ABSTRACT
Brazil has the second highest COVID-19 death rate while Rio de Janeiro is among the states with the highest rate in the country. Although effective vaccines have been developed, it is anticipated that the ongoing COVID-19 pandemic will transition into an endemic state. Under this scenario, it is worrisome that the underlying molecular mechanisms associated with the disease clinical evolution from mild to severe, as well as the mechanisms leading to long COVID are not yet fully understood. In this study, 1H Nuclear Magnetic Resonance spectroscopy and Liquid Chromatography-Mass spectrometry-based metabolomics were used to identify potential pathways and metabolites involved in COVID-19 pathophysiology and disease outcome. Between April and July 2020, 35 plasma samples from patients with confirmed severe COVID-19 from two reference centers in Rio de Janeiro, and 12 samples from non-infected control subjects, were collected and included in this study. Of the 35 samples from COVID-19 patients, 18 were from survivors and 17 from non-survivors. We observed that patients with severe COVID-19 had their plasma metabolome significantly changed if compared to control subjects. We observed lower levels of glycerophosphocholine and other choline-related metabolites, serine, glycine, and betaine, indicating a dysregulation in methyl donors and one-carbon metabolism. Importantly, non-survivors had higher levels of creatine/creatinine, 4-hydroxyproline, gluconic acid and N-acetylserine compared to survivors and controls, reflecting uncontrolled inflammation, liver and kidney dysfunction, and insulin resistance in these patients. Lipoprotein dynamics and amino acid metabolism were also altered in severe COVID-19 subjects. Several changes were greater in women, thus patient's sex should be considered in pandemic surveillance to achieve better disease stratification and improve outcomes. The incidence of severe outcome after hospital discharge is very high in Brazil, thus these metabolic alterations may be used to monitor patients' organs and tissues and to understand the pathophysiology of long-post COVID-19.
Subject(s)
Chronobiology Disorders , Kidney Diseases , Death , COVID-19 , InflammationABSTRACT
By the peak of COVID-19 restrictions on April 8, 2020, up to 1.5 billion students across 188 countries were by the suspension of physical attendance in schools. Schools were among the first services to reopen as vaccination campaigns advanced. With the emergence of new variants and infection waves, the question now is to find safe protocols for the continuation of school activities. We need to understand how reliable these protocols are under different levels of vaccination coverage, as many countries have a meager fraction of their population vaccinated, including Uganda where the coverage is about 8\%. We investigate the impact of face-to-face classes under different protocols and quantify the surplus number of infected individuals in a city. Using the infection transmission when schools were closed as a baseline, we assess the impact of physical school attendance in classrooms with poor air circulation. We find that (i) resuming school activities with people only wearing low-quality masks leads to a near fivefold city-wide increase in the number of cases even if all staff is vaccinated, (ii) resuming activities with students wearing good-quality masks and staff wearing N95s leads to about a threefold increase, (iii) combining high-quality masks and active monitoring, activities may be carried out safely even with low vaccination coverage. These results highlight the effectiveness of good mask-wearing. Compared to ICU costs, high-quality masks are inexpensive and can help curb the spreading. Classes can be carried out safely, provided the correct set of measures are implemented.
Subject(s)
COVID-19ABSTRACT
BackgroundMass vaccination campaigns started in Brazil on January/2021 with CoronaVac followed by ChAdOx1 nCov-19, and BNT162b2 mRNA vaccines. Target populations initially included vulnerable groups such as people older than 80 years, with comorbidities, of indigenous origin, and healthcare workers. Younger age groups were gradually included. MethodsA national cohort of 66.3 million records was compiled by linking registry-certified COVID-19 vaccination records from the Brazilian National Immunization Program with information on severe COVID-19 cases and deaths. Cases and deaths were aggregated by state and age group. Mixed-effects Poisson models were used to estimate the rate of severe cases and deaths among vaccinated and unvaccinated individuals, and the corresponding estimates of vaccine effectiveness by vaccine platform and age group. The study period is from mid-January to mid-July 2021. ResultsEstimates of vaccine effectiveness preventing deaths were highest at 97.9% (95% CrI: 93.5-99.8) among 20-39 years old with ChAdOx1 nCov-19, at 82.7% (95% CrI: 80.7-84.6) among 40-59 years old with CoronaVac, and at 89.9% (87.8--91.8) among 40-59 years old with partial immunization of BNT162b2. For all vaccines combined in the full regimen, the effectiveness preventing severe cases among individuals aged 80+ years old was 35.9% (95% CrI: 34.9-36.9) which is lower than that observed for individuals aged 60-79 years (61.0%, 95% CrI: 60.5-61.5). ConclusionDespite varying effectiveness estimates, Brazils population benefited from vaccination in preventing severe COVID-19 outcomes. Results, however, suggest significant vaccine-specific reductions in effectiveness by age, given by differences between age groups 60-79 years and over 80 years.
Subject(s)
COVID-19 , DeathABSTRACT
Background: The EPICOVID19-RS study conducted 10 population-based surveys in Rio Grande do Sul (Southern Brazil), starting early in the epidemic. The sensitivity of the rapid point-of-care test used in the first eight surveys has been shown to decrease over time after some phases of the study were concluded. The 9th survey used both the rapid test and an enzyme-linked immunosorbent assay (ELISA) test, which has a higher and stable sensitivity. Methods: We provide a theoretical justification for a correction procedure of the rapid test estimates, assess its performance in a simulated dataset and apply it to empirical data from the EPICOVID19-RS study. COVID-19 deaths from official statistics were used as an indicator of the temporal distribution of the epidemic, under the assumption that fatality is constant over time. Both the indicator and results from the 9th survey were used to calibrate the temporal decay function of the rapid test's sensitivity from a previous validation study, which was used to estimate the true sensitivity in each survey and adjust the rapid test estimates accordingly. Results: Simulations corroborated the procedure is valid. Corrected seroprevalence estimates were substantially larger than uncorrected estimates, which were substantially smaller than respective estimates from confirmed cases and therefore clearly underestimate the true infection prevalence. Conclusion: Correcting biased estimates requires a combination of data and modelling assumptions. This work illustrates the practical utility of analytical procedures, but also the critical need for good quality, populationally-representative data for tracking the progress of the epidemic and substantiate both projection models and policy making.
Subject(s)
COVID-19ABSTRACT
During the early months of the current COVID-19 pandemic, social-distancing measures effectively slowed disease transmission in many countries in Europe and Asia, but the same benefits have not been observed in some developing countries such as Brazil. In part, this is due to a failure to organise systematic testing campaigns at nationwide or even regional levels. To gain effective control of the pandemic, decision-makers in developing countries, particularly those with large populations, must overcome difficulties posed by an unequal distribution of wealth combined with low daily testing capacities. The economic infrastructure of the country, often concentrated in a few cities, forces workers to travel from commuter cities and rural areas, which induces strong nonlinear effects on disease transmission. In the present study, we develop a smart testing strategy to identify geographic regions where COVID-19 testing could most effectively be deployed to limit further disease transmission. The strategy uses readily available anonymised mobility and demographic data integrated with intensive care unit (ICU) occupancy data and city-specific social-distancing measures. Taking into account the heterogeneity of ICU bed occupancy in differing regions and the stages of disease evolution, we use a data-driven study of the Brazilian state of Sao Paulo as an example to show that smart testing strategies can rapidly limit transmission while reducing the need for social-distancing measures, thus returning life to a so-called new normal, even when testing capacity is limited.
Subject(s)
COVID-19 , Heart FailureABSTRACT
Since the beginning of the pandemic of COVID-19, there has been a widespread assumption that most infected persons are asymptomatic. A frequently-cited early study from China suggested that 86% of all infections were undocumented, which was used as indirect evidence that patients were asymptomatic. Using data from the most recent wave of the EPICOVID19 study, a nationwide household-based survey including 133 cities from all states of Brazil, we estimated the proportion of people with and without antibodies for SARS-CoV-2 who were asymptomatic, which symptoms were most frequently reported, the number of symptoms reported and the association between symptomatology and socio-demographic characteristics. We were able to test 33,205 subjects using a rapid antibody test that was previously validated. Information on symptoms was collected before participants received the test result. Out of 849 (2.7%) participants who tested positive for SARS-CoV-2 antibodies, only 12.1% (95%CI 10.1-14.5) reported no symptoms since the start of the pandemic, compared to 42.2% (95%CI 41.7-42.8) among those who tested negative. The largest difference between the two groups was observed for changes in smell or taste (56.5% versus 9.1%, a 6.2-fold difference). Symptoms change in smell or taste, fever and myalgia were most likely to predict positive test results as suggested by recursive partitioning tree analysis. Among individuals without any of these three symptoms (74.2% of the sample), only 0.8% tested positive, compared to 18.3% of those with both fever and changes in smell or taste. Most subjects with antibodies against SARS-CoV-2 in Brazil are symptomatic, even though most present only mild symptoms.
Subject(s)
COVID-19 , Fever , MyalgiaABSTRACT
Combinations of intense non-pharmaceutical interventions ('lockdowns') were introduced in countries worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement lockdown exit strategies that allow restrictions to be relaxed while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, will allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. The roadmap requires a global collaborative effort from the scientific community and policy-makers, and is made up of three parts: i) improve estimation of key epidemiological parameters; ii) understand sources of heterogeneity in populations; iii) focus on requirements for data collection, particularly in Low-to-Middle-Income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.