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Conteúdo (article):

Effect of vaccination in Brazil against COVID-19 and its behavior against variants of concern and their sublineages

Paulo Monteiro Araujo1*, paulomonteirothe@gmail.com - orcid.org/0000-0002-5829-6268/
Susan Catherine Lima Lemos2, susanlemos@ufpi.edu.br - orcid.org/0000-0002-0090-1234/
Maria Eduarda de Carvalho Barbosa3, eduardacarvalhob@ufpi.edu.br - orcid.org/0009-0007-8522-4861/
Igor Frederico da Silveira Ramos1, igorfrederico10@gmail.com - https://orcid.org/0000-0003-3589-7578/
Marcília Pinheiro da Costa1, marciliapc@ufpi.edu.br - orcid.org/0000-0001-6093-6781/
Marcia dos Santos Rizzo1,4, marciarizzo@ufpi.edu.br - orcid.org/0000-0003-4276-3113/

1 Programa de Pós-Graduação em Ciências Farmacêuticas, Universidade Federal do Piauí, Teresina - PI, Brasil.
2 Bacharelado em Enfermagem, Universidade Federal do Piauí, Teresina - PI, Brasil.
3 Bacharelado em Medicina, Universidade Federal do Piauí, Teresina - PI, Brasil.
4 Programa de Pós-Graduação em Ciências e Saúde, Universidade Federal do Piauí, Teresina - PI, Brasil.

Abstract
This study aims to evaluate the effect of vaccination in Brazil against COVID-19 by studying correlations. An ecological study was carried out that looked at the number of cases, stratifying them by the variants of concern (VOC) of SARS-CoV-2 and its sublineages, deaths, vaccination coverage and stringency index, evaluating the relationship of these variables using Kendall\'s tau correlation coefficient (τ). It was possible to observe a strong correlation between the increase in vaccination coverage, total vaccinations (τ=-0.702, p<0.001, 95%CI [-0.723; -0.679]) and immunized individuals (τ=-0.717, p<0.001, 95%CI [-0.737; -0.695]), and the reduction in new deaths. There is also a relationship with the decrease in new cases, although considerably weaker. When stratifying by VOC, what stands out is what was observed in Omicron and its sublineages, whose vaccination proved incapable of containing, suggesting that the negative correlation (τ=-0.199, p<0.001, 95%CI [-0.249; -0.141]) between the number of estimated cases and the stringency index may be part of the explanation. The COVID-19 pandemic has required a cohesive, global effort to deal with it, and the effect of vaccination on reducing new cases and deaths for the Brazilian population is undeniable; however, it is important to combine these measures with other preventive actions.
Keywords: SARS-CoV-2; Immunization Programs; Pandemics.

Introduction
At the end of 2019, the Chinese office of the WHO (World Health Organization) received information about cases of pneumonia with an unknown cause in the city of Wuhan - China1. This situation continued to evolve until January 30, 2020, following the advice of the Emergency Committee convened under the International Health Regulations, WHO Director-General Tedros Adhanom declared a Public Health Emergency of International Concern. This is considered to be the WHO\'s highest level of alert, applied to control the global outbreak of COVID-19, known as Coronavirus Disease 2019, and this state of emergency will last until May 5, 2023. As of this date, almost 7 million deaths related to COVID-19 have been reported to the WHO, but the estimated number could exceed 20 million2.
One of the measures to control COVID-19 was vaccination, which began in January 2021 in Brazil after strong pressure from society on the federal government3. In Brazil, certain factors negatively influenced the response capacity. For some authors, the national and centralized command to face the pandemic did not occur4. Another point to be taken into account was the dispute between the Brazilian federative entities - Union, States, Municipalities and the Federal District5.
In addition, there is concern about new infections, especially when we consider variants of concern (VOC), as these can totally or partially escape previously acquired immunity. Knowing the mutations they carry is an initial step towards studies demonstrating the effectiveness of this protection6. For this to happen, there must be tracking of the spread and viral genetic drift of SARS-CoV-27. Although most mutations have little or no effect on viral properties, some may affect infectivity, pathogenicity, performance of available vaccines, therapies and diagnostics, and other measures of social and/or public health control. VOCs can have a substantial impact on the ability of health systems to provide effective patient care, requiring major public health interventions8. At present, VOCs have Greek letters assigned to their classification, which are called Alpha, Beta, Gamma, Delta and Omicron9.
One of the platforms that has made it possible to monitor these genomic changes is the Global Initiative on Sharing All Influenza Data (GISAID). This surveillance includes clinical, epidemiological and genetic sequencing data. For SARS-CoV-2, millions of sequences are available on the platform, and by mid-February 2024 this number exceeded 16 million10. One of the projects using GISAID data is CoV-Spectrum, which aims to help track VOC and facilitate their early identification, allowing a multifaceted view of the variant11. Another database that has enabled a better understanding of the COVID-19 pandemic is Our World In Data (OWID). It is thought that one of the main reasons why greater progress has not been made in knowledge about, for example, the epidemiological chain and characteristics of infectious agents, is due to the precarious use of existing research and data12.
The aim of this study was to evaluate the effect of vaccination in Brazil in the fight against COVID-19 through correlations, from its implementation until the end of the state of emergency declared by the WHO, through some variables such as the Stringency Index, number of deaths and cases stratified by the numbers estimated by VOC and underlining. This study made possible to find points for improvement that can be implemented as preventive measures for future pandemics.

Materials and Methods
An ecological study was carried out summarizing the number of COVID-19 cases, stratified by VOC and SARS-CoV-2 sublineages, deaths and vaccination coverage data. Subsequently, correlations were performed in order to evaluate the behavior of these variables in the Brazilian population.
The period covered was from 01/01/2020 to 05/05/2023, with general data extraction through the OWID database, which takes part of its data from the World Health Organization Coronavirus Dashboard. These include, new cases (smoothed) - new confirmed cases of COVID-19 (smoothed by 7 days), new deaths (smoothed) - new deaths attributed to COVID-19 (smoothed by 7 days), population - population estimated by the United Nations, total vaccinations - total doses administered of the COVID-19 vaccine, people fully vaccinated - total number of individuals who received all the doses prescribed by the initial vaccination protocol for COVID-19, booster doses - number of doses in addition to the initial vaccination protocol for COVID-1912.
The Stringency Index is calculated by the Oxford COVID-19 Government Response Tracker, based on the average of nine metrics, each assuming a value between 0 and 100, such as: closure of schools and workplaces; cancellation of public events; restrictions on crowds, public transportation, domestic and international travel; stay-at-home measures and public information campaigns13.
Data on VOCs and their underlining were obtained on May 17, 2024, through CoV-Spectrum, which uses genomic sequences from GISAID and NCBI GenBank, subsequently running Nextclade to obtain aligned sequences, assigning the clade, mutations and phylogenetic positioning11.
The information was compiled using Microsoft Excel® software in its Microsoft 365 version. Statistical analysis was carried out using JASP 0.18.2 software, using Kendall\'s tau correlation coefficient, due to the repetition of values caused by the smoothing of measurements, which sometimes has a spatial collection, following the recommendations found in the Goss-Sampson guide14. For correlations with VOCs, those without any measurable cases were excluded. This exclusion criterion was aimed at better exploration within their period of dominance and the long period explored. The graphs presented in this paper were generated using OriginPro software, version 2024. All the data used, as well as the save files for the aforementioned programs, can be found at the following link: https://doi.org/10.48331/scielodata.GDVK7C.
For all the statistical analyses, a p-value ≤ 0.05 was considered significant and the correlations were interpreted according to the recommendations of Schober and Schwarte15. The bootstrap confidence interval technique was used with 1,000 replicates and cells with significant correlations were colored in shades according to their values: negative cells were colored blue and positive cells were colored red.

Results
According to Table 1, it is necessary to detail the booster doses, which obtained 58.70% coverage, assuming only one dose per individual, although there were those who received more than one booster dose. As for the number of cases, it should be noted that there is no distinction by individual. Assuming that each person was infected only once and this was listed in the OWID database, it was found that 17.39% of the Brazilian population would have been infected with SARS-CoV-2 and that 0.33% had died related with COVID-19.
In Figure 1, the relationship between the number of new cases and deaths, and the progression of vaccination in Brazil is presented. The first vaccination recorded on OWID took place on 01/17/2021, with the first full vaccination protocol being completed on 02/05/2021. Booster doses began on 09/02/2021, around seven months after the start of vaccination.
It is also interesting to note, in Figure 1, the inversion, within the proportionality of the measures studied in the graph, between the number of cases and deaths that occurred on 08/01/2022, which visually shows an observable change in the lethality of COVID-19. At that time, the coverage of immunized individuals was 66.82%, with 13.1% coverage per booster dose, and a downward trend in the stringency index was also observed as well as the beginning of domination by the Omicron variant (Figure 2).
Table 2 displays the estimated number of cases for each VOC and its sublineages. The largest number of cases was caused by the Omicron VOC, followed by Gamma. Beta had a low number of sequences and was excluded from the correlation tests carried out later. The amount of sequencing coverage in relation to the number of cases varied considerably, peaking during the outbreak caused by VOC Delta and its sublineages.
Figure 2 shows the relationship between the number of new cases and deaths related to COVID-19 and the VOCs and their sublineages. It is worth noting that there is generally a domination by each of the VOCs, starting with Gamma. It can be seen that the peaks of deaths that occur within the period of these last three VOCs become increasingly weaker as the observed period progresses.
Table 3, in its first part, shows the relationship between some variables characterizing the progression of vaccination in Brazil in relation to deaths and cases related to COVID-19. There is a strong correlation between the increase in vaccination coverage, total vaccinations (τ =-0.702, p<0.001, 95%CI [-0.723; -0.679]) and immunized individuals (τ=-0.717, p<0.001, 95%CI [-0.737; -0.695]), and the reduction in new deaths. There is also a correlation between the reduction in cases, although considerably weaker than that linked to the reduction in deaths, total vaccinations (τ=-0.408, p<0.001, 95%CI [-0.443; -0.370]) and immunized individuals (τ=-0.409, p<0.001, 95%CI [-0.446; -0.370]). For booster doses, the correlation with new cases becomes much weaker, but still shows a moderate correlation for the reduction in COVID-19-related deaths (τ=-0.546 p<0.001, 95%CI [-0.582; -0.509]).
In the second part of Table 3, now stratified by VOC and its sublineages, there is a correlation between the variants, with Alpha being succeeded more strongly by Gamma in terms of the frequency of deaths. During the period of domination by Gama, the correlation coincides with the start of the vaccination effort and continues to increase over time. However, no correlation was observed for VOC Delta and Omicron. The total number of vaccinations and people fully vaccinated correlates with the reduction in the number of cases of VOC Alpha and Gamma, with a slight correlation for Delta and an insignificant one for Omicron.
Booster doses correlated with a reduction in cases of the Gamma and Delta variants, but not of Omicron (τ=-0.081, p=0.015, 95%CI [-0.189; 0.016]). It should be noted that during the last two peaks of Omicron cases, there was a slowdown in the progression of booster doses and a notable reduction in the stringency index (Figure 1).
The stringency index is positively correlated with the VOC Alpha and Gamma, while for the Omicron (τ=-0.199, p<0.001, 95%CI [-0.249; -0.141]) this relationship becomes weakly negative. In other words, there is a relationship whereby when there is an increase in cases, there is a decrease in the stringency index, or vice versa (Table 3). This finding could be one of the reasons for the vaccination\'s inability to effectively contain the Omicron variant.

Discussion
Coping with the pandemic began with non-pharmacological measures, such as closing schools, social distancing, isolating symptomatic cases and elderly individuals16. Supplementing these measures with the use of vaccines was seen as a way of making these preventive actions even more effective, especially if there was prioritization of population groups, such as the elderly and vulnerable individuals17. Both actions, non-pharmacological and pharmacological (immunization through vaccines), were based on mathematical models regarding their benefit16,17.
This synergism was also important in controlling the variants, especially in the case of VOCs. For example, an epidemic model found that a rapid vaccination rate decreased the likelihood of a resistant variant. In light of this, it is worth noting that when there was a relaxation of non-pharmacological interventions at a time when the majority of individuals in the population had been vaccinated, the likelihood of resistant variants appearing increased significantly18.
This type of synergistic behavior, such as that observed in the reduction in the stringency index (Figure 1 and Table 3), may be part of the explanation for the Omicron variant, which had an explosion of cases despite growing and considerable vaccination coverage. However, the number of observable deaths was lower than for VOC and its antecedent sublineages, as highlighted by Figure 2. This finding was already observed by Moura et al. in the period from 2020 to 2022. The authors characterize three waves of COVID-19, as well the effect of immunization in reducing mortality in the second and third wave, with the last being attributed to VOC Omicron, but vaccination was not able to contain the new cases. It is in this period that the highest peak ever observed of new cases of this disease occurs4.
Moreover, it is important to highlight the effectiveness of vaccination, which was proven by Victora et al., who evaluated the impact of vaccination on the mortality of elderly Brazilians during the period of widespread transmission of Gamma VOC. The rapid increase in vaccination coverage for this population reduced relative mortality compared to younger individuals, estimating that if mortality rates remained at the pre-vaccination level, an additional 43,802 deaths would have occurred by the end of the study period19.
This study evaluated the correlations of several variables, demonstrating the relationship between the reduction in the number of cases and deaths as a result of the efforts of vaccination programs. The number of new deaths related to COVID-19 was strongly negatively related to vaccination, that is, with increased vaccination coverage there was a decrease in deaths related to this disease. When this is stratified by VOC, the number of new deaths was related to the cases observed for the first variants (Alpha and Gamma), at a time when the vaccination effort was growing in the country.
We should point out that the emergence of SARS-CoV-2 variants and their sublineages has shown that reinfections can occur, despite of the acquired immunity caused by primoinfection or vaccination. A study using data from the Netherlands, between March and August 2021, showed an increased risk of infection by the VOC Beta, Gamma and Delta compared to Alpha after vaccination, this risk being higher between 14 and 59 days post-vaccination compared to >60 days. However, in contrast to vaccine-induced immunity, there was no increased risk of re-infection with Beta, Gamma or Delta variants compared to the Alpha variant in individuals with infection-induced immunity20. Other studies have shown that, during the Delta VOC period, primoinfection conferred a better immune response than vaccination without prior infection. In any case, the combination of vaccination and prior infection induced better immune protection. Primary infection carries nevertheless a risk of hospitalization or death, especially in older people or those with comorbidities21,22.
In Brazil, the variant that correlated most strongly with deaths was Gamma (Figure 2 and Table 3). It is worth highlighting what happened in the city of Manaus, in the state of Amazonas, where the local health system collapsed due to the increase in the infection rate and the severity of the condition23, reaching high numbers of cases and deaths from March to June 2022, with a peak of 3000 deaths per day, using the moving average24.
This VOC was primarily detected in patients returning to Japan after passing through the state of Amazonas and had twelve mutations, including three in the region of the receptor binding domain (RBD)25. Starting at the time of the end of the year celebrations24, there was considerable relaxation of control measures in previous months, as seen in the drop in the stringency index in Figure 1. This variant proved to be more transmissible than the other variants previously circulating in this region. This can be explained by the mutations in the RBD of its Spike protein as well as a higher viral load compared to other variants25,26.
In the case of the correlations for booster doses, these were able to considerably reduce the number of new cases of Delta VOC. For this VOC, the evaluation of the sera of individuals vaccinated with the BNT162b2 (Pfizer/BioNTech) and ChAdOx1 (Oxford/AstraZeneca) vaccines, used widely in Brazil, demonstrated the ability to neutralize to this VOC. The accumulation of mutations in VOCs demonstrates the risk of antigenic drift and subsequent reduction in the efficacy of a vaccine. This is why it is necessary, as time goes by, for booster vaccines to be based on updated variants for this virus27.
The Omicron VOC in Brazil belongs to the third wave of transmission of SARS-CoV-2, which begins in December 2021, again coinciding with the end of the year period and a considerable relaxation of control measures. This VOC and its sublineages quickly become predominant24. It was first identified in Botswana 28 and in South Africa29. It has come to be considered the most divergent of the variants by the WHO since its beginning, evolving genetically and antigenically, with an increasing amount of sublineages and mutations. Some of these are characterized by their ability to evade immunity in the population, as well as preferentially infecting the upper respiratory tract rather than the lower, when compared to previous VOCs9.
It is worth noting that the neutralization assay carried out with South African individuals vaccinated with BNT162b2 (Pfizer/BioNTech) showed that, for VOC Omicron, the escape from acquired immunity had not been complete, as seroneutralization had still occurred, although with less strength compared to VOC Beta and the ancestral virus with the D614G mutation. The ancestral strains with this mutation were responsible for the first wave of SARS-CoV-2 infections29. This suggests that recent vaccination, or booster doses, may increase the level of neutralization and promote protection against the severe forms of Omicron VOC infection28. It should be noted that updating vaccines can improve their neutralization capacity, as observed in a Brazilian article carried out in the city of Barreiras (Bahia), showing that individuals vaccinated with the bivalent version of the vaccine updated for the Omicron sublineages (BA.4-5) had greater neutralization capacity, even for the original virus and for some sublineages of this VOC30.
For the period studied, it was not possible to observe any significant correlations between the vaccination effort and the reduction in Omicron cases, but as discussed above, vaccination is only part of the equation for controlling new cases and deaths from a variant and should not be interpreted as a lack of vaccine efficacy in relation to this variable. The decrease in the stringency index itself provides part of the explanation for what happened, but this index does not assess the population\'s real compliance with preventive measures, but rather the government policies implemented.
It should be understood that an individual\'s protection begins even before their exposure to a pathogen, and is understood, in part, by the Behavioral Immune System, a field of study that suggests that humans and other animals have a set of psychological adaptations that allow them to detect the threat of a disease and activate behaviors that avoid exposure to a particular pathogen. In a study conducted in Croatia, between the first and second wave of COVID-19, gender, age, perceived personal risk, trust in science, germ aversion and belief in the second wave, organized in ascending order, were positively correlated with the intention to adhere to non-pharmacological recommendations. For vaccination, only belief in the second wave and trust in science were significant positive predictors, and these correlations were stronger compared to the non-pharmacological measures31.
Comparing these results with those obtained in our study, we can see a very different reality from that in Brazil. However, it is worth reflecting on how the COVID-19 pandemic could have been shortened if there had been a stronger relationship of trust with science and a more effective understanding of the risks and impacts of COVID-19 on individuals, rather than it being considered a simple cold or flu. Individual and collective attitudes can contribute to strengthening and establishing an effective vaccination policy. Wisdom is an essential part of this process and must be clear, truthful and widely available to all Brazilians.

Conclusion
The COVID-19 pandemic caused by SARS-CoV-2 has taken the world\'s population by surprise, necessitating a cohesive, global effort to deal effectively with it. The end of its effects is still on a distant horizon, but the effectiveness of vaccination in reducing cases and deaths for the population during the period studied is undeniable. When stratifying these comparisons for the SARS-CoV-2 VOCs, initial immunization reduced the appearance of new cases for the Alpha VOC, and even more strongly for the Gamma VOC, the deadliest of the VOCs. Booster doses have strongly reduced notifications of new cases of the Delta VOC. For Omicron it was not possible to observe correlations with the vaccination effort, but this does not attest to vaccine ineffectiveness, since other variables such as the reduction in the implementation of non-pharmacological measures may be influencing this relationship, as observed in the reduction in the stringency index during this period. It was not possible to carry out correlations for VOC Beta due to the small number of cases detected in Brazil.

Acknowledgements
We would like to thank all the individuals who contributed indirectly to this work, especially those who are part of Our World in Data (OWID), the Oxford COVID-19 Government Response Tracker (OxCGRT), CoV-Spectrum, the Global Initiative on Sharing All Influenza Data (GISAID) and Jeffrey\'s Amazing Statistics Program (JASP).

Funding
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, FAPEPI (Fundação de Amparo à Pesquisa do Estado do Piauí) and CNPq, Conselho Nacional de Desenvolvimento Científico e Tecnológico – Brasil.

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