ABSTRACT
The structural spike (S) glycoprotein of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) plays an essential role in infection and is an important target for neutralizing antibody recognition. Mutations in the S gene can generate variants of concern (VOCs), which improve "viral fitness" through selective or survival advantages, such as increased ACE-2 receptor affinity, infectivity, viral replication, higher transmissibility, resistance to neutralizing antibodies and immune escape, increasing disease severity and reinfection risk. Five VOCs have been recognized and include B.1.1.7 (U.K.), B.1.351 (South Africa), P.1 (Brazil), B.1.617.2 (India), and B.1.1.529 (multiple countries). In this review, we addressed the following critical points concerning VOCs: a) characteristics of the SARS-CoV-2 VOCs with mutations in the S gene; b) possible evasion of variants from neutralizing antibodies generated through vaccination, previous infection, or immune therapies; c) potential risk of new pandemic waves induced by the variants worldwide; and d) perspectives for further studies and actions aimed at preventing or reducing the impact of new variants during the current COVID-19 pandemic.
Subject(s)
COVID-19 , SARS-CoV-2 , Antibodies, Neutralizing , Antibodies, Viral , Humans , Immune Evasion , Mutation , PandemicsABSTRACT
We propose herein a mathematical model to predict the COVID-19 evolution and evaluate the impact of governmental decisions on this evolution, attempting to explain the long duration of the pandemic in the 26 Brazilian states and their capitals well as in the Federative Unit. The prediction was performed based on the growth rate of new cases in a stable period, and the graphics plotted with the significant governmental decisions to evaluate the impact on the epidemic curve in each Brazilian state and city. Analysis of the predicted new cases was correlated with the total number of hospitalizations and deaths related to COVID-19. Because Brazil is a vast country, with high heterogeneity and complexity of the regional/local characteristics and governmental authorities among Brazilian states and cities, we individually predicted the epidemic curve based on a specific stable period with reduced or minimal interference on the growth rate of new cases. We found good accuracy, mainly in a short period (weeks). The most critical governmental decisions had a significant temporal impact on pandemic curve growth. A good relationship was found between the predicted number of new cases and the total number of inpatients and deaths related to COVID-19. In summary, we demonstrated that interventional and preventive measures directly and significantly impact the COVID-19 pandemic using a simple mathematical model. This model can easily be applied, helping, and directing health and governmental authorities to make further decisions to combat the pandemic.