The spread of infectious diseases is shaped by spatial and temporal aspects, such as host population structure or changes in the transmission rate or number of infected individuals over time. These spatiotemporal dynamics are imprinted in the genomes of pathogens and can be recovered from those genomes using phylodynamics methods. However, phylodynamic methods typically quantify either the temporal or spatial transmission dynamics, which leads to unclear biases, as one can potentially not be inferred without the other. Here, we address this challenge by introducing a structured coalescent skyline approach, MASCOT-Skyline, that allows us to jointly infer spatial and temporal transmission dynamics of infectious diseases using Markov chain Monte Carlo inference. To do so, we model the effective population size dynamics in different locations using a non-parametric function, allowing us to approximate a range of population size dynamics. We show, using a range of different viral outbreak datasets, potential issues with phylogeographic methods. We then use these viral datasets to motivate simulations of outbreaks that illuminate the nature of biases present in the different phylogeographic methods. We show that spatial and temporal dynamics should be modeled jointly, even if one seeks to recover just one of the two. Further, we showcase conditions under which we can expect phylogeographic analyses to be biased, particularly different subsampling approaches, as well as provide recommendations on when we can expect them to perform well. We implemented MASCOT-Skyline as part of the open-source software package MASCOT for the Bayesian phylodynamics platform BEAST2.

Target trial emulation applies design principles from randomised controlled trials to the analysis of observational data for causal inference and is increasingly used within human epidemiology. Using anonymised veterinary clinical data from the VetCompass Programme, this study applied the target trial emulation framework to determine whether surgical (compared to non-surgical) management for cranial cruciate ligament (CCL) rupture in dogs causes improved short- and long-term lameness and analgesia outcomes.The emulated target trial included dogs diagnosed with CCL rupture between January 1, 2019 and December 31, 2019 within the VetCompass database. Inclusion in the emulated trial required dogs aged ≥ 1.5 and < 12 years, first diagnosed with unilateral CCL rupture during 2019 and with no prior history of contralateral ligament rupture or stifle surgery. Dogs were retrospectively observed to have surgical or non-surgical management. Informed from a directed acyclic graph derived from expert opinion, data on the following variables were collected: age, breed, bodyweight, neuter status, insurance status, non-orthopaedic comorbidities, orthopaedic comorbidities and veterinary group. Inverse probability of treatment weighting (IPTW) was used to adjust for confounding, with weights calculated based on a binary logistic regression exposure model. Censored dogs were accounted for in the IPTW analysis using inverse probability of censoring weighting (IPCW). The IPCWs were combined with IPTWs and used to weight each dog’s contribution to binary logistic regression outcome models. Standardized mean differences (SMD) examined the balance of covariate distribution between treatment groups.The emulated trial included 615 surgical CCL rupture cases and 200 non-surgical cases. The risk difference for short-term lameness in surgically managed cases (compared with non-surgically managed cases) was -25.7% (95% confidence interval (CI) -36.7% to -15.9%) and the risk difference for long-term lameness -31.7% (95% CI -37.9% to -18.1%).The study demonstrated the application of the target trial framework to veterinary observational data. The findings show that surgical management causes a reduction in short- and long-term lameness compared with non-surgical management in dogs.

OBJECTIVE:Dose shortages delayed access to COVID-19 vaccination. We aim to characterise inequality in two-dose vaccination by sociodemographic group across Brazil. DESIGN:This is a cross-sectional study. SETTING:We used data retrieved from the Brazilian Ministry of Health databases published between 17 January 2021 and 6 September 2021. METHODS:We assessed geographical inequalities in full vaccination coverage and dose by age, sex, race and socioeconomic status. We developed a Campaign Optimality Index to characterise inequality in vaccination access due to premature vaccination towards younger populations before older and vulnerable populations were fully vaccinated. Generalised linear regression was used to investigate the risk of death and hospitalisation by age group, socioeconomic status and vaccination coverage. RESULTS:Vaccination coverage is higher in the wealthier South and Southeast. Men, people of colour and low-income groups were more likely to be only partially vaccinated due to missing or delaying a second dose. Vaccination started prematurely for age groups under 50 years which may have hindered uptake in older age groups. Vaccination coverage was associated with a lower risk of death, especially in older age groups (ORs 9.7 to 29.0, 95% CI 9. 4 to 29.9). Risk of hospitalisation was greater in areas with higher vaccination rates due to higher access to care and reporting. CONCLUSIONS:Vaccination inequality persists between states, age and demographic groups despite increasing uptake. The association between hospitalisation rates and vaccination is attributed to preferential delivery to areas of greater transmission and access to healthcare.

This study aimed to describe the relationship between blood and CSF volumes in different compartments on baseline CT after aSAH, assess if they independently predict long-term outcome, and explore their interaction with age. CT scans from patients participating in a prospective multicenter randomized controlled trial of patients with aSAH were segmented for blood and CSF volumes. The primary outcomes were the mRS, and the Subarachnoid Hemorrhage Outcome Tool (SAHOT) at day 28 and 180. Univariate regressions were conducted to identify significant predictors of poor outcomes, followed by principal component analysis to explore correlations between imaging variables and WFNS. A multivariate predictive model was then developed and optimized using stepwise regression. CT scans from 97 patients with a median delay from symptom onset of 271 min (131–547) were analyzed. Univariate analysis showed only WFNS, and total blood volume (TBV) were significant predictors of both short and long-term outcome with WFNS more predictive of mRS and TBV more predictive of SAHOT. Principal component analysis showed strong dependencies between the imaging predictors. Multivariate ordinal regression showed models with WFNS alone were most predictive of day 180 mRS and models with TBV alone were most predictive of SAHOT. TBV was the most significant measured imaging predictor of poor long-term outcome after aSAH. All these imaging predictors are correlated, however, and may have multiple complex interactions necessitating larger datasets to detect if they provide any additional predictive value for long-term outcome.

AbstractThe COVID-19 pandemic has caused over half a million deaths in Brazil, and public healthcare nearly collapsed. Vaccination differs between states and demographics. Dose shortages delayed access.In this cross-sectional study, data were retrieved from the Brazilian Ministry of Health databases published since 17 January 2021, respectively. We developed a campaign optimality index to characterise inequality in vaccination access caused by age due to premature vaccination towards younger populations before older and vulnerable populations were fully vaccinated. We assessed geographical inequalities in full vaccination coverage and dose by age, sex, race, and socioeconomic status. Generalised linear regression was used to investigate the risk of death and hospitalisation by age group, socioeconomic status, and vaccination coverage.Vaccination coverage is higher in the wealthier South and Southeast. Men, people of colour, and low-income groups were more likely to be only partially vaccinated due to missing or delaying a second dose. Vaccination started prematurely for age groups under 50 years and may have hindered uptake of older age groups. Vaccination coverage was associated with a lower risk of death, especially in older age groups (OR: 10.5–34.8, 95% CI: (10.2, 35.9)). Risk of hospitalisation was greater in areas with higher vaccination rates due to higher access to care and reporting.Vaccination inequality persists between states, age and demographic groups despite increasing uptake. The association between hospitalisation rates and vaccination is attributed to preferential delivery to areas of greater transmission and access to healthcare.

Classification of unlabeled data is usually achieved by supervised learning from labeled samples. Although there exist many sophisticated supervised machine learning methods that can predict the missing labels with a high level of accuracy, they often lack the required transparency in situations where it is important to provide interpretable results and meaningful measures of confidence. Body fluid classification of forensic casework data is the case in point. We develop a new Biclustering Dirichlet Process for Class-assignment with Random Matrices (BDP-CaRMa), with a three-level hierarchy of clustering, and a model-based approach to classification that adapts to block structure in the data matrix. As the class labels of some observations are missing, the number of rows in the data matrix for each class is unknown. BDP-CaRMa handles this and extends existing biclustering methods by simultaneously biclustering multiple matrices each having a randomly variable number of rows. We demonstrate our method by applying it to the motivating problem, which is the classification of body fluids based on mRNA profiles taken from crime scenes. The analyses of casework-like data show that our method is interpretable and produces well-calibrated posterior probabilities. Our model can be more generally applied to other types of data with a similar structure to the forensic data.

Continually emerging SARS- CoV-2 variants of concern that can evade immune defenses are driving recurrent epidemic waves of COVID- 19 globally. However, the impact of measures to contain the virus and their effect on lineage diversity dynamics are poorly understood. Here, we jointly analyzed international travel, public health and social measures (PHSM), COVID- 19 vaccine rollout, SARS- CoV-2 lineage diversity, and the case growth rate (GR) from March 2020 to September 2022 across 63 countries. We showed that despite worldwide vaccine rollout, PHSM are effective in mitigating epidemic waves and lineage diversity. An increase of 10,000 monthly travelers in a single country - to-country route between endemic countries corresponds to a 5.5% (95% CI: 2.9 to 8.2%) rise in local lineage diversity. After accounting for PHSM, natural immunity from previous infections, and waning immunity, we discovered a negative association between the GR of cases and adjusted vaccine coverage (AVC). We also observed a complex relationship between lineage diversity and vaccine rollout. Specifically, we found a significant negative association between lineage diversity and AVC at both low and high levels but not significant at the medium level. Our study deepens the understanding of population immunity and lineage dynamics for future pandemic preparedness and responsiveness.

The COVID–19 pandemic has caused over half a million deaths in Brazil, and public healthcare nearly collapsed. Vaccination differs between states and demographics. Dose shortages delayed access. In this cross-sectional study, data were retrieved from the Brazilian Ministry of Health databases published since 17 January 2021, respectively. We developed a campaign optimality index to characterise inequality in vaccination access caused by age due to premature vaccination towards younger populations before older and vulnerable populations were fully vaccinated. We assessed geographical inequalities in full vaccination coverage and dose by age, sex, race, and socioeconomic status. Generalised linear regression was used to investigate the risk of death and hospitalisation by age group, socioeconomic status, and vaccination coverage. Vaccination coverage is higher in the wealthier South and Southeast. Men, people of colour, and low–income groups were more likely to be only partially vaccinated due to missing or delaying a second dose. Vaccination started prematurely for age groups under 50 years and may have hindered uptake of older age groups. Vaccination coverage was associated with a lower risk of death, especially in older age groups (OR: 10.5–34.8, 95% CI: (10.2, 35.9)). Risk of hospitalisation was greater in areas with higher vaccination rates due to higher access to care and reporting. Vaccination inequality persists between states, age and demographic groups despite increasing uptake. The association between hospitalisation rates and vaccination is attributed to preferential delivery to areas of greater transmission and access to healthcare. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement SLL was supported by the Oxford Martin Programme on Pandemic Genomics and the Canadian Social Sciences and Humanities Research Council (SSHRC) Doctoral Fellowship. CAPJ is supported by FAPESP (2019/21858-0 and 2022/15985-1). AEZ was supported by the Oxford Martin School Programme on Pandemic Genomics. CAPJ and VHN were supported by Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior Brasil (CAPES) Finance Code 001. VHN is supported by the Brazilian National Council for Scientific and Technological Development (CNPq: 304714/2018- 6). ECS is supported by a Medical Research Council Sao Paulo Research Foundation (FAPESP) CADDE partnership award (MR/S0195/1 and FAPESP 18/14389-0) (http://caddecentre.org/). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This project was approved by the Brazilian National Research Ethics Committee (CONEP CAAE-30178220.3.1001.0068). This study used only openly available data that were originally located at https://opendatasus.saude.gov.br/dataset/srag-2020 https://opendatasus.saude.gov.br/dataset/srag-2021-a-2023 https://opendatasus.saude.gov.br/dataset/covid-19-vacinacao I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors. <https://github.com/sabrinalyli/BrazilCOVID19Vaccination>

China had conducted some of the most stringent public health measures to control the spread of successive SARS-CoV-2 variants. However, the effectiveness of these measures and their impacts on the associated disease burden have rarely been quantitatively assessed at the national level. To address this gap, we developed a stochastic age-stratified metapopulation model that incorporates testing, contact tracing and isolation, based on 419 million travel movements among 366 Chinese cities. The study period for this model began from September 2022. The COVID-19 disease burden was evaluated, considering 8 types of underlying health conditions in the Chinese population. We identified the marginal effects between the testing speed and reduction in the epidemic duration. The findings suggest that assuming a vaccine coverage of 89%, the Omicron-like wave could be suppressed by 3-day interval population-level testing (PLT), while it would become endemic with 4-day interval PLT, and without testing, it would result in an epidemic. PLT conducted every 3 days would not only eliminate infections but also keep hospital bed occupancy at less than 29.46% (95% CI, 22.73–38.68%) of capacity for respiratory illness and ICU bed occupancy at less than 58.94% (95% CI, 45.70–76.90%) during an outbreak. Furthermore, the underlying health conditions would lead to an extra 2.35 (95% CI, 1.89–2.92) million hospital admissions and 0.16 (95% CI, 0.13–0.2) million ICU admissions. Our study provides insights into health preparedness to balance the disease burden and sustainability for a country with a population of billions.

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI

Neurological complications occur in 4% of patients following acute COVID-19 infection, causing significant morbidity with lasting health economic consequences. However treatment studies to date in COVID-19 have not addressed neurological complications as outcome measures. We therefore performed a retrospective, non-interventional cohort study using the ISARIC-4C platform, assessing 62,729 hospital inpatients with severe COVID-19 between 31 Jan 2020 and 29 Jun 2021. Treatment with dexamethasone, remdesivir or both was compared to standard of care. The primary outcome was a neurological complication, namely stroke, seizure, meningitis/encephalitis or any other neurological complication, occurring at the point of death, discharge, or resolution of the COVID-19 clinical episode. A propensity scoring methodology was used to balance confounding between treatment groups and between patients with and without neurological complications. Treatment with dexamethasone, remdesivir or both reduced the incidence of neurological complications from severe COVID-19, with odds ratios of 0.76 (0.69-0.83), 0.68 (0.51-0.90) and 0.64 (0.56 -0.72) respec- tively. Neurological complications were associated with increased length of hospital stay, worse ability to self-care on discharge and increased mortality. This study is the first to focus on the prevention of neurological complications and strongly supports the continued use of both dexamethasone and remdesivir in severe COVID-19. Our results suggest that the established benefit of dexamethasone on mortality in COVID-19 is not associated with an increased burden of long-term neurological disability.

ObjectiveThe objective of this study was to assess the impact of treatment with dexamethasone, remdesivir or both on neurological complications in acute coronavirus diease 2019 (COVID‐19).MethodsWe used observational data from the International Severe Acute and emerging Respiratory Infection Consortium World Health Organization (WHO) Clinical Characterization Protocol, United Kingdom. Hospital inpatients aged ≥18 years with laboratory‐confirmed severe acute respiratory syndrome‐coronavirus 2 (SARS‐CoV‐2) infection admitted between January 31, 2020, and June 29, 2021, were included. Treatment allocation was non‐blinded and performed by reporting clinicians. A propensity scoring methodology was used to minimize confounding. Treatment with remdesivir, dexamethasone, or both was assessed against the standard of care. The primary outcome was a neurological complication occurring at the point of death, discharge, or resolution of the COVID‐19 clinical episode.ResultsOut of 89,297 hospital inpatients, 64,088 had severe COVID‐19 and 25,209 had non‐hypoxic COVID‐19. Neurological complications developed in 4.8% and 4.5%, respectively. In both groups, neurological complications were associated with increased mortality, intensive care unit (ICU) admission, worse self‐care on discharge, and time to recovery. In patients with severe COVID‐19, treatment with dexamethasone (n = 21,129), remdesivir (n = 1,428), and both combined (n = 10,846) were associated with a lower frequency of neurological complications: OR = 0.76 (95% confidence interval [CI] = 0.69–0.83), OR = 0.69 (95% CI = 0.51–0.90), and OR = 0.54 (95% CI = 0.47–0.61), respectively. In patients with non‐hypoxic COVID‐19, dexamethasone (n = 2,580) was associated with less neurological complications (OR = 0.78, 95% CI = 0.62–0.97), whereas the dexamethasone/remdesivir combination (n = 460) showed a similar trend (OR = 0.63, 95% CI = 0.31–1.15).InterpretationTreatment with dexamethasone, remdesivir, or both in patients hospitalized with COVID‐19 was associated with a lower frequency of neurological complications in an additive manner, such that the greatest benefit was observed in patients who received both drugs together. ANN NEUROL 2023;93:88–102

Model-based Bayesian evidence combination leads to models with multiple parameteric modules. In this setting the effects of model misspecification in one of the modules may in some cases be ameliorated by cutting the flow of information from the misspecified module. Semi-Modular Inference (SMI) is a framework allowing partial cuts which modulate but do not completely cut the flow of information between modules. We show that SMI is part of a family of inference procedures which implement partial cuts. It has been shown that additive losses determine an optimal, valid and order-coherent belief update. The losses which arise in Cut models and SMI are not additive. However, like the prequential score function, they have a kind of prequential additivity which we define. We show that prequential additivity is sufficient to determine the optimal valid and order-coherent belief update and that this belief update coincides with the belief update in each of our SMI schemes.

Zoonotic viruses that originate in wildlife harm global human health and economic prosperity 1 . Understanding virus transmission at the human-animal-environment interface is a key component of pandemic risk-reduction 2,3 . Zoonotic disease emergence is highest in biodiverse, tropical forests undergoing intensive land-use change 4,5 . Phylodynamic analyses of virus genomes can powerfully test epidemiological hypotheses, but are rarely applied to viruses of animals inhabiting these habitats. Brazil’s densely-populated Atlantic Forest and Cerrado region experienced in 2016–2021 an explosive human outbreak of sylvatic yellow fever, caused by repeated virus spillover from wild neotropical primates 6 . Here we use yellow fever virus (YFV) genome sequences and epidemiological data from neotropical primates, humans, and mosquito vectors to identify the environmental, demographic, and climatic factors determining zoonotic virus spread. Using portable sequencing approaches we generated 498 YFV genomes, resulting in a well-sampled dataset of zoonotic virus genomes sampled from wild mammals. YFV dispersal velocity was slower at higher elevation, in colder regions, and further away from main roads. Virus lineage dispersal was more frequent through wetter areas, areas with high neotropical primate density and through landscapes covered by mosaic vegetation. Higher temperatures were associated with higher virus effective population sizes, and peaks of transmission in warmer, wetter seasons were associated with higher virus evolutionary rates. Our study demonstrates how zoonotic disease transmission is linked to land-use and climate, underscoring the need for One-Health approaches to reducing the rate of zoonotic spillover.

People with chronic obstructive pulmonary disease, cardiovascular disease, or hypertension have a high risk of developing severe coronavirus disease 2019 (COVID-19) and of COVID-19 mortality. However, the association between long-term exposure to air pollutants, which increases cardiopulmonary damage, and vulnerability to COVID-19 has not yet been fully established. We collected data of confirmed COVID-19 cases during the first wave of the epidemic in mainland China. We fitted a generalized linear model using city-level COVID-19 cases and severe cases as the outcome, and long-term average air pollutant levels as the exposure. Our analysis was adjusted using several variables, including a mobile phone dataset, covering human movement from Wuhan before the travel ban and movements within each city during the period of the emergency response. Other variables included smoking prevalence, climate data, socioeconomic data, education level, and number of hospital beds for 324 cities in China. After adjusting for human mobility and socioeconomic factors, we found an increase of 37.8% (95% confidence interval [CI]: 23.8%-52.0%), 32.3% (95% CI: 22.5%-42.4%), and 14.2% (7.9%-20.5%) in the number of COVID-19 cases for every 10-μg/m3 increase in long-term exposure to NO2, PM2.5, and PM10, respectively. However, when stratifying the data according to population size, the association became non-significant. The present results are derived from a large, newly compiled and geocoded repository of population and epidemiological data relevant to COVID-19. The findings suggested that air pollution may be related to population vulnerability to COVID-19 infection, although the extent to which this relationship is confounded by city population density needs further exploration.

In Bayesian phylogenetics, the coalescent process provides an informative framework for inferring changes in the effective size of a population from a phylogeny (or tree) of sequences sampled from that population. Popular coalescent inference approaches such as the Bayesian Skyline Plot, Skyride, and Skygrid all model these population size changes with a discontinuous, piecewise-constant function but then apply a smoothing prior to ensure that their posterior population size estimates transition gradually with time. These prior distributions implicitly encode extra population size information that is not available from the observed coalescent data or tree. Here, we present a novel statistic, $\Omega$, to quantify and disaggregate the relative contributions of the coalescent data and prior assumptions to the resulting posterior estimate precision. Our statistic also measures the additional mutual information introduced by such priors. Using $\Omega$ we show that, because it is surprisingly easy to overparametrize piecewise-constant population models, common smoothing priors can lead to overconfident and potentially misleading inference, even under robust experimental designs. We propose $\Omega$ as a useful tool for detecting when effective population size estimates are overly reliant on prior assumptions and for improving quantification of the uncertainty in those estimates.[Coalescent processes; effective population size; information theory; phylodynamics; prior assumptions; skyline plots.].

Cases of SARS-CoV-2 infection in Manaus, Brazil, resurged in late 2020, despite high levels of previous infection there. Through genome sequencing of viruses sampled in Manaus between November 2020 and January 2021, we identified the emergence and circulation of a novel SARS-CoV-2 variant of concern, lineage P.1, that acquired 17 mutations, including a trio in the spike protein (K417T, E484K and N501Y) associated with increased binding to the human ACE2 receptor. Molecular clock analysis shows that P.1 emergence occurred around early November 2020 and was preceded by a period of faster molecular evolution. Using a two-category dynamical model that integrates genomic and mortality data, we estimate that P.1 may be 1.4-2.2 times more transmissible and 25-61% more likely to evade protective immunity elicited by previous infection with non-P.1 lineages. Enhanced global genomic surveillance of variants of concern, which may exhibit increased transmissibility and/or immune evasion, is critical to accelerate pandemic responsiveness.

Brazil has one of the fastest-growing COVID-19 epidemics worldwide. Non-pharmaceutical interventions (NPIs) have been adopted at the municipal level with asynchronous actions taken across 5,568 municipalities and the Federal District. This paper systematises the fragmented information on NPIs reporting on a novel dataset with survey responses from 4,027 mayors, covering 72.3% of all municipalities in the country. This dataset responds to the urgency to track and share findings on fragmented policies during the COVID-19 pandemic. Quantifying NPIs can help to assess the role of interventions in reducing transmission. We offer spatial and temporal details for a range of measures aimed at implementing social distancing and the dates when these measures were relaxed by local governments.

Staphylococcus aureus is a major bacterial pathogen in humans, and a dominant cause of severe bloodstream infections. Globally, antimicrobial resistance (AMR) in S. aureus remains challenging. While human risk factors for infection have been defined, contradictory evidence exists for the role of bacterial genomic variation in S. aureus disease. To investigate the contribution of bacterial lineage and genomic variation to the development of bloodstream infection, we undertook a genome-wide association study comparing bacteria from 1017 individuals with bacteraemia to 984 adults with asymptomatic S. aureus nasal carriage. Within 984 carriage isolates, we also compared healthcare-associated (HA) carriage with community-associated (CA) carriage. All major global lineages were represented in both bacteraemia and carriage, with no evidence for different infection rates. However, kmers tagging trimethoprim resistance-conferring mutation F99Y in dfrB were significantly associated with bacteraemia-vs-carriage ( P= 10 -8.9 -10 -9.3 ). Pooling variation within genes, bacteraemia-vs-carriage was associated with the presence of mecA (HMP=10 -5.3 ) as well as the presence of SCCmec (HMP=10 -4.4 ). Among S. aureus carriers, no lineages were associated with HA-vs-CA carriage. However, we found a novel signal of HA-vs-CA carriage in the foldase protein prsA , where kmers representing conserved sequence allele were associated with CA carriage ( P= 10 -7.1 -10 -19.4 ), while in gyrA , a ciprofloxacin resistance-conferring mutation, L84S, was associated with HA carriage ( P= 10 -7.2 ). In an extensive study of S. aureus bacteraemia and nasal carriage in the UK, we found strong evidence that all S. aureus lineages are equally capable of causing bloodstream infection, and of being carried in the healthcare environment. Genomic variation in the foldase protein prsA is a novel genomic marker of healthcare origin in S. aureus but was not associated with bacteraemia. AMR determinants were associated with both bacteraemia and healthcare-associated carriage, suggesting that AMR increases the propensity not only to survive in healthcare environments, but also to cause invasive disease.

Background: A new SARS-CoV-2 lineage, named P.1 (20J/501Y.V3), has recently been detected in Brazil. Mutations accrued by the P.1 lineage include amino acid changes in the receptor-binding domain of the spike protein that also are reported in variants of concern in the United Kingdom (B.1.1.7) and South Africa (B.1.325). Methods: We isolated two P.1-containing specimens from nasopharyngeal and bronchoalveolar lavage samples of patients of Manaus, Brazil. We measured neutralization of the P.1 virus after incubation with the plasma of 19 COVID-19 convalescent blood donors and recipients of the chemically-inactivated CoronaVac vaccine and compared these results to neutralization of a SARS-CoV-2 B-lineage previously circulating in Brazil. Findings: The immune plasma of COVID-19 convalescent blood donors had 6-fold less neutralizing capacity against the P.1 than against the B-lineage. Moreover, five months after booster immunization with CoronaVac, plasma from vaccinated individuals failed to efficiently neutralize P.1 lineage isolates. Interpretation: These data indicate that the P.1 lineage may escape from neutralizing antibodies generated in response to polyclonal stimulation against previously circulating variants of SARS-CoV-2. Funding: Sao Paulo Research Foundation, MCTI/FINEP, Medical Research Council, National Council for Scientific and Technological Development, National Institutes of Health. Conflict of Interest: M.S.D. is a consultant for Inbios, Vir Biotechnology, NGMBiopharmaceuticals, and Carnival Corporation, and on the Scientific Advisory Boards of Moderna and Immunome. The Diamond laboratory has received funding support in sponsored research agreements from Moderna, Vir Biotechnology, and Emergent BioSolutions. Ethical Approval: All procedures followed the ethical standards of the responsible committee on humanexperimentation and approved by the ethics committees from the University of Campinas, Brazil (Approval number CONEP 4.021.484 for plasma collection of blood donors, CAEE32078620.4.0000.5404 and 30227920.9.0000.5404 for the sampling of vaccinated and viral genome sequencing, respectively). All patient data were anonymized before study inclusion.Informed consent was obtained from all subjects for being included in the study.