Background: COVID-19, caused by SARS-CoV-2, is highly contagious and causes substantial morbidity and mortality. Mask usage has been advocated by health professionals to minimize its spread. Thus, it is important to develop a simulation that models SARS-CoV-2 spread in indoor environments to evaluate mask usage effectiveness. Methods: A visual computer simulation was developed with Pygame in Python 3. A virtual indoor supermarket is simulated by a given flow of customers with an initial infection percentage and mask usage percentage who enter, move around, and exit a supermarket with shelves, tables, and cashiers to demonstrate a system’s dynamic complexity, i.e., nonlinear interactions of system elements over time. A supermarket was simulated with initial infection rates of 5%, 10%, and 20% and mask use percentages of 0%, 25%, 50% 75%, and 100%. The environmental settings (e.g., shelf number and location) and total customers (N=200) were kept constant. Results: The number of infected customers increased as the percentage of mask usage decreased (p<0.01). At 5% initial infection, almost no infections were observed at 50% mask usage and greater, with a logarithmic best-fit model (R2=0.947). At 10% initial infection, the association between mask usage and decrease in number of infections was best fit with a linear model (R2=0.924). For 20% initial infection, a quadratic model was the best fit (R2=0.934). While a linear model suggests proportional decreases in infection, the quadratic model suggests more significant reductions in infections at higher rates of mask use (i.e., increasing mask usage from 5% to 10% is less impactful than from 65% to 70%). Conclusion: The results suggest that mask usage has a significant impact on decreasing COVID-19 transmission. Ideally, mask usage should be as high as possible to achieve more significant reductions in COVID-19 infections. Various parameters can be adjusted during simulation as we learn more about SARS-CoV-2 to guide policies for minimizing COVID-19 transmission.
| Published in | American Journal of Science, Engineering and Technology (Volume 7, Issue 1) |
| DOI | 10.11648/j.ajset.20220701.11 |
| Page(s) | 1-7 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
COVID-19, Transmission, Computer Simulation, Mask Usage, Modelling
| [1] | Pan X, Chen D, Xia Y, et al. (2020). Asymptomatic cases in a family cluster with SARS-CoV-2 infection. The Lancet. 20 (4), 410-411. |
| [2] | He, X., Lau, E. H. Y., Wu, P. et al. (2020). Temporal dynamics in viral shedding and transmissibility of COVID-19. Nature Medicine, 26, 672–675. doi: https://doi.org/10.1038/s41591-020-0869-5. |
| [3] | Timeline: WHO’s COVID-19 response. (2021). https://www.who.int/emergencies/diseases/novel-coronavirus-2019/interactive-timeline. Accessed December 29, 2021. |
| [4] | Emanuel EJ, Persad G, Upshur R, et al. (2020). Fair Allocation of Scarce Medical Resources in the Time of COVID-19. N Engl J Med. 382 (21), 2049-2055. doi: 10.1056/NEJMsb2005114. |
| [5] | Soumya RS, Unni TG, Raghu KG. (2020). Impact of COVID-19 on the Cardiovascular System: A Review of Available Reports. Cardiovasc Drugs Ther. 1-15. doi: 10.1007/s10557-020-07073-y. |
| [6] | Kimball A. (2020). Asymptomatic and Presymptomatic SARS-CoV-2 Infections in Residents of a Long-Term Care Skilled Nursing Facility — King County, Washington, March 2020. MMWR Morb Mortal Wkly Rep. 69. doi: 10.15585/mmwr.mm6913e1. |
| [7] | Smith T. (2021). The Battle Between The Masked And The Masked-Nots Unveils Political Rifts. (2021). NPR.org. https://www.npr.org/2020/05/29/864515630/the-battle-between-the-masked-and-the-masked-nots-unveils-political-rifts. Accessed December 29, 2021. |
| [8] | Eikenberry SE, Mancuso M, Iboi E, et al. (2020). To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic. Infect Dis Model. 5, 293-308. doi: 10.1016/j.idm.2020.04.001. |
| [9] | Iwata K, Miyakoshi C. (2020). A Simulation on Potential Secondary Spread of Novel Coronavirus in an Exported Country Using a Stochastic Epidemic SEIR Model. J Clin Med. 9 (4), 944. doi: 10.3390/jcm9040944. |
| [10] | Malkov E. (2020). Simulation of coronavirus disease 2019 (COVID-19) scenarios with possibility of reinfection. Chaos Solitons Fractals. 139: 110296. doi: 10.1016/j.chaos.2020.110296. |
| [11] | Morawska L, Tang JW, Bahnfleth W, et al. (2020). How can airborne transmission of COVID-19 indoors be minimised? Environ Int. 142: 105832. doi: 10.1016/j.envint.2020.105832. |
| [12] | Konda A, Prakash A, Moss GA, et al. (2020). Aerosol Filtration Efficiency of Common Fabrics Used in Respiratory Cloth Masks. ACS Nano. 14 (5), 6339-6347. doi: 10.1021/acsnano.0c03252. |
| [13] | Watanabe T, Bartrand TA, Weir MH, et al. (2010). Development of a Dose-Response Model for SARS Coronavirus. Risk Anal. 30 (7), 1129-1138. doi: 10.1111/j.1539-6924.2010.01427.x. |
| [14] | Tang JW, Nicolle AD, Klettner CA, et al. (2013). Airflow Dynamics of Human Jets: Sneezing and Breathing - Potential Sources of Infectious Aerosols. PLoS ONE. 8 (4). doi: 10.1371/journal.pone.0059970. |
| [15] | Dechter R, Pearl J. (1985). Generalized best-first search strategies and the optimality of A*. J ACM. 32 (3), 505-536. doi: 10.1145/3828.3830. |
| [16] | Bandiera L, Pavar G, Pisetta G, et al. (2020). Face coverings and respiratory tract droplet dispersion. R Soc Open Sci. 7 (12): 201663. doi: 10.1098/rsos.201663. |
| [17] | Howard J, Huang A, Li Z, et al. (2021). An evidence review of face masks against COVID-19. Proc Natl Acad Sci U S A. 118 (4). doi: 10.1073/pnas.2014564118. |
| [18] | Rader B, White LF, Burns MR, et al. (2021). Mask-wearing and control of SARS-CoV-2 transmission in the USA: a cross-sectional study. Lancet Digit Health. doi: 10.1016/S2589-7500(20)30293-4. |
| [19] | Pilishvili T, Gierke R, Fleming-Dutra KE, et al. (2021). Effectiveness of mRNA COVID-19 Vaccine among U.S. Health Care Personnel. N Engl J Med. 385: e90. doi: 10.1056/NEJMoa2106599. |
| [20] | Lauer SA, Grantz KH, Bi Q, et al. The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. (2020). Ann Intern Med. doi: 10.7326/M20-0504. |
| [21] | Li Ka Shing Faculty of Medicine, The University of Hong Kong. (2021). HKUMed finds Omicron SARS-CoV-2 can infect faster and better than Delta in human bronchus but with less severe infection in lung. http://www.med.hku.hk/en/news/press/20211215-omicron-sars-cov-2-infection. Accessed December 29, 2021. |
APA Style
Roger Lacson, Peter Veldkamp, Conrad Zapanta. (2022). Assessing the Impact of Mask Usage on COVID-19 Transmission Using a Computer Simulation. American Journal of Science, Engineering and Technology, 7(1), 1-7. https://doi.org/10.11648/j.ajset.20220701.11
ACS Style
Roger Lacson; Peter Veldkamp; Conrad Zapanta. Assessing the Impact of Mask Usage on COVID-19 Transmission Using a Computer Simulation. Am. J. Sci. Eng. Technol. 2022, 7(1), 1-7. doi: 10.11648/j.ajset.20220701.11
AMA Style
Roger Lacson, Peter Veldkamp, Conrad Zapanta. Assessing the Impact of Mask Usage on COVID-19 Transmission Using a Computer Simulation. Am J Sci Eng Technol. 2022;7(1):1-7. doi: 10.11648/j.ajset.20220701.11
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.ajset.20220701.11,
author = {Roger Lacson and Peter Veldkamp and Conrad Zapanta},
title = {Assessing the Impact of Mask Usage on COVID-19 Transmission Using a Computer Simulation},
journal = {American Journal of Science, Engineering and Technology},
volume = {7},
number = {1},
pages = {1-7},
doi = {10.11648/j.ajset.20220701.11},
url = {https://doi.org/10.11648/j.ajset.20220701.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajset.20220701.11},
abstract = {Background: COVID-19, caused by SARS-CoV-2, is highly contagious and causes substantial morbidity and mortality. Mask usage has been advocated by health professionals to minimize its spread. Thus, it is important to develop a simulation that models SARS-CoV-2 spread in indoor environments to evaluate mask usage effectiveness. Methods: A visual computer simulation was developed with Pygame in Python 3. A virtual indoor supermarket is simulated by a given flow of customers with an initial infection percentage and mask usage percentage who enter, move around, and exit a supermarket with shelves, tables, and cashiers to demonstrate a system’s dynamic complexity, i.e., nonlinear interactions of system elements over time. A supermarket was simulated with initial infection rates of 5%, 10%, and 20% and mask use percentages of 0%, 25%, 50% 75%, and 100%. The environmental settings (e.g., shelf number and location) and total customers (N=200) were kept constant. Results: The number of infected customers increased as the percentage of mask usage decreased (p2=0.947). At 10% initial infection, the association between mask usage and decrease in number of infections was best fit with a linear model (R2=0.924). For 20% initial infection, a quadratic model was the best fit (R2=0.934). While a linear model suggests proportional decreases in infection, the quadratic model suggests more significant reductions in infections at higher rates of mask use (i.e., increasing mask usage from 5% to 10% is less impactful than from 65% to 70%). Conclusion: The results suggest that mask usage has a significant impact on decreasing COVID-19 transmission. Ideally, mask usage should be as high as possible to achieve more significant reductions in COVID-19 infections. Various parameters can be adjusted during simulation as we learn more about SARS-CoV-2 to guide policies for minimizing COVID-19 transmission.},
year = {2022}
}
TY - JOUR T1 - Assessing the Impact of Mask Usage on COVID-19 Transmission Using a Computer Simulation AU - Roger Lacson AU - Peter Veldkamp AU - Conrad Zapanta Y1 - 2022/01/24 PY - 2022 N1 - https://doi.org/10.11648/j.ajset.20220701.11 DO - 10.11648/j.ajset.20220701.11 T2 - American Journal of Science, Engineering and Technology JF - American Journal of Science, Engineering and Technology JO - American Journal of Science, Engineering and Technology SP - 1 EP - 7 PB - Science Publishing Group SN - 2578-8353 UR - https://doi.org/10.11648/j.ajset.20220701.11 AB - Background: COVID-19, caused by SARS-CoV-2, is highly contagious and causes substantial morbidity and mortality. Mask usage has been advocated by health professionals to minimize its spread. Thus, it is important to develop a simulation that models SARS-CoV-2 spread in indoor environments to evaluate mask usage effectiveness. Methods: A visual computer simulation was developed with Pygame in Python 3. A virtual indoor supermarket is simulated by a given flow of customers with an initial infection percentage and mask usage percentage who enter, move around, and exit a supermarket with shelves, tables, and cashiers to demonstrate a system’s dynamic complexity, i.e., nonlinear interactions of system elements over time. A supermarket was simulated with initial infection rates of 5%, 10%, and 20% and mask use percentages of 0%, 25%, 50% 75%, and 100%. The environmental settings (e.g., shelf number and location) and total customers (N=200) were kept constant. Results: The number of infected customers increased as the percentage of mask usage decreased (p2=0.947). At 10% initial infection, the association between mask usage and decrease in number of infections was best fit with a linear model (R2=0.924). For 20% initial infection, a quadratic model was the best fit (R2=0.934). While a linear model suggests proportional decreases in infection, the quadratic model suggests more significant reductions in infections at higher rates of mask use (i.e., increasing mask usage from 5% to 10% is less impactful than from 65% to 70%). Conclusion: The results suggest that mask usage has a significant impact on decreasing COVID-19 transmission. Ideally, mask usage should be as high as possible to achieve more significant reductions in COVID-19 infections. Various parameters can be adjusted during simulation as we learn more about SARS-CoV-2 to guide policies for minimizing COVID-19 transmission. VL - 7 IS - 1 ER -
Information
Email: