For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
29
views
Article has an altmetric score of 12
23
references
 Top references
cited by
1
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
1 collections
    0
    shares
      472
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Artificial intelligence tool for the study of COVID-19 microdroplet spread across the human diameter and airborne space

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The 2019 novel coronavirus (SARS-CoV-2 / COVID-19), with a point of origin in Wuhan, China, has spread rapidly all over the world. It turned into a raging pandemic wrecking havoc on health care facilities, world economy and affecting everyone’s life to date. With every new variant, rate of transmission, spread of infections and the number of cases continues to rise at an international level and scale. There are limited reliable researches that study microdroplets spread and transmissions from human sneeze or cough in the airborne space. In this paper, we propose an intelligent technique to visualize, detect, measure the distance of spread in a real-world settings of microdroplet transmissions in airborne space, called “COVNET45”. In this paper, we investigate the microdroplet transmission and validate the measurements accuracy compared to published researches, by examining several microscopic and visual images taken to investigate the novel coronavirus (SARS-CoV-2 / COVID-19). The ultimate contribution is to calculate the spread of the microdroplets, measure it precisely and provide a graphical presentation. Additionally, the work employs machine learning and five algorithms for image optimization, detection and measurement.

          Related collections

          Most cited references23

          • Record: found
          • Abstract: found
          • Article: not found

          Aerosol and Surface Stability of SARS-CoV-2 as Compared with SARS-CoV-1

          Neeltje van Doremalen, Trenton Bushmaker, Dylan H Morris … (2021)
          To the Editor: A novel human coronavirus that is now named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (formerly called HCoV-19) emerged in Wuhan, China, in late 2019 and is now causing a pandemic. 1 We analyzed the aerosol and surface stability of SARS-CoV-2 and compared it with SARS-CoV-1, the most closely related human coronavirus. 2 We evaluated the stability of SARS-CoV-2 and SARS-CoV-1 in aerosols and on various surfaces and estimated their decay rates using a Bayesian regression model (see the Methods section in the Supplementary Appendix, available with the full text of this letter at NEJM.org). SARS-CoV-2 nCoV-WA1-2020 (MN985325.1) and SARS-CoV-1 Tor2 (AY274119.3) were the strains used. Aerosols (<5 μm) containing SARS-CoV-2 (105.25 50% tissue-culture infectious dose [TCID50] per milliliter) or SARS-CoV-1 (106.75-7.00 TCID50 per milliliter) were generated with the use of a three-jet Collison nebulizer and fed into a Goldberg drum to create an aerosolized environment. The inoculum resulted in cycle-threshold values between 20 and 22, similar to those observed in samples obtained from the upper and lower respiratory tract in humans. Our data consisted of 10 experimental conditions involving two viruses (SARS-CoV-2 and SARS-CoV-1) in five environmental conditions (aerosols, plastic, stainless steel, copper, and cardboard). All experimental measurements are reported as means across three replicates. SARS-CoV-2 remained viable in aerosols throughout the duration of our experiment (3 hours), with a reduction in infectious titer from 103.5 to 102.7 TCID50 per liter of air. This reduction was similar to that observed with SARS-CoV-1, from 104.3 to 103.5 TCID50 per milliliter (Figure 1A). SARS-CoV-2 was more stable on plastic and stainless steel than on copper and cardboard, and viable virus was detected up to 72 hours after application to these surfaces (Figure 1A), although the virus titer was greatly reduced (from 103.7 to 100.6 TCID50 per milliliter of medium after 72 hours on plastic and from 103.7 to 100.6 TCID50 per milliliter after 48 hours on stainless steel). The stability kinetics of SARS-CoV-1 were similar (from 103.4 to 100.7 TCID50 per milliliter after 72 hours on plastic and from 103.6 to 100.6 TCID50 per milliliter after 48 hours on stainless steel). On copper, no viable SARS-CoV-2 was measured after 4 hours and no viable SARS-CoV-1 was measured after 8 hours. On cardboard, no viable SARS-CoV-2 was measured after 24 hours and no viable SARS-CoV-1 was measured after 8 hours (Figure 1A). Both viruses had an exponential decay in virus titer across all experimental conditions, as indicated by a linear decrease in the log10TCID50 per liter of air or milliliter of medium over time (Figure 1B). The half-lives of SARS-CoV-2 and SARS-CoV-1 were similar in aerosols, with median estimates of approximately 1.1 to 1.2 hours and 95% credible intervals of 0.64 to 2.64 for SARS-CoV-2 and 0.78 to 2.43 for SARS-CoV-1 (Figure 1C, and Table S1 in the Supplementary Appendix). The half-lives of the two viruses were also similar on copper. On cardboard, the half-life of SARS-CoV-2 was longer than that of SARS-CoV-1. The longest viability of both viruses was on stainless steel and plastic; the estimated median half-life of SARS-CoV-2 was approximately 5.6 hours on stainless steel and 6.8 hours on plastic (Figure 1C). Estimated differences in the half-lives of the two viruses were small except for those on cardboard (Figure 1C). Individual replicate data were noticeably “noisier” (i.e., there was more variation in the experiment, resulting in a larger standard error) for cardboard than for other surfaces (Fig. S1 through S5), so we advise caution in interpreting this result. We found that the stability of SARS-CoV-2 was similar to that of SARS-CoV-1 under the experimental circumstances tested. This indicates that differences in the epidemiologic characteristics of these viruses probably arise from other factors, including high viral loads in the upper respiratory tract and the potential for persons infected with SARS-CoV-2 to shed and transmit the virus while asymptomatic. 3,4 Our results indicate that aerosol and fomite transmission of SARS-CoV-2 is plausible, since the virus can remain viable and infectious in aerosols for hours and on surfaces up to days (depending on the inoculum shed). These findings echo those with SARS-CoV-1, in which these forms of transmission were associated with nosocomial spread and super-spreading events, 5 and they provide information for pandemic mitigation efforts.
          Article 0 comments Cited 4215 times – based on 0 reviews     Review now
          Article has an altmetric score of 22844
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            The airborne lifetime of small speech droplets and their potential importance in SARS-CoV-2 transmission

            Valentyn Stadnytskyi, Christina Bax, Adriaan Bax … (2020)
            Speech droplets generated by asymptomatic carriers of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are increasingly considered to be a likely mode of disease transmission. Highly sensitive laser light scattering observations have revealed that loud speech can emit thousands of oral fluid droplets per second. In a closed, stagnant air environment, they disappear from the window of view with time constants in the range of 8 to 14 min, which corresponds to droplet nuclei of ca. 4 μm diameter, or 12- to 21-μm droplets prior to dehydration. These observations confirm that there is a substantial probability that normal speaking causes airborne virus transmission in confined environments.
            Article 0 comments Cited 499 times – based on 0 reviews     Review now
            Article has an altmetric score of 8172
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Size distribution and sites of origin of droplets expelled from the human respiratory tract during expiratory activities

              L Morawska, G.R. Johnson, Z.D. Ristovski … (2009)
              Article 0 comments Cited 404 times – based on 0 reviews     Review now
              Article has an altmetric score of 754
                Bookmark
                All references

                Author and article information

                Contributors
                Hesham H. Alsaadi: Role: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draft
                Monther Aldwairi:
                ORCID: https://orcid.org/0000-0003-1150-2404
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Faten Yasin: Role: Data curationRole: Formal analysisRole: MethodologyRole: ValidationRole: Writing – original draft
                Sandra C. P. Cachinho: Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: ResourcesRole: SoftwareRole: SupervisionRole: Writing – original draft
                Abdullah Hussein: Role: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: Validation
                Anandakumar Haldorai: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS One
                Journal ID (publisher-id): plos
                Title: PLOS ONE
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Electronic): 1932-6203
                Publication date Collection: 2023
                Publication date (Electronic): 19 July 2023
                Publication date PMC-release: 19 July 2023
                Volume: 18
                Issue: 7
                Electronic Location Identifier: e0269905
                Affiliations
                [1 ] College of Technological Innovation, Zayed University, Abu Dhabi, UAE
                [2 ] Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, United Kindom
                [3 ] Cell Sorting and Isolation Facility, Research Technology Building, University of Liverpool, Liverpool, United Kindom
                [4 ] Rochester Institute of Technology, Dubai, UAE
                Sri Eshwar College of Engineering, INDIA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                [¤]

                Current address: Department of Network Engineering and Security, Jordan University of Science and Technology, Irbid, Jordan

                Author information
                https://orcid.org/0000-0003-1150-2404
                Article
                Publisher ID: PONE-D-22-15591
                DOI: 10.1371/journal.pone.0269905
                PMC ID: 10355432
                SO-VID: f39e0301-c976-499d-a689-9fded7a2e209
                Copyright © © 2023 Alsaadi et al
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 30 May 2022
                Date accepted : 20 March 2023
                Page count
                Figures: 11, Tables: 0, Pages: 19
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100008675, Zayed University;
                Award ID: R20089
                Award Recipient :
                ORCID: https://orcid.org/0000-0003-1150-2404
                MA Research Incentive Fund Grant #R20089 Zayed University Research Office www.zu.ac.ae The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Subject: Research Article
                Subject: Medicine and Health Sciences
                Subject: Medical Conditions
                Subject: Infectious Diseases
                Subject: Viral Diseases
                Subject: Covid 19
                Subject: Research and Analysis Methods
                Subject: Imaging Techniques
                Subject: Physical Sciences
                Subject: Materials Science
                Subject: Materials
                Subject: Mixtures
                Subject: Aerosols
                Subject: Medicine and Health Sciences
                Subject: Medical Conditions
                Subject: Infectious Diseases
                Subject: Respiratory Infections
                Subject: Medicine and Health Sciences
                Subject: Medical Conditions
                Subject: Respiratory Disorders
                Subject: Respiratory Infections
                Subject: Medicine and Health Sciences
                Subject: Pulmonology
                Subject: Respiratory Disorders
                Subject: Respiratory Infections
                Subject: Biology and Life Sciences
                Subject: Physiology
                Subject: Physiological Processes
                Subject: Coughing
                Subject: Medicine and Health Sciences
                Subject: Clinical Medicine
                Subject: Signs and Symptoms
                Subject: Coughing
                Subject: Engineering and Technology
                Subject: Signal Processing
                Subject: Image Processing
                Subject: Physical Sciences
                Subject: Mathematics
                Subject: Applied Mathematics
                Subject: Algorithms
                Subject: Machine Learning Algorithms
                Subject: Research and Analysis Methods
                Subject: Simulation and Modeling
                Subject: Algorithms
                Subject: Machine Learning Algorithms
                Subject: Computer and Information Sciences
                Subject: Artificial Intelligence
                Subject: Machine Learning
                Subject: Machine Learning Algorithms
                Subject: Biology and life sciences
                Subject: Organisms
                Subject: Viruses
                Subject: RNA viruses
                Subject: Coronaviruses
                Subject: SARS coronavirus
                Subject: SARS CoV 2
                Subject: Biology and life sciences
                Subject: Microbiology
                Subject: Medical microbiology
                Subject: Microbial pathogens
                Subject: Viral pathogens
                Subject: Coronaviruses
                Subject: SARS coronavirus
                Subject: SARS CoV 2
                Subject: Medicine and health sciences
                Subject: Pathology and laboratory medicine
                Subject: Pathogens
                Subject: Microbial pathogens
                Subject: Viral pathogens
                Subject: Coronaviruses
                Subject: SARS coronavirus
                Subject: SARS CoV 2
                Subject: Biology and life sciences
                Subject: Organisms
                Subject: Viruses
                Subject: Viral pathogens
                Subject: Coronaviruses
                Subject: SARS coronavirus
                Subject: SARS CoV 2
                Custom metadata
                Data Availability COVNET45 is currently hosted in AWS and can be integrated with the AMAZON AI machine learning platform Tool, Data and code will be shared via https://github.com/HeshamAlsaadi.
                Outbreaks COVID-19

                ScienceOpen disciplines: Uncategorized
                Data availability:
                ScienceOpen disciplines: Uncategorized

                Comments

                Comment on this article

                scite_
                0
                0
                0
                0
                Smart Citations
                0
                0
                0
                0
                Citing PublicationsSupportingMentioningContrasting
                View Citations

                See how this article has been cited at scite.ai

                scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

                Similar content472

                See all similar

                Cited by1

                See all cited by

                Most referenced authors429

                See all reference authors