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Spreading of Novel Coronavirus (COVID-19) in Ambient Air: Modeling, Prediction...
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Spreading of Novel Coronavirus (COVID-19) in Ambient Air: Modeling, Prediction and Mitigation Strategies

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality and Human Health".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 4068

Special Issue Editors

Prof. Wan-ki Chow

E-Mail Website
Guest Editor
Department of Building Services Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Interests: architectural science; fire safety and engineering; sustainable buildings
Special Issues, Collections and Topics in MDPI journals
Dr. Kwok Wai Tham

E-Mail Website
Guest Editor
Department of Building, National University of Singapore, 4 Architecture Drive, Singapore 117566, Singapore
Interests: indoor air quality and human effects; building performance and energy efficiency; thermal comfort; ventilation
Dr. Yanfeng Li

E-Mail Website
Guest Editor
College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China
Interests: fire safety especially for ventilation and smoke control in underground public transport space (subway, tunnel and comprehensive hub); mathematical programming models for building intelligent control and energy conservation

Special Issue Information

Dear Colleagues,

The COVID-19 pandemic has brought about tremendous impact on most activities all over the world, with over 130 million infected by March 2021. Before having specific medicine and appropriate vaccines for comprehensive vaccination coverage against SARS-CoV-2, workable mitigation strategies must be implemented to fight such global pandemics resulting from disease transmission, particularly those infected through indoor environments. 

Apart from direct contact with carriers or contaminated surfaces, stopping infection through air transmission is the key point in controlling the outbreak inside buildings. Appropriate ventilation design would be an effective way to control COVID-19 transmission through air in indoor environments. There are many arguments regarding the current practices such as 6 air changes per hour (ACH) in different types of indoor environments such as restaurants. This Special Issue is specifically devoted to studies in the spreading of COVID-19 through ambient air and possible mitigation strategies, especially via effective ventilation. Research on modelling and prediction of air transmission of SARS-CoV-2 and associated ventilation design guides on COVID-19 control are the targeted subjects of this Special Issue. Results will also be useful for advising the government in making decisions on virus control for indoor environments.

Papers are welcome on mitigation strategy and codes developed in building ventilation requirements, indoor air flow patterns, local air speeds, turbulence, and their effects on virus control in determining criteria for providing better ventilation. Specific topics are:

  • Transmission mechanism of SARS-CoV-2 through air.
  • Air flow pattern and mixing of clean and infected indoor air.
  • Local air speeds and turbulence at occupied zones.
  • Ventilation theory, design and application related to SARS-CoV-2 control.
  • Correlation relations among the key macroscopic design parameters.
  • Computational Fluid Dynamics application in indoor aerodynamics and field measurements.
  • Personal ventilation system. 
  • Ventilation requirements expressed in terms of macroscopic flow number such as number of air changes per hour.
  • Comparison of, and criticisms on, current practices in ventilation requirements.
  • Criticisms for working out mitigation strategy.
  • Case studies.

Dr. Wan-ki Chow
Dr. Yanfeng Li
Dr. Kwok Wai Tham
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • spreading of SARS-CoV-2
  • ambient air
  • air transmission
  • modeling
  • prediction and mitigation strategies
  • novel coronavirus (COVID-19)
  • ventilation provision

Published Papers (2 papers)

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Research

20 pages, 1456 KiB  
Article
Data-Driven Prediction of COVID-19 Daily New Cases through a Hybrid Approach of Machine Learning Unsupervised and Deep Learning
by Ulises Manuel Ramirez-Alcocer, Edgar Tello-Leal, Bárbara A. Macías-Hernández and Jaciel David Hernandez-Resendiz
Atmosphere 2022, 13(8), 1205; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13081205 - 31 Jul 2022
Viewed by 1304
Abstract
Air pollution is associated with respiratory diseases and the transmission of infectious diseases. In this context, the association between meteorological factors and poor air quality possibly contributes to the transmission of COVID-19. Therefore, analyzing historical data of particulate matter (PM2.5 and PM [...] Read more.
Air pollution is associated with respiratory diseases and the transmission of infectious diseases. In this context, the association between meteorological factors and poor air quality possibly contributes to the transmission of COVID-19. Therefore, analyzing historical data of particulate matter (PM2.5 and PM10) and meteorological factors in indoor and outdoor environments to discover patterns that allow predicting future confirmed cases of COVID-19 is a challenge within a long pandemic. In this study, a hybrid approach based on machine learning and deep learning is proposed to predict confirmed cases of COVID-19. On the one hand, a clustering algorithm based on K-means allows the discovery of behavior patterns by forming groups with high cohesion. On the other hand, multivariate linear regression is implemented through a long short-term memory (LSTM) neural network, building a reliable predictive model in the training stage. The LSTM prediction model is evaluated through error metrics, achieving the highest performance and accuracy in predicting confirmed cases of COVID-19, using data of PM2.5 and PM10 concentrations and meteorological factors of the outdoor environment. The predictive model obtains a root-mean-square error (RMSE) of 0.0897, mean absolute error (MAE) of 0.0837, and mean absolute percentage error (MAPE) of 0.4229 in the testing stage. When using a dataset of PM2.5, PM10, and meteorological parameters collected inside 20 households from 27 May to 13 October 2021, the highest performance is obtained with an RMSE of 0.0892, MAE of 0.0592, and MAPE of 0.2061 in the testing stage. Moreover, in the validation stage, the predictive model obtains a very acceptable performance with values between 0.4152 and 3.9084 for RMSE, and a MAPE of less than 4.1%, using three different datasets with indoor environment values. Full article
(This article belongs to the Special Issue Spreading of Novel Coronavirus (COVID-19) in Ambient Air: Modeling, Prediction and Mitigation Strategies)
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14 pages, 28314 KiB  
Article
Analysis of Symptomology, Infectiveness, and Reinfections between Male and Female COVID-19 Patients: Evidence from Japanese Registry Data
by Meng-Hao Li, Abu Bakkar Siddique, Ali Andalibi and Naoru Koizumi
Atmosphere 2021, 12(11), 1528; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos12111528 - 19 Nov 2021
Viewed by 1980
Abstract
Background: Hokkaido was the first Japanese prefecture to be affected by COVID-19. Since the beginning of the pandemic, the Japanese government has been publishing the information of each individual who was tested positive for the virus. Method: The current study analyzed the 1269 [...] Read more.
Background: Hokkaido was the first Japanese prefecture to be affected by COVID-19. Since the beginning of the pandemic, the Japanese government has been publishing the information of each individual who was tested positive for the virus. Method: The current study analyzed the 1269 SARS-CoV-2 cases confirmed in Hokkaido in order to examine sex-based differences in symptomology and infectiveness, as well as the status of reinfections and the viral transmission networks. Results: The majority of asymptomatic patients were females and older. Females were 1.3-fold more likely to be asymptomatic (p < 0.001) while a decade of difference in age increased the likelihood of being asymptomatic by 1% (p < 0.001). The data contained information up to quaternary viral transmission. The transmission network revealed that, although asymptomatic patients are more likely to transmit the virus, the individuals infected by asymptomatic cases are likely to be asymptomatic (p < 0.001). Four distinct co-occurrences of symptoms were observed, including (i) fever/fatigue, (ii) pharyngitis/rhinitis, (iii) ageusia/anosmia, and (iv) nausea/vomiting/diarrhea. The presences of diarrhea (p = 0.05) as well as nausea/vomiting (p < 0.001) were predictive of developing dyspnea, i.e., severe disease. About 1% of the patients experienced reinfection. Conclusions: Sex and symptomatology appear to play important roles in determining the levels of viral transmission as well as disease severity. Full article
(This article belongs to the Special Issue Spreading of Novel Coronavirus (COVID-19) in Ambient Air: Modeling, Prediction and Mitigation Strategies)
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