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Prognosis of Viral Transmission in Naturally Ventilated Office Rooms Using ML

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Key Digital Trends Shaping the Future of Information and Management Science (ISMS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 671))

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Abstract

More than 6.5 million people have already died as a result of the coronavirus pandemic, and there have been almost 0.6 billion cases of infection documented. Numerous organisations foresee the fourth wave, even though many countries are presently dealing with the severe consequences of the previous waves and the long-COVID repercussions. For all stakeholders, it is essential to forecast the rate of COVID-19 transmission likelihood inside enclosed environments. Based on the relevance of machine learning (ML) techniques in estimating the COVID-19 transmission probability, we collected the data through real-time measurements of eleven input parameters namely indoor temperature, indoor relative humidity, area of opening, number of occupants, area per person, volume per person, CO2 concentration, air quality index, outer wind speed, outdoor temperature, and outdoor humidity. The R-Event value was predicted using these inputs. Current literature is lagging behind the current pace of research in ML techniques which can be an effective and sustainable option for increasing public health and safety. In this work as a novel contribution, the prediction of new COVID-19 instances inside a workplace is done using MLtechniques and the parameters have been connected using novel methods. The performances of the six techniques are contrasted with one another using traditional statistical indicators to determine the merits of the suggested algorithms. This work will nudge the readers to use AI techniques in the prognosis of COVID-19 cases.

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Author information

Authors and Affiliations

  1. CSIR-Central Building Research Institute, Roorkee, 247667, India

    Nishant Raj Kapoor, Ashok Kumar & Anuj Kumar

  2. Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India

    Nishant Raj Kapoor, Ashok Kumar & Anuj Kumar

Authors
  1. Nishant Raj Kapoor
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  2. Ashok Kumar
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  3. Anuj Kumar
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Corresponding author

Correspondence to Nishant Raj Kapoor .

Editor information

Editors and Affiliations

  1. Faculty of Information and Communication Technology, University of Malta, Msida, Malta

    Lalit Garg

  2. Department of Computer Science and Engineering, National Institute of Technology Raipur, Chhatisgarh, India

    Dilip Singh Sisodia

  3. Central University of Rajasthan, Tehsil Kishangarh, Rajasthan, India

    Nishtha Kesswani

  4. CIS Department, University of Malta, Msida, Malta

    Joseph G. Vella

  5. EMLYON Business School, Écully, France

    Imene Brigui

  6. Department of Applied Data Science, Institute of Energy Technology, Halden, Norway

    Sanjay Misra

  7. Computer Science and Engineering, National Institute of Technology, Chhattisgarh, India

    Deepak Singh

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Kapoor, N.R., Kumar, A., Kumar, A. (2023). Prognosis of Viral Transmission in Naturally Ventilated Office Rooms Using ML. In: Garg, L., et al. Key Digital Trends Shaping the Future of Information and Management Science. ISMS 2022. Lecture Notes in Networks and Systems, vol 671. Springer, Cham. https://doi.org/10.1007/978-3-031-31153-6_22

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