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Wearing Masks in COVID-19 pandemic: Mathematical model and simulation for evaluating the impact of non-pharmaceutical intervention strategy associated with social distancing on pandemic behavior in Minas Gerais/Brazil

Cardoso AO;
Cardoso RF;
Utsumi AG;
Sousa NG

Anamaria de Oliveira Cardoso

Renato Fleury Cardoso

Alex Garcez Utsumi

Nádia Guimarães Sousa


Resumo

The COVID-19 pandemic has caused health system collapse and led governments to implement non-pharmaceutical interventions, such as wearing a mask and social distancing. In this study, the SEIR (susceptible-exposed-infected-recovered) model is proposed for assessing the effects of social distancing and wearing a mask on the prediction of COVID-19 transmission dynamic in Minas Gerais – Brazil.  This work presents a theoretical-computational study and the mathematical modeling simulations of COVID-19 transmission dynamics. The model describes eight population groups: susceptible, confined, exposed, asymptomatic, symptomatic, hospitalized, recovered, and dead. The mask-wearing is inferred by the following parameters: mask aerosol reduction (M_red  ), mask availability (M_ava), and proper mask-wearing (M_cov). Different scenarios are simulated for evaluating the effect of the parameters on the pandemic evolution. Simulations demonstrate a reduction of around 99% compared to the no-mask-wearing scenario when masks are available for 80% of the population. Professional masks, such as N95 and FFP2 (M_red=97%), reduce by 98,9% of the number of deaths. The proper mask-wearing shows a significant impact on pandemic development, by reducing considerably M_cov it could even overcome the total number of deaths and infections than those in a no-mask-wearing scenario, if the social distancing measures were not intensified. Wearing a mask is extremely efficient and required in the fight against the COVID-19 pandemic. A combination of social distancing and wearing a mask, if properly performed, allows controlling the pandemic more efficiently, minimizing the total and the daily number of deaths and infections, and avoiding a greater health system overload.

 

DOI:https://doi.org/10.56238/devopinterscie-205


Creative Commons License

Este trabalho está licenciado sob uma licença Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Copyright (c) 2023 Anamaria de Oliveira Cardoso, Renato Fleury Cardoso, Alex Garcez Utsumi, Nádia Guimarães Sousa

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  • Anamaria de Oliveira Cardoso
  • Renato Fleury Cardoso
  • Alex Garcez Utsumi
  • Nádia Guimarães Sousa