Arora & Nishant Raj Kapoor & Mazin Abed Mohammed & Krishna Kumar & Arnab Majumdar & Orawit Thinnukool, 2022. "Compressive Strength Prediction of Lightweight Concrete: Machine Learning Models," Sustainability, MDPI, vol. 14(4), pages 1-22, February.
    3. Dyani Lewis, 2021. "Superspreading drives the COVID pandemic — and could help to tame it," Nature, Nature, vol. 590(7847), pages 544-546, February.
    4. Kavita Batra & Tejinder Pal Singh & Manoj Sharma & Ravi Batra & Nena Schvaneveldt, 2020. "Investigating the Psychological Impact of COVID-19 among Healthcare Workers: A Meta-Analysis," IJERPH, MDPI, vol. 17(23), pages 1-33, December.
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    1. Nishant Raj Kapoor & Aman Kumar & Ashok Kumar & Harish Chandra Arora & Anuj Kumar & Sulakshya Gaur, 2024. "Energy-Efficient Strategies for Mitigating Airborne Pathogens in Buildings—Building Stage-Based Sustainable Strategies," Sustainability, MDPI, vol. 16(2), pages 1-22, January.

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