Search

Cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in Manaus, Brazil, resurged in late 2020 despite previously high levels of infection. Genome sequencing of viruses sampled in Manaus between November 2020 and January 2021 revealed the emergence and circulation of a novel SARS-CoV-2 variant of concern. Lineage P.1 acquired 17 mutations, including a trio in the spike protein (K417T, E484K, and N501Y) associated with increased binding to the human ACE2 (angiotensin-converting enzyme 2) receptor. Molecular clock analysis shows that P.1 emergence occurred around mid-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 1.7- to 2.4-fold more transmissible and that previous (non-P.1) infection provides 54 to 79% of the protection against infection with P.1 that it provides against 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.

Cases of a novel coronavirus were first reported in Wuhan, Hubei province, China, in December 2019 and have since spread across the world. Epidemiological studies have indicated human-to-human transmission in China and elsewhere. To aid the analysis and tracking of the COVID-19 epidemic we collected and curated individual-level data from national, provincial, and municipal health reports, as well as additional information from online reports. All data are geo-coded and, where available, include symptoms, key dates (date of onset, admission, and confirmation), and travel history. The generation of detailed, real-time, and robust data for emerging disease outbreaks is important and can help to generate robust evidence that will support and inform public health decision making.

Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates, phenotypic data, radiocarbon dates, fossil occurrences, and biogeographic range information among others. Including all relevant data into a single joint model is very challenging both conceptually and computationally. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing such software frameworks is increasingly a major scientific activity in its own right, and comes with specific challenges, from practical software design, development and engineering challenges to statistical and conceptual modelling challenges. BEAST 2 is one such computational software platform, and was first announced over 4 years ago. Here we describe a series of major new developments in the BEAST 2 core platform and model hierarchy that have occurred since the first release of the software, culminating in the recent 2.5 release.

Outbreaks caused by Dengue, Zika and Chikungunya viruses can spread rapidly in immunologically naïve populations. By analysing 92 newly generated viral genome sequences from blood donors and recipients, we assess the dynamics of dengue virus serotype 4 during the 2012 outbreak in Rio de Janeiro. Phylogenetic analysis indicates that the outbreak was caused by genotype II, although two isolates of genotype I were also detected for the first time in Rio de Janeiro. Evolutionary analysis and modelling estimates are congruent, indicating a reproduction number above 1 between January and June, and at least two thirds of infections being unnoticed. Modelling analysis suggests that viral transmission started in early January, which is consistent with multiple introductions, most likely from the northern states of Brazil, and with an increase in within-country air travel to Rio de Janeiro. The combination of genetic and epidemiological data from blood donor banks may be useful to anticipate epidemic spread of arboviruses.

We present a new open source, extensible and flexible software platform for Bayesian evolutionary analysis called BEAST 2. This software platform is a re-design of the popular BEAST 1 platform to correct structural deficiencies that became evident as the BEAST 1 software evolved. Key among those deficiencies was the lack of post-deployment extensibility. BEAST 2 now has a fully developed package management system that allows third party developers to write additional functionality that can be directly installed to the BEAST 2 analysis platform via a package manager without requiring a new software release of the platform. This package architecture is showcased with a number of recently published new models encompassing birth-death-sampling tree priors, phylodynamics and model averaging for substitution models and site partitioning. A second major improvement is the ability to read/write the entire state of the MCMC chain to/from disk allowing it to be easily shared between multiple instances of the BEAST software. This facilitates checkpointing and better support for multi-processor and high-end computing extensions. Finally, the functionality in new packages can be easily added to the user interface (BEAUti 2) by a simple XML template-based mechanism because BEAST 2 has been re-designed to provide greater integration between the analysis engine and the user interface so that, for example BEAST and BEAUti use exactly the same XML file format.

Probabilistic inference of a phylogenetic tree from molecular sequence data is predicated on a substitution model describing the relative rates of change between character states along the tree for each site in the multiple sequence alignment. Commonly, one assumes that the substitution model is homogeneous across sites within large partitions of the alignment, assigns these partitions a priori, and then fixes their underlying substitution model to the best-fitting model from a hierarchy of named models. Here, we introduce an automatic model selection and model averaging approach within a Bayesian framework that simultaneously estimates the number of partitions, the assignment of sites to partitions, the substitution model for each partition, and the uncertainty in these selections. This new approach is implemented as an add-on to the BEAST 2 software platform. We find that this approach dramatically improves the fit of the nucleotide substitution model compared with existing approaches, and we show, using a number of example data sets, that as many as nine partitions are required to explain the heterogeneity in nucleotide substitution process across sites in a single gene analysis. In some instances, this improved modeling of the substitution process can have a measurable effect on downstream inference, including the estimated phylogeny, relative divergence times, and effective population size histories.

Objective: The 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). Methods: We 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. Results: Out 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). Interpretation: Treatment 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 [ABSTRACT FROM AUTHOR]