During pandemics such as COVID-19, shortages of approved respirators necessitate the use of alternative masks, including homemade designs. The effectiveness of the masks is often quantified in terms of the ability to filter particles. However, to formulate public policy the efficacy of the mask in reducing the risk of infection for a given population is considerably more useful than its filtration efficiency (FE). The effect of the mask on the infection profile is complicated to estimate as it depends strongly upon the behavior of the affected population. A recently introduced tool known as the dynamic-spread model is well suited for performing population-specific risk assessment. The dynamic-spread model was used to simulate the performance of a variety of mask designs (all used for source control only) in different COVID-19 scenarios. The efficacy of different masks was found to be highly scenario dependent. Switching from a cotton T-shirt of 8% FE to a 3-layer cotton-gauze-cotton mask of 44% FE resulted in a decrease in number of new infections of about 30% in the New York State scenario and 60% in the Harris County, Texas scenario. The results are valuable to policy makers for quantifying the impact upon the infection rate for different intervention strategies, e.g., investing resources to provide the community with higher-filtration masks.
Citation: Shayna Berman, Gavin D'Souza, Jenna Osborn, Matthew Myers. Comparison of homemade mask designs based on calculated infection risk, using actual COVID-19 infection scenarios[J]. Mathematical Biosciences and Engineering, 2023, 20(8): 14811-14826. doi: 10.3934/mbe.2023663
During pandemics such as COVID-19, shortages of approved respirators necessitate the use of alternative masks, including homemade designs. The effectiveness of the masks is often quantified in terms of the ability to filter particles. However, to formulate public policy the efficacy of the mask in reducing the risk of infection for a given population is considerably more useful than its filtration efficiency (FE). The effect of the mask on the infection profile is complicated to estimate as it depends strongly upon the behavior of the affected population. A recently introduced tool known as the dynamic-spread model is well suited for performing population-specific risk assessment. The dynamic-spread model was used to simulate the performance of a variety of mask designs (all used for source control only) in different COVID-19 scenarios. The efficacy of different masks was found to be highly scenario dependent. Switching from a cotton T-shirt of 8% FE to a 3-layer cotton-gauze-cotton mask of 44% FE resulted in a decrease in number of new infections of about 30% in the New York State scenario and 60% in the Harris County, Texas scenario. The results are valuable to policy makers for quantifying the impact upon the infection rate for different intervention strategies, e.g., investing resources to provide the community with higher-filtration masks.
| [1] |
F. Drewnick, J. Pikmann, F. Fachinger, L. Lasse Moormann, F. Sprang, S. Borrmann, Aerosol filtration efficiency of household materials for homemade face masks: Influence of material properties, particle size, particle electrical charge, face velocity, and leaks, Aerosol Sci. Technol., 55 (2021), 63–79. https://doi.org/10.1080/02786826.2020.1817846 doi: 10.1080/02786826.2020.1817846
|
| [2] |
K. H. Y. Hahn, G. Bhaduri, Mask up: Exploring cross-cultural influences on mask-making behavior during the COVID-19 pandemic, Clothing Text. Res. J., 39 (2021), 297–313. https://doi.org/10.1177/0887302X211012747 doi: 10.1177/0887302X211012747
|
| [3] |
S. Guha, A. Herman, I. A. Carr, D. Porter, R. Natu, S. Berman, et al., Comprehensive characterization of protective face coverings made from household fabrics, PLoS One, 16 (2021), e0244626. https://doi.org/10.1371/journal.pone.0244626 doi: 10.1371/journal.pone.0244626
|
| [4] | National Academies of Sciences, Engineering, and Medicine 2020, Rapid expert consultation on the effectiveness of fabric masks for the COVID-19 Pandemic, in Rapid Expert Consultations on the COVID-19 Pandemic, The National Academies Press, 2020. https://doi.org/10.17226/25776 |
| [5] |
J. Howard, A. Huang, Z. Li, Z. Tufekci, V. Zdimal, H. M. van der Westhuizen, et al., An evidence review of face masks against COVID-19, Proc. Nat. Acad. Sci., 118 (2021), e2014564118. https://doi.org/10.1073/pnas.2014564118 doi: 10.1073/pnas.2014564118
|
| [6] | C. Varallyay, N. Li, B. Case, B. Wolf, Material suitability testing for nonmedical grade community face masks to decrease viral transmission during a pandemic, Disaster Med. Public Health Prep., 15 (2021), e26–e32. https://doi.org/10.1017%2Fdmp.2020.262 |
| [7] | B. Osman, H. Mahmud, N. L. A. Rani, T. A. Ibrahim, I. Ismail, Household materials for homemade masks: how effective are they, Malays. J. Med. Health Sci., 17 (2021), 59–64. |
| [8] |
B. Maher, R. Chavez, G. C. Q. Tomaz, T. Nguyen, Y. Hassan, A fluid mechanics explanation of the effectiveness of common materials for respiratory masks, Int. J. Infect. Dis., 99 (2020), 505–513. https://doi.org/10.1016/j.ijid.2020.07.066 doi: 10.1016/j.ijid.2020.07.066
|
| [9] |
R. O. J. H. Stutt, R. Retkute, M. Bradley, C. A. Gilligan, J. Colvin, A modelling framework to assess the likely effectiveness of facemasks in combination with 'lock-down' in managing the COVID-19 pandemic, Proc. Royal Soc., 476 (2020), 20200376. https://doi.org/10.1098/rspa.2020.0376 doi: 10.1098/rspa.2020.0376
|
| [10] |
G. Giordano, F. Blanchini, R. Bruno, P. Colaneri, A. Di Filippo, A. Di Matteo, et al., Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy, Nat. Med., 26 (2020), 855–860. https://doi.org/10.1038/s41591-020-0883-7 doi: 10.1038/s41591-020-0883-7
|
| [11] |
I. Cooper, A. Mondal, C. G. Antonopoulos, A SIR model assumption for the spread of COVID-19 in different communities, Chaos, Solitons Fractals, 139 (2020), 110057. https://doi.org/10.1016/j.chaos.2020.110057 doi: 10.1016/j.chaos.2020.110057
|
| [12] |
A. L. Bertozzi, E. Franco, G. Mohler, M. B. Short, D. Sledge, The challenges of modeling and forecasting the spread of COVID-19, Proc. Nat. Acad. Sci., 117 (2020), 16732–16738. https://doi.org/10.1073/pnas.2006520117 doi: 10.1073/pnas.2006520117
|
| [13] |
C. N. Ngonghala, E. Iboi, S. Eikenberry, M. Scotch, C. R. MacIntyre, M. H. Bonds, et al., Mathematical assessment of the impact of non-pharmaceutical interventions on curtailing the 2019 novel Coronavirus, Math. Biosci., 325 (2020), 108364. https://doi.org/10.1016/j.mbs.2020.108364 doi: 10.1016/j.mbs.2020.108364
|
| [14] |
S. E. Eikenberry, M. Mancuso, E. Iboi, T. Phan, K. Eikenberry, Y. Kuang, et al., 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. Modell., 5 (2020), 293–308. https://doi.org/10.1016/j.idm.2020.04.001 doi: 10.1016/j.idm.2020.04.001
|
| [15] |
J. Fernández-Villaverde, C. I. Jones, Estimating and simulating a SIRD Model of COVID-19 for many countries, states, and cities, J. Econ. Dyn. Control, 140 (2022), 104318. https://doi.org/10.3386/w27128 doi: 10.3386/w27128
|
| [16] |
G. D'Souza, J. Osborn, S. Berman, M. Myers, Comparison of effectiveness of enhanced infection countermeasures in different scenarios, using a dynamic-spread-function model, Math. Biosci. Eng., 19 (2022), 9571–9589. https://doi.org/10.3934/mbe.2022445 doi: 10.3934/mbe.2022445
|
| [17] |
C. Deschanvres, T. Haudebourg, N. Peiffer-Smadja, K. Blanckaert, D. Boutoille, J. C. Lucet, et al., How do the general population behave with facemasks to prevent COVID-19 in the community? A multi-site observational study, Antimicrob. Resist. Infect. Control, 10 (2021), 1–6. https://doi.org/10.1186/s13756-021-00927-6. doi: 10.1186/s13756-021-00927-6
|
| [18] | K. Abid, H. Ahmed, Y. A. Bari, M. Younus, Z. P. Khambati, A. Imran, et al., Perceived barriers to facemask adherence in the COVID-19 pandemic in Pakistan-A cross-sectional survey, PLoS One, 17 (2022), e0267376. https://doi.org/10.1371%2Fjournal.pone.0267376 |
| [19] |
M. Coccia, Sources, diffusion and prediction in COVID-19 pandemic: lessons learned to face next health emergency, AIMS Public Health, 10 (2023), 145–168. https://doi.org/10.3934/publichealth.2023012 doi: 10.3934/publichealth.2023012
|
| [20] |
A. Núñez-Delgado, E. Bontempi, M. Coccia, M. Kumar, J. L. Domingo, SARS-CoV-2 and other pathogenic microorganisms in the environment, Environ. Res., 201 (2021), 111606. https://doi.org/10.1016/j.envres.2021.111606 doi: 10.1016/j.envres.2021.111606
|
| [21] | M. Coccia, Pandemic prevention: Lessons from COVID-19, Encyclopedia, 1 (2021), 433–444. https://Doi.org/10.3390/encyclopedia1020036 |
| [22] |
J. Osborn, S. Berman, S. Bender-Bier, G. D'Souza, M. Myers, Retrospective analysis of interventions to epidemics using dynamic simulation of population behavior, Math. Biosci., 341 (2021), 108712. https://doi.org/10.1016/j.mbs.2021.108712 doi: 10.1016/j.mbs.2021.108712
|
| [23] |
N. I. Stilianakis, Y. Drossinos, Dynamics of infectious disease transmission by inhalable respiratory droplets, J. R. Soc. Interface, 7 (2010), 1355–1366. https://doi.org/10.1098/rsif.2010.0026 doi: 10.1098/rsif.2010.0026
|
| [24] |
M. Myers, P. Hariharan, S. Guha, J. Yan, A mathematical model for assessing the effectiveness of protective devices in reducing risk of infection by inhalable droplets, Math. Med. Biol., 35 (2018), 1–23. https://doi.org/10.1093/imammb/dqw018 doi: 10.1093/imammb/dqw018
|
| [25] |
J. Schmitt, J. Wang, A critical review on the role of leakages in the facemask protection against SARS-CoV-2 infection with consideration of vaccination and virus variants, Indoor Air, 32 (2022), e13127. https://doi.org/10.1111/ina.13127 doi: 10.1111/ina.13127
|
Figures(2) / Tables(2)
Shayna Berman, Gavin D'Souza, Jenna Osborn, Matthew Myers. Comparison of homemade mask designs based on calculated infection risk, using actual COVID-19 infection scenarios[J]. Mathematical Biosciences and Engineering, 2023, 20(8): 14811-14826. doi: 10.3934/mbe.2023663
DownLoad: