Heterogeneous influence of individuals’ behavior on mask efficacy in gathering environments

Abstract

Wearing masks is an easy way to operate and popular measure for preventing epidemics. Although masks can slow down the spread of viruses, their efficacy in gathering environments involving heterogeneous person-to-person contacts remains unknown. Therefore, we aim to investigate the epidemic prevention effect of masks in different real-life gathering environments. This study uses four real interpersonal contact datasets to construct four empirical networks to represent four gathering environments. The transmission of COVID-19 is simulated using the Monte Carlo simulation method. The heterogeneity of individuals can cause mask efficacy in a specific gathering environment to be different from the baseline efficacy in general society. Furthermore, the heterogeneity of gathering environments causes the epidemic prevention effect of masks to differ. Wearing masks can greatly reduce the probability of clustered epidemics and the infection scale in primary schools, high schools, and hospitals. However, the use of masks alone in primary schools and hospitals cannot control outbreaks. In high schools with social distancing between classes and in workplaces where the interpersonal contact is relatively sparse, masks can meet the need for prevention. Given the heterogeneity of individual behavior, if individuals who are more active in terms of interpersonal contact are prioritized for mask-wearing, the epidemic prevention effect of masks can be improved. Finally, asymptomatic infection has varying effects on the prevention effect of masks in different environments. The effect can be weakened or eliminated by increasing the usage rate of masks in high schools and workplaces. However, the effect on primary schools and hospitals cannot be weakened. This study contributes to the accurate evaluation of mask efficacy in various gathering environments to provide scientific guidance for epidemic prevention.

Graphical abstract

Keywords

COVID-19 / masks / behavioral heterogeneity / asymptomatic infection

References

[1] Alvarez, M M Bravo-González, S Trujillo-de, Santiago G (2021). Modeling vaccination strategies in an Excel spreadsheet: Increasing the rate of vaccination is more effective than increasing the vaccination coverage for containing COVID-19. PLoS One, 16( 7): e0254430

[2] Brienen, N C J Timen, A Wallinga, J van Steenbergen, J E Teunis, P F M (2010). The effect of mask use on the spread of influenza during a pandemic. Risk Analysis, 30( 8): 1210–1218

[3] Cattuto, C van den Broeck, W Barrat, A Colizza, V Pinton, J F Vespignani, A (2010). Dynamics of person-to-person interactions from distributed RFID sensor networks. PLoS One, 5( 7): e11596

[4] Cheng, Y Ma, N Witt, C Rapp, S Wild, P S Andreae, M O Pöschl, U Su, H (2021). Face masks effectively limit the probability of SARS-CoV-2 transmission. Science, 372( 6549): 1439–1443

[5] Chu, D K Akl, E A Duda, S Solo, K Yaacoub, S Schünemann, H J (2020). Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: A systematic review and meta-analysis. Lancet, 395( 10242): 1973–1987

[6] Cui, Q Hu, Z Li, Y Han, J Teng, Z Qian, J (2020). Dynamic variations of the COVID-19 disease at different quarantine strategies in China. Journal of Infection and Public Health, 13( 6): 849–855

[7] Davies, A Thompson, K A Giri, K Kafatos, G Walker, J Bennett, A (2013). Testing the efficacy of homemade masks: Would they protect in an influenza pandemic. Disaster Medicine and Public Health Preparedness, 7( 4): 413–418

[8] Day, M (2020). Covid-19: Four fifths of cases are asymptomatic, China figures indicate. BMJ, 369: m1375

[9] Du, Z Nugent, C Galvani, A P Krug, R M Meyers, L A (2020). Modeling mitigation of influenza epidemics by Baloxavir. Nature Communications, 11( 1): 2750–2755

[10] Gemmetto, V Barrat, A Cattuto, C (2014). Mitigation of infectious disease at school: Targeted class closure vs. school closure. BMC Infectious Diseases, 14( 1): 695–715

[11] Génois, M Vestergaard, C L Fournet, J Panisson, A Bonmarin, I Barrat, A (2015). Data on face-to-face contacts in an office building suggest a low-cost vaccination strategy based on community linkers. Network Science, 3( 3): 326–347

[12] Guan, W J Ni, Z Y Hu, Y Liang, W H Ou, C Q He, J X Liu, L Shan, H Lei, C L Hui, D S C Du, B Li, L J Zeng, G Yuen, K Y Chen, R C Tang, C L Wang, T Chen, P Y Xiang, J Li, S Y Wang, J L Liang, Z J Peng, Y X Wei, L Liu, Y Hu, Y H Peng, P Wang, J M Liu, J Y Chen, Z Li, G Zheng, Z J Qiu, S Q Luo, J Ye, C J Zhu, S Y Zhong, N S (2020). Clinical characteristics of Coronavirus Disease 2019 in China. New England Journal of Medicine, 382( 18): 1708–1720

[13] Lai, A C Poon, C K Cheung, A C (2012). Effectiveness of facemasks to reduce exposure hazards for airborne infections among general populations. Journal of the Royal Society, Interface, 9( 70): 938–948

[14] Leung, N H L Chu, D K W Shiu, E Y C Chan, K H McDevitt, J J Hau, B J P Yen, H L Li, Y Ip, D K M Peiris, J S M Seto, W H Leung, G M Milton, D K Cowling, B J (2020). Respiratory virus shedding in exhaled breath and efficacy of face masks. Nature Medicine, 26( 5): 676–680

[15] Liu, Y Rocklöv, J (2021). The reproductive number of the Delta variant of SARS-CoV-2 is far higher compared to the ancestral SARS-CoV-2 virus. Journal of Travel Medicine, 28( 7): taab124

[16] Mao, Y J Rong, Z H Wu, Z X (2021). Effect of collective influence on the evolution of cooperation in evolutionary prisoner’s dilemma games. Applied Mathematics and Computation, 392: 125679

[17] Mastrandrea, R Fournet, J Barrat, A (2015). Contact patterns in a high school: A comparison between data collected using wearable sensors, contact diaries and friendship surveys. PLoS One, 10( 9): e0136497

[18] Patterson, B Marks, M Martinez-Garcia, G Bidwell, G Luintel, A Ludwig, D Parks, T Gothard, P Thomas, R Logan, S Shaw, K Stone, N Brown, M (2020). A novel cohorting and isolation strategy for suspected COVID-19 cases during a pandemic. Journal of Hospital Infection, 105( 4): 632–637

[19] Rockx, B Kuiken, T Herfst, S Bestebroer, T Lamers, M M Oude Munnink, B B de Meulder, D van Amerongen, G van den Brand, J Okba, N M A Schipper, D van Run, P Leijten, L Sikkema, R Verschoor, E Verstrepen, B Bogers, W Langermans, J Drosten, C van Vlissingen, M F Fouchier, R de Swart, R Koopmans, M Haagmans, B L (2020). Comparative pathogenesis of COVID-19, MERS, and SARS in a nonhuman primate model. Science, 368( 6494): 1012–1015

[20] Rong, Z Wu, Z X Li, X Holme, P Chen, G (2019). Heterogeneous cooperative leadership structure emerging from random regular graphs. Chaos, 29( 10): 103103

[21] Stehlé J Voirin, N Barrat, A Cattuto, C Isella, L Pinton, J F Quaggiotto, M van, den Broeck W Régis, C Lina, B Vanhems, P (2011). High-resolution measurements of face-to-face contact patterns in a primary school. PLoS One, 6( 8): e23176

[22] Sun, H C Liu, X F Du, Z W Xu, X K Wu, Y (2021). Mitigating COVID-19 transmission in schools with digital contact tracing. IEEE Transactions on Computational Social Systems, 8( 6): 1302–1310

[23] Sun, M C Wah, C B L C (2020). Lessons to be learnt from the COVID-19 public health response in Mauritius. Public Health in Practice, 1: 100023

[24] Thu, T P B Ngoc, P N H Hai, N M Tuan, L A (2020). Effect of the social distancing measures on the spread of COVID-19 in 10 highly infected countries. Science of the Total Environment, 742: 140430

[25] Vanhems, P Barrat, A Cattuto, C Pinton, J F Khanafer, N Régis, C Kim, B A Comte, B Voirin, N (2013). Estimating potential infection transmission routes in hospital wards using wearable proximity sensors. PLoS One, 8( 9): e73970

[26] Wang, D Hu, B Hu, C Zhu, F Liu, X Zhang, J Wang, B Xiang, H Cheng, Z Xiong, Y Zhao, Y Li, Y Wang, X Peng, Z (2020). Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia. Journal of the American Medical Association, 323( 11): 1061–1069

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