Dynamic transmission modeling of COVID-19 to support decision-making in Brazil: A scoping review in the pre-vaccine era.

Brazil was one of the countries most affected during the first year of the COVID-19 pandemic, in a pre-vaccine era, and mathematical and statistical models were used in decision-making and public policies to mitigate and suppress SARS-CoV-2 dispersion. In this article, we intend to overview the modeling for COVID-19 in Brazil, focusing on the first 18 months of the pandemic. We conducted a scoping review and searched for studies on infectious disease modeling methods in peer-reviewed journals and gray literature, published between January 01, 2020, and June 2, 2021, reporting real-world or scenario-based COVID-19 modeling for Brazil. We included 81 studies, most corresponding to published articles produced in Brazilian institutions. The models were dynamic and deterministic in the majority. The predominant model type was compartmental, but other models were also found. The main modeling objectives were to analyze epidemiological scenarios (testing interventions' effectiveness) and to project short and long-term predictions, while few articles performed economic impact analysis. Estimations of the R0 and transmission rates or projections regarding the course of the epidemic figured as major, especially at the beginning of the crisis. However, several other outputs were forecasted, such as the isolation/quarantine effect on transmission, hospital facilities required, secondary cases caused by infected children, and the economic effects of the pandemic. This study reveals numerous articles with shared objectives and similar methods and data sources. We observed a deficiency in addressing social inequities in the Brazilian context within the utilized models, which may also be expected in several low- and middle-income countries with significant social disparities. We conclude that the models were of great relevance in the pandemic scenario of COVID-19. Nevertheless, efforts could be better planned and executed with improved institutional organization, dialogue among research groups, increased interaction between modelers and epidemiologists, and establishment of a sustainable cooperation network.

Modeling the impact of child vaccination (5-11 y) on overall COVID-19 related hospitalizations and mortality in a context of omicron variant predominance and different vaccination coverage paces in Brazil.

Background: Developing countries have experienced significant COVID-19 disease burden. With the emergence of new variants, particularly omicron, the disease burden in children has increased. When the first COVID-19 vaccine was approved for use in children aged 5-11 years of age, very few countries recommended vaccination due to limited risk-benefit evidence for vaccination of this population. In Brazil, ranking second in the global COVID-19 death toll, the childhood COVID-19 disease burden increased significantly in early 2022. This prompted a risk-benefit assessment of the introduction and scaling-up of COVID-19 vaccination of children. Methods: To estimate the potential impact of vaccinating children aged 5-11 years with mRNA-based COVID-19 vaccine in the context of omicron dominance, we developed a discrete-time SEIR-like model stratified in age groups, considering a three-month time horizon. We considered three scenarios: No vaccination, slow, and maximum vaccination paces. In each scenario, we estimated the potential reduction in total COVID-19 cases, hospitalizations, deaths, hospitalization costs, and potential years of life lost, considering the absence of vaccination as the base-case scenario. Findings: We estimated that vaccinating at a maximum pace could prevent, between mid-January and April 2022, about 26,000 COVID-19 hospitalizations, and 4200 deaths in all age groups; of which 5400 hospitalizations and 410 deaths in children aged 5-11 years. Continuing vaccination at a slow/current pace would prevent 1450 deaths and 9700 COVID-19 hospitalizations in all age groups in this same time period; of which 180 deaths and 2390 hospitalizations in children only. Interpretation: Maximum vaccination of children results in a significant reduction of COVID-19 hospitalizations and deaths and should be enforced in developing countries with significant disease incidence in children. Funding: This manuscript was funded by the Brazilian Council for Scientific and Technology Development (CNPq - Process # 402834/2020-8).

Transitions in frailty state 12 months after kidney transplantation: a prospective cohort study.

BACKGROUND: Frailty is associated with several unfavorable outcomes after kidney transplantation (KTx). However, limited information is available regarding the transitions in frailty state and its components after KTx. This study aimed to evaluate the transitions in physical frailty phenotype and its components over a period of 12 months after KTx. METHODS: In this prospective single-center cohort study, we measured physical frailty phenotype (PFP) and its components at the time of admission for KTx and 12 months after KTx. The evaluation includes five components: weakness (grip strength analysed by sex and body mass index quartiles), physical activity (kcals/week based on the Minnesota Leisure Time Physical Activity questionnaire), exhaustion (self-report using the Center for Epidemiological Studies Depression Scale), gait speed (time taken to walk 15 feet based on sex and height-specific cutoff), and unintentional weight loss (self-report of unintentional weight loss > 10 lbs in the last year). The exhaustion and physical activity components are validated in the Brazilian population. Each component is scored as 0 or 1 according to its presence or absence, and a PFP score of 3-5 defines frailty, 2 is intermediate, and 0-1 is rated as non-frail. We used the McNemar and Wilcoxon test to compare physical frailty phenotype and its components between the two periods. RESULTS: Among 87 patients included in the study, 16.1% were classified as frail, 20.7% as intermediately frail, and 63.2% as non-frail. Sixty-four patients were included in the analysis to evaluate transitions in frailty. At the time of admission for KTx, 15.6% of patients were defined as frail compared to 4.7% of patients at 12 months after KTx (p = 0.023). Among the physical frailty phenotype components, the proportion of patients who scored in the weight loss category 12 months after KTx was significantly lower than that at the time of KTx (6.3% vs 34.4%, p < 0.001). CONCLUSIONS: There was a 69.9% reduction in the prevalence of frail patients at the end of the 12-month follow-up after KTx. Among the components of frailty, weight loss showed a significant improvement.

The use of health geography modeling to understand early dispersion of COVID-19 in São Paulo, Brazil.

Public health policies to contain the spread of COVID-19 rely mainly on non-pharmacological measures. Those measures, especially social distancing, are a challenge for developing countries, such as Brazil. In São Paulo, the most populous state in Brazil (45 million inhabitants), most COVID-19 cases up to April 18th were reported in the Capital and metropolitan area. However, the inner municipalities, where 20 million people live, are also at risk. As governmental authorities discuss the loosening of measures for restricting population mobility, it is urgent to analyze the routes of dispersion of COVID-19 in São Paulo territory. We hypothesize that urban hierarchy is the main responsible for the disease spreading, and we identify the hotspots and the main routes of virus movement from the metropolis to the inner state. In this ecological study, we use geographic models of population mobility to check for patterns for the spread of SARS-CoV-2 infection. We identify two patterns based on surveillance data: one by contiguous diffusion from the capital metropolitan area, and the other hierarchical with long-distance spread through major highways that connects São Paulo city with cities of regional relevance. This knowledge can provide real-time responses to support public health strategies, optimizing the use of resources in order to minimize disease impact on population and economy.