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, Anamaria de Oliveira; Cardoso, Renato Fleury; Utsumi, Alex Garcez; Sousa, Nádia Guimarães (Seven Editora, 2023)
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