Rapid viral metagenomics using SMART-9N amplification and nanopore sequencing [version 2, peer review: 2 approved]

Emerging and re-emerging viruses are a global health concern. Genome sequencing as an approach for monitoring circulating viruses is currently hampered by complex and expensive methods. Untargeted, metagenomic nanopore sequencing can provide genomic information to identify pathogens, prepare for or even prevent outbreaks. SMART (Switching Mechanism at the 5′ end of RNA Template) is a popular approach for RNA-Seq but most current methods rely on oligo-dT priming to target polyadenylated mRNA molecules. We have developed two random primed SMART-Seq approaches, a sequencing agnostic approach ‘SMART-9N' and a version compatible rapid adapters available from Oxford Nanopore Technologies ‘Rapid SMART-9N'. The methods were developed using viral isolates, clinical samples, and compared to a gold-standard amplicon-based method. From a Zika virus isolate the SMART-9N approach recovered 10kb of the 10.8kb RNA genome in a single nanopore read. We also obtained full genome coverage at a high depth coverage using the Rapid SMART-9N, which takes only 10 minutes and costs up to 45% less than other methods. We found the limits of detection of these methods to be 6 focus forming units (FFU)/mL with 99.02% and 87.58% genome coverage for SMART-9N and Rapid SMART-9N respectively. Yellow fever virus plasma samples and SARS-CoV-2 nasopharyngeal samples previously confirmed by RT-qPCR with a broad range of Ct-values were selected for validation. Both methods produced greater genome coverage when compared to the multiplex PCR approach and we obtained the longest single read of this study (18.5 kb) with a SARS-CoV-2 clinical sample, 60% of the virus genome using the Rapid SMART-9N method. This work demonstrates that SMART-9N and Rapid SMART-9N are sensitive, low input, and long-read compatible alternatives for RNA virus detection and genome sequencing and Rapid SMART-9N improves the cost, time, and complexity of laboratory work.

Rapid viral metagenomics using SMART-9N amplification and nanopore sequencing.

Emerging and re-emerging viruses are a global health concern. Genome sequencing as an approach for monitoring circulating viruses is currently hampered by complex and expensive methods. Untargeted, metagenomic nanopore sequencing can provide genomic information to identify pathogens, prepare for or even prevent outbreaks. SMART (Switching Mechanism at the 5' end of RNA Template) is a popular approach for RNA-Seq but most current methods rely on oligo-dT priming to target polyadenylated mRNA molecules. We have developed two random primed SMART-Seq approaches, a sequencing agnostic approach 'SMART-9N' and a version compatible rapid adapters  available from Oxford Nanopore Technologies 'Rapid SMART-9N'. The methods were developed using viral isolates, clinical samples, and compared to a gold-standard amplicon-based method. From a Zika virus isolate the SMART-9N approach recovered 10kb of the 10.8kb RNA genome in a single nanopore read. We also obtained full genome coverage at a high depth coverage using the Rapid SMART-9N, which takes only 10 minutes and costs up to 45% less than other methods. We found the limits of detection of these methods to be 6 focus forming units (FFU)/mL with 99.02% and 87.58% genome coverage for SMART-9N and Rapid SMART-9N respectively. Yellow fever virus plasma samples and SARS-CoV-2 nasopharyngeal samples previously confirmed by RT-qPCR with a broad range of Ct-values were selected for validation. Both methods produced greater genome coverage when compared to the multiplex PCR approach and we obtained the longest single read of this study (18.5 kb) with a SARS-CoV-2 clinical sample, 60% of the virus genome using the Rapid SMART-9N method. This work demonstrates that SMART-9N and Rapid SMART-9N are sensitive, low input, and long-read compatible alternatives for RNA virus detection and genome sequencing and Rapid SMART-9N improves the cost, time, and complexity of laboratory work.

Tracking the emergence of disparities in the subnational spread of COVID-19 in Brazil using an online application for real-time data visualisation: A longitudinal analysis.

BACKGROUND: Brazil is one of the countries worst affected by the COVID-19 pandemic with over 20 million cases and 557,000 deaths reported by August 2021. Comparison of real-time local COVID-19 data between areas is essential for understanding transmission, measuring the effects of interventions, and predicting the course of the epidemic, but are often challenging due to different population sizes and structures. METHODS: We describe the development of a new app for the real-time visualisation of COVID-19 data in Brazil at the municipality level. In the CLIC-Brazil app, daily updates of case and death data are downloaded, age standardised and used to estimate the effective reproduction number (Rt ). We show how such platforms can perform real-time regression analyses to identify factors associated with the rate of initial spread and early reproduction number. We also use survival methods to predict the likelihood of occurrence of a new peak of COVID-19 incidence. FINDINGS: After an initial introduction in São Paulo and Rio de Janeiro states in early March 2020, the epidemic spread to northern states and then to highly populated coastal regions and the Central-West. Municipalities with higher metrics of social development experienced earlier arrival of COVID-19 (decrease of 11·1 days [95% CI:8.9,13.2] in the time to arrival for each 10% increase in the social development index). Differences in the initial epidemic intensity (mean Rt ) were largely driven by geographic location and the date of local onset. INTERPRETATION: This study demonstrates that platforms that monitor, standardise and analyse the epidemiological data at a local level can give useful real-time insights into outbreak dynamics that can be used to better adapt responses to the current and future pandemics. FUNDING: This project was supported by a Medical Research Council UK (MRC-UK) -São Paulo Research Foundation (FAPESP) CADDE partnership award (MR/S0195/1 and FAPESP 18/14389-0).

Global disparities in SARS-CoV-2 genomic surveillance.

Genomic sequencing is essential to track the evolution and spread of SARS-CoV-2, optimize molecular tests, treatments, vaccines, and guide public health responses. To investigate the global SARS-CoV-2 genomic surveillance, we used sequences shared via GISAID to estimate the impact of sequencing intensity and turnaround times on variant detection in 189 countries. In the first two years of the pandemic, 78% of high-income countries sequenced >0.5% of their COVID-19 cases, while 42% of low- and middle-income countries reached that mark. Around 25% of the genomes from high income countries were submitted within 21 days, a pattern observed in 5% of the genomes from low- and middle-income countries. We found that sequencing around 0.5% of the cases, with a turnaround time <21 days, could provide a benchmark for SARS-CoV-2 genomic surveillance. Socioeconomic inequalities undermine the global pandemic preparedness, and efforts must be made to support low- and middle-income countries improve their local sequencing capacity.

Characterization of Severe Acute Respiratory Syndrome Coronavirus 2 Omicron Variant Shedding and Predictors of Viral Culture Positivity on Vaccinated Healthcare Workers With Mild Coronavirus Disease 2019.

In this prospective cohort of 30 vaccinated healthcare workers with mild Omicron variant infection, we evaluated viral culture, rapid antigen test (RAT), and real-time reverse-transcription polymerase chain reaction (RT-PCR) of respiratory samples at days 5, 7, 10, and 14. Viral culture was positive in 46% (11/24) and 20% (6/30) of samples at days 5 and 7, respectively. RAT and RT-PCR (Ct ≤35) showed 100% negative predictive value (NPV), with positive predictive values (PPVs) of 32% and 17%, respectively, for predicting viral culture positivity. A lower RT-PCR threshold (Ct ≤24) improved culture prediction (PPV = 39%; NPV = 100%). Vaccinated persons with mild Omicron infection are potentially transmissible up to day 7. RAT and RT-PCR might be useful tools for shortening the isolation period.

SARS-CoV-2 antibody dynamics in blood donors and COVID-19 epidemiology in eight Brazilian state capitals: A serial cross-sectional study.

Background: The COVID-19 situation in Brazil is complex due to large differences in the shape and size of regional epidemics. Understanding these patterns is crucial to understand future outbreaks of SARS-CoV-2 or other respiratory pathogens in the country. Methods: We tested 97,950 blood donation samples for IgG antibodies from March 2020 to March 2021 in 8 of Brazil's most populous cities. Residential postal codes were used to obtain representative samples. Weekly age- and sex-specific seroprevalence were estimated by correcting the crude seroprevalence by test sensitivity, specificity, and antibody waning. Results: The inferred attack rate of SARS-CoV-2 in December 2020, before the Gamma variant of concern (VOC) was dominant, ranged from 19.3% (95% credible interval [CrI] 17.5-21.2%) in Curitiba to 75.0% (95% CrI 70.8-80.3%) in Manaus. Seroprevalence was consistently smaller in women and donors older than 55 years. The age-specific infection fatality rate (IFR) differed between cities and consistently increased with age. The infection hospitalisation rate increased significantly during the Gamma-dominated second wave in Manaus, suggesting increased morbidity of the Gamma VOC compared to previous variants circulating in Manaus. The higher disease penetrance associated with the health system's collapse increased the overall IFR by a minimum factor of 2.91 (95% CrI 2.43-3.53). Conclusions: These results highlight the utility of blood donor serosurveillance to track epidemic maturity and demonstrate demographic and spatial heterogeneity in SARS-CoV-2 spread. Funding: This work was supported by Itaú Unibanco 'Todos pela Saude' program; FAPESP (grants 18/14389-0, 2019/21585-0); Wellcome Trust and Royal Society Sir Henry Dale Fellowship 204311/Z/16/Z; the Gates Foundation (INV- 034540 and INV-034652); REDS-IV-P (grant HHSN268201100007I); the UK Medical Research Council (MR/S0195/1, MR/V038109/1); CAPES; CNPq (304714/2018-6); Fundação Faculdade de Medicina; Programa Inova Fiocruz-CE/Funcap - Edital 01/2020 Number: FIO-0167-00065.01.00/20 SPU N°06531047/2020; JBS - Fazer o bem faz bem.

Correlating drug prescriptions with prognosis in severe COVID-19: first step towards resource management.

BACKGROUND: Optimal COVID-19 management is still undefined. In this complicated scenario, the construction of a computational model capable of extracting information from electronic medical records, correlating signs, symptoms and medical prescriptions, could improve patient management/prognosis. METHODS: The aim of this study is to investigate the correlation between drug prescriptions and outcome in patients with COVID-19. We extracted data from 3674 medical records of hospitalized patients: drug prescriptions, outcome, and demographics. The outcome evaluated was hospital outcome. We applied correlation analysis using a Logistic Regression algorithm for machine learning with Lasso and Matthews correlation coefficient. RESULTS: We found correlations between drugs and patient outcomes (death/discharged alive). Anticoagulants, used very frequently during all phases of the disease, were associated with good prognosis only after the first week of symptoms. Antibiotics very frequently prescribed, especially early, were not correlated with outcome, suggesting that bacterial infections may not be important in determining prognosis. There were no differences between age groups. CONCLUSIONS: In conclusion, we achieved an important result in the area of Artificial Intelligence, as we were able to establish a correlation between concrete variables in a real and extremely complex environment of clinical data from COVID-19. Our results are an initial and promising contribution in decision-making and real-time environments to support resource management and forecasting prognosis of patients with COVID-19.

Understanding the Potential Impact of Different Drug Properties on Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Transmission and Disease Burden: A Modelling Analysis.

BACKGROUND: The public health impact of the coronavirus disease 2019 (COVID-19) pandemic has motivated a rapid search for potential therapeutics, with some key successes. However, the potential impact of different treatments, and consequently research and procurement priorities, have not been clear. METHODS: Using a mathematical model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission, COVID-19 disease and clinical care, we explore the public-health impact of different potential therapeutics, under a range of scenarios varying healthcare capacity, epidemic trajectories; and drug efficacy in the absence of supportive care. RESULTS: The impact of drugs like dexamethasone (delivered to the most critically-ill in hospital and whose therapeutic benefit is expected to depend on the availability of supportive care such as oxygen and mechanical ventilation) is likely to be limited in settings where healthcare capacity is lowest or where uncontrolled epidemics result in hospitals being overwhelmed. As such, it may avert 22% of deaths in high-income countries but only 8% in low-income countries (assuming R = 1.35). Therapeutics for different patient populations (those not in hospital, early in the course of infection) and types of benefit (reducing disease severity or infectiousness, preventing hospitalization) could have much greater benefits, particularly in resource-poor settings facing large epidemics. CONCLUSIONS: Advances in the treatment of COVID-19 to date have been focused on hospitalized-patients and predicated on an assumption of adequate access to supportive care. Therapeutics delivered earlier in the course of infection that reduce the need for healthcare or reduce infectiousness could have significant impact, and research into their efficacy and means of delivery should be a priority.

CARACTERIZAÇÃO DA INFECTIVIDADE DA VARIANTE ÔMICRON E PREDITORES DE POSITIVIDADE DE CULTURA VIRAL EM PROFISSIONAIS DE SAÚDE COM COVID-19 LEVE

Introdução A variante Ômicron do vírus SARS-CoV-2 (B.1.1.529) foi designada uma variante preocupante (VOC) devido à alta transmissibilidade e capacidade de escapar da imunidade natural e induzida por vacina. Objetivo Caracterizar a duração da infectividade da variante Ômicron em indivíduos vacinados com sintomas leves de COVID-19. Método Estudo transversal com 30 indivíduos vacinados com COVID-19 para avaliar a duração da infectividade da Ômicron comparando o isolamento viral com o teste rápido de antígeno (RAT) e os valores de Ct da reação em cadeia da polimerase em tempo real (RT-PCR) de amostras respiratórias nos dias 5, 7, 10 e 14 a partir do início dos sintomas. Resultados O crescimento viral foi observado em 46% (11/24) das amostras dos indivíduos vacinados no dia 5 dos sintomas e 20% (6/30) no dia 7, nenhuma amostra teve isolamento viral no dia 10. A carga de RNA viral permaneceu detectável em 97% (29/30) e 57% (17/30) dos participantes nos dias 10 e 14, respectivamente. Entre as amostras com isolamento viral, todas (n = 17) foram RAT e RT-PCR positivas. Por outro lado, amostras sem isolamento viral (n = 97) foram RAT e RT-PCR positivas em 36 (37%) e 83 (86%), respectivamente. RAT e RT-PCR evidenciaram sensibilidade global e valores preditivos negativos de 100%, porém, RAT apresentou 63% de especificidade global e 32% de valor preditivo positivo (VPP), enquanto RT-PCR evidenciou menor especificidade (14%) e VPP (17%) para predizer a infectividade. Conclusão Indivíduos vacinados imunocompetentes com infecção por Ômicron ainda podem transmitir o vírus no 7° dia de sintomas, portanto, é altamente improvável que estejam transmitindo o vírus infeccioso no dia 10. Testes rápidos de antígeno podem ser usados para estimar a duração da infectividade dos casos de Ômicron. Ag. Financiadora Instituto todos pela saúde do Banco Itaú.

PREDITORES DE PROTEÇÃO CONTRA INFECÇÃO POR SARS-COV-2 APÓS DUAS DOSES DE CORONAVAC EM UMA COORTE DE PROFISSIONAIS DE SAÚDE

Introdução A elucidação dos preditores de proteção contra infecção pelo SARS-CoV-2 após a vacinação contra o mesmo pode auxiliar no controle da pandemia. Objetivo Identificar fatores de proteção contra infecção por SARS-CoV-2 após recebimento de duas doses de CoronaVac. Método Trata-se de uma coorte prospectiva de profissionais de saúde (PS) do HC-FMUSP vacinados com 2 doses da CoronaVac. O desfecho avaliado foi infecção pelo SARS-CoV-2 (confirmada por RT-PCR) desde 10 semanas após a segunda dose da vacina até pararem de trabalhar no HC-FMUSP ou até a data 08/03/2022. A infecção pelo SARS-CoV-2 foi verificada através dos registros do Centro de Atendimento ao Colaborador (CEAC) e do Núcleo de Vigilância Epidemiológica (NUVE) do HCFMUSP e através de entrevistas aos participantes do estudo. Os PS foram submetidos a sorologia para o SARS-CoV-2 para detecção de IgG anti-S (Liaison®/DiaSorin). Fatores de proteção contra infecção pelo SARS-CoV-2 foram avaliados com modelos de regressão de Cox. Os participantes assinaram um TCLE antes de ingressarem no estudo e o projeto foi aprovado no CEP do HC-FMUSP. Resultados Entre a 2ª e a 3ª dose da vacina, 3.979 PS foram avaliados. A idade mediana foi 44 anos e 79% era do sexo feminino. Casos de COVID-19 antes da 1ª dose da vacina foram detectados em 18% dos participantes. Sorologia reagente (título ≥ 33,8) foi detectada em 90% dos participantes em um teste realizado 10 semanas após a 2ª dose da vacina e houve 247 (6%) casos de COVID-19 entre a coleta desta sorologia e o recebimento da 3ª dose da vacina. Fatores de proteção contra infecção pelo SARS-CoV-2 neste período foram: diagnóstico de COVID-19 antes da 1ª dose da vacina (adjHR = 0,35), sorologia reagente coletada 10 semanas após 2ª dose da vacina (adjHR = 0,50) e idade entre 50-70 anos (adjHR = 0,52). Após a 3ª dose da vacina, 1305 PS foram avaliados. Sorologia reagente foi detectada em 99,8% dos participantes em um teste realizado 8 semanas após a 3ª dose da vacina e houve 159 (12%) casos de COVID-19 entre a coleta desta sorologia e o término do seguimento. Fatores de proteção contra infecção pelo SARS-CoV-2 no período foram: diagnóstico de COVID-19 antes da 3ª dose da vacina (adjHR = 0,57) e altos títulos da sorologia coletada 8 semanas após a terceira dose da vacina (adjHR = 0,99). Conclusão Diagnóstico prévio de COVID-19 e altos títulos de IgG contra o SARS-CoV-2 8-10 semanas após a vacinação são fatores protetores de infecção pelo SARS-CoV-2 em PS vacinados com CoronaVac. Ag. Financiadora: Instituto todos pela saúde. Nr. Processo: C1864.

Predicting SARS-CoV-2 Variant Spread in a Completely Seropositive Population Using Semi-Quantitative Antibody Measurements in Blood Donors.

SARS-CoV-2 serologic surveys estimate the proportion of the population with antibodies against historical variants, which nears 100% in many settings. New approaches are required to fully exploit serosurvey data. Using a SARS-CoV-2 anti-Spike (S) protein chemiluminescent microparticle assay, we attained a semi-quantitative measurement of population IgG titers in serial cross-sectional monthly samples of blood donations across seven Brazilian state capitals (March 2021-November 2021). Using an ecological analysis, we assessed the contributions of prior attack rate and vaccination to antibody titer. We compared anti-S titer across the seven cities during the growth phase of the Delta variant and used this to predict the resulting age-standardized incidence of severe COVID-19 cases. We tested ~780 samples per month, per location. Seroprevalence rose to >95% across all seven capitals by November 2021. Driven by vaccination, mean antibody titer increased 16-fold over the study, with the greatest increases occurring in cities with the highest prior attack rates. Mean anti-S IgG was strongly correlated (adjusted R2 = 0.89) with the number of severe cases caused by Delta. Semi-quantitative anti-S antibody titers are informative about prior exposure and vaccination coverage and may also indicate the potential impact of future SARS-CoV-2 variants.

Predicting the outcome for COVID-19 patients by applying time series classification to electronic health records.

BACKGROUND: COVID-19 caused more than 622 thousand deaths in Brazil. The infection can be asymptomatic and cause mild symptoms, but it also can evolve into a severe disease and lead to death. It is difficult to predict which patients will develop severe disease. There are, in the literature, machine learning models capable of assisting diagnose and predicting outcomes for several diseases, but usually these models require laboratory tests and/or imaging. METHODS: We conducted a observational cohort study that evaluated vital signs and measurements from patients who were admitted to Hospital das Clínicas (São Paulo, Brazil) between March 2020 and October 2021 due to COVID-19. The data was then represented as univariate and multivariate time series, that were used to train and test machine learning models capable of predicting a patient's outcome. RESULTS: Time series-based machine learning models are capable of predicting a COVID-19 patient's outcome with up to 96% general accuracy and 81% accuracy considering only the first hospitalization day. The models can reach up to 99% sensitivity (discharge prediction) and up to 91% specificity (death prediction). CONCLUSIONS: Results indicate that time series-based machine learning models combined with easily obtainable data can predict COVID-19 outcomes and support clinical decisions. With further research, these models can potentially help doctors diagnose other diseases.

Clearance of Persistent SARS-CoV-2 RNA Detection in a NFκB-Deficient Patient in Association with the Ingestion of Human Breast Milk: A Case Report.

Currently, there are no evidence-based treatment options for long COVID-19, and it is known that SARS-CoV-2 can persist in part of the infected patients, especially those with immunosuppression. Since there is a robust secretion of SARS-CoV-2-specific highly-neutralizing IgA antibodies in breast milk, and because this immunoglobulin plays an essential role against respiratory virus infection in mucosa cells, being, in addition, more potent in neutralizing SARS-CoV-2 than IgG, here we report the clinical course of an NFκB-deficient patient chronically infected with the SARS-CoV-2 Gamma variant, who, after a non-full effective treatment with plasma infusion, received breast milk from a vaccinated mother by oral route as treatment for COVID-19. After such treatment, the symptoms improved, and the patient was systematically tested negative for SARS-CoV-2. Thus, we hypothesize that IgA and IgG secreted antibodies present in breast milk could be useful to treat persistent SARS-CoV-2 infection in immunodeficient patients.

Identification and characterization of the anti-SARS-CoV-2 activity of cationic amphiphilic steroidal compounds.

The ongoing COVID-19 pandemic caused a significant loss of human lives and a worldwide decline in quality of life. Treatment of COVID-19 patients is challenging, and specific treatments to reduce COVID-19 aggravation and mortality are still necessary. Here, we describe the discovery of a novel class of epiandrosterone steroidal compounds with cationic amphiphilic properties that present antiviral activity against SARS-CoV-2 in the low micromolar range. Compounds were identified in screening campaigns using a cytopathic effect-based assay in Vero CCL81 cells, followed by hit compound validation and characterization. Compounds LNB167 and LNB169 were selected due to their ability to reduce the levels of infectious viral progeny and viral RNA levels in Vero CCL81, HEK293, and HuH7.5 cell lines. Mechanistic studies in Vero CCL81 cells indicated that LNB167 and LNB169 inhibited the initial phase of viral replication through mechanisms involving modulation of membrane lipids and cholesterol in host cells. Selection of viral variants resistant to steroidal compound treatment revealed single mutations on transmembrane, lipid membrane-interacting Spike and Envelope proteins. Finally, in vivo testing using the hACE2 transgenic mouse model indicated that SARS-CoV-2 infection could not be ameliorated by LNB167 treatment. We conclude that anti-SARS-CoV-2 activities of steroidal compounds LNB167 and LNB169 are likely host-targeted, consistent with the properties of cationic amphiphilic compounds that modulate host cell lipid biology. Although effective in vitro, protective effects were cell-type specific and did not translate to protection in vivo, indicating that subversion of lipid membrane physiology is an important, yet complex mechanism involved in SARS-CoV-2 replication and pathogenesis.

Spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Gamma variant of concern has spread rapidly across Brazil since late 2020, causing substantial infection and death waves. Here we used individual-level patient records after hospitalization with suspected or confirmed coronavirus disease 2019 (COVID-19) between 20 January 2020 and 26 July 2021 to document temporary, sweeping shocks in hospital fatality rates that followed the spread of Gamma across 14 state capitals, during which typically more than half of hospitalized patients aged 70 years and older died. We show that such extensive shocks in COVID-19 in-hospital fatality rates also existed before the detection of Gamma. Using a Bayesian fatality rate model, we found that the geographic and temporal fluctuations in Brazil's COVID-19 in-hospital fatality rates were primarily associated with geographic inequities and shortages in healthcare capacity. We estimate that approximately half of the COVID-19 deaths in hospitals in the 14 cities could have been avoided without pre-pandemic geographic inequities and without pandemic healthcare pressure. Our results suggest that investments in healthcare resources, healthcare optimization and pandemic preparedness are critical to minimize population-wide mortality and morbidity caused by highly transmissible and deadly pathogens such as SARS-CoV-2, especially in low- and middle-income countries.

MALDI-TOF mass spectrometry of saliva samples as a prognostic tool for COVID-19.

BACKGROUND: The SARS-CoV-2 infections are still imposing a great public health challenge despite the recent developments in vaccines and therapy. Searching for diagnostic and prognostic methods that are fast, low-cost and accurate are essential for disease control and patient recovery. The MALDI-TOF mass spectrometry technique is rapid, low cost and accurate when compared to other MS methods, thus its use is already reported in the literature for various applications, including microorganism identification, diagnosis and prognosis of diseases. METHODS: Here we developed a prognostic method for COVID-19 using the proteomic profile of saliva samples submitted to MALDI-TOF and machine learning algorithms to train models for COVID-19 severity assessment. RESULTS: We achieved an accuracy of 88.5%, specificity of 85% and sensitivity of 91.5% for classification between mild/moderate and severe conditions. When we tested the model performance in an independent dataset, we achieved an accuracy, sensitivity and specificity of 67.18, 52.17 and 75.60% respectively. CONCLUSION: Saliva is already reported to have high inter-sample variation; however, our results demonstrates that this approach has the potential to be a prognostic method for COVID-19. Additionally, the technology used is already available in several clinics, facilitating the implementation of the method. Further investigation using a larger dataset is necessary to consolidate the technique.

Tracking the emergence of disparities in the subnational spread of COVID-19 in Brazil using an online application for real-time data visualisation: A longitudinal analysis

Background Brazil is one of the countries worst affected by the COVID-19 pandemic with over 20 million cases and 557,000 deaths reported by August 2021. Comparison of real-time local COVID-19 data between areas is essential for understanding transmission, measuring the effects of interventions, and predicting the course of the epidemic, but are often challenging due to different population sizes and structures. Methods We describe the development of a new app for the real-time visualisation of COVID-19 data in Brazil at the municipality level. In the CLIC-Brazil app, daily updates of case and death data are downloaded, age standardised and used to estimate the effective reproduction number (Rt). We show how such platforms can perform real-time regression analyses to identify factors associated with the rate of initial spread and early reproduction number. We also use survival methods to predict the likelihood of occurrence of a new peak of COVID-19 incidence. Findings After an initial introduction in São Paulo and Rio de Janeiro states in early March 2020, the epidemic spread to northern states and then to highly populated coastal regions and the Central-West. Municipalities with higher metrics of social development experienced earlier arrival of COVID-19 (decrease of 11·1 days [95% CI:8.9,13.2] in the time to arrival for each 10% increase in the social development index). Differences in the initial epidemic intensity (mean Rt) were largely driven by geographic location and the date of local onset. Interpretation This study demonstrates that platforms that monitor, standardise and analyse the epidemiological data at a local level can give useful real-time insights into outbreak dynamics that can be used to better adapt responses to the current and future pandemics. Funding This project was supported by a Medical Research Council UK (MRC-UK) -São Paulo Research Foundation (FAPESP) CADDE partnership award (MR/S0195/1 and FAPESP 18/14389-0)

Immunogenicity and safety of two doses of the CoronaVac SARS-CoV-2 vaccine in SARS-CoV-2 seropositive and seronegative patients with autoimmune rheumatic diseases in Brazil: a subgroup analysis of a phase 4 prospective study.

BACKGROUND: We aimed to examine the immunogenicity pattern induced by the inactivated SARS-CoV-2 vaccine CoronaVac (Sinovac Life Sciences, Beijing, China) in SARS-CoV-2 seropositive patients with autoimmune rheumatic diseases compared with seropositive controls, seronegative patients with autoimmune rheumatic diseases, and seronegative controls. METHODS: CoronavRheum is an ongoing, prospective, controlled, phase 4 study, in which patients aged 18 years or older with autoimmune rheumatic diseases, and healthy controls were recruited from a single site (Rheumatology Division of Hospital das Clínicas, Faculdade de Medicina da Universidade de São Paulo) in São Paulo, Brazil Participants were vaccinated with two doses of CoronaVac (intramuscular injection, 3 µg in 0·5 mL of ß-propiolactone inactivated SARS-CoV-2) on day 0 and on day 28. Blood samples were taken pre-vaccination on day 0, day 28, and also on day 69. For this subgroup analysis, participants were defined as being SARS-CoV-2 seropositive or seronegative prevaccination via anti-SARS-CoV-2 spike (S)1 or S2 IgG (cutoff of 15·0 arbitrary units [AU] per mL) or neutralising antibody titres (cutoff of ≥30%) and were matched for age and sex, via convenience sampling, in a 1:3:1:1 ratio (seropositive patients to seronegative patients to seropositive controls to seronegative controls). The primary outcomes were rates of anti-SARS-CoV-2 S1 and S2 IgG seropositivity and SARS-CoV-2 neutralising antibody positivity at day 28 and day 69 and immunogenicity dynamics assessed by geometric mean titres (GMTs) of IgG and median neutralising activity in seropositive patients with autoimmune rheumatic diseases compared with seronegative patients and seropositive and seronegative controls. We assessed safety in all participants randomly selected for this subgroup analysis. This study is registered with ClinicalTrials.gov, NCT04754698, and is ongoing for long-term immunogenicity evaluation. FINDINGS: Between Feb 4 and Feb 8, 2021, 1418 patients and 542 controls were recruited, of whom 1685 received two vaccinations (1193 patients and 492 controls). After random sampling, our immunogenicity analysis population comprised 942 participants, of whom 157 were SARS-CoV-2 seropositive patients with autoimmune rheumatic diseases, 157 were seropositive controls, 471 were seronegative patients, and 157 were seronegative controls; the median age was 48 years (IQR 38-56) and 594 (63%) were female and 348 (37%) were male. For seropositive patients and controls, an increase in anti-SARS-CoV-2 S1 and S2 IgG titres (seropositive patients GMT 52·3 [95% CI 42·9-63·9] at day 0 vs 128·9 [105·6-157·4] at day 28; seropositive controls 53·3 [45·4-62·5] at day 0 vs 202·0 [174·8-233·4] at day 28) and neutralising antibody activity (seropositive patients 59% [IQR 39-83] at day 0 vs 82% [54-96] at day 28; seropositive controls 58% [41-79] at day 0 vs 92% [79-96] at day 28), was observed from day 0 to day 28, without further increases from day 28 to day 69 (at day 69 seropositive patients' GMT was 137·1 [116·2-161·9] and neutralising antibody activity was 79% [57-94]); and seropositive controls' GMT was 188·6 [167·4-212·6] and neutralising antibody activity was 92% [75-96]). By contrast, for seronegative patients and controls, the second dose was required for maximum response at day 69, which was lower in seronegative patients than in seronegative controls. GMTs in seronegative patients were 2·3 (95% CI 2·2-2·3) at day 0, 5·7 (5·1-6·4) at day 28, and 29·6 (26·4-33·3) at day 69, and in seronegative controls were 2·3 (2·1-2·5) at day 0, 10·6 (8·7-13·1) at day 28, and 71·7 (63·5-81·0) at day 69; neutralising antibody activity in seronegative patients was 15% (IQR 15-15) on day 0, 15% (15-15) at day 28, and 39% (15-65) at day 69, and in seronegative controls was 15% (15-15) at day 0, 24% (15-37) at day 28, and 61% (37-79) at day 69. Neither seronegative patients nor seronegative controls reached the GMT or antibody activity levels of seropositive patients at day 69. INTERPRETATION: By contrast with seronegative patients with autoimmune rheumatic diseases, seropositive patients have a robust response after a single dose of CoronaVac. Our findings raise the possibility that the reduced immunogenicity observed in seronegative patients might not be the optimum response potential to SARS-CoV-2 vaccination, and therefore emphasise the importance of at least a single booster vaccination in these patients. FUNDING: Fundação de Amparo à Pesquisa do Estado de São Paulo, Conselho Nacional de Desenvolvimento Científico e Tecnológico, and B3-Bolsa de Valores do Brasil. TRANSLATION: For the Portuguese translation of the abstract see Supplementary Materials section.

Epidemiology of COVID-19 after Emergence of SARS-CoV-2 Gamma Variant, Brazilian Amazon, 2020-2021.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Gamma variant has been hypothesized to cause more severe illness than previous variants, especially in children. Successive SARS-CoV-2 IgG serosurveys in the Brazilian Amazon showed that age-specific attack rates and proportions of symptomatic SARS-CoV-2 infections were similar before and after Gamma variant emergence.