The SARS-CoV-2 epidemic in southern Africa has been characterized by three distinct waves. The first was associated with a mix of SARS-CoV-2 lineages, while the second and third waves were driven by the Beta (B.1.351) and Delta (B.1.617.2) variants, respectively1–3. In November 2021, genomic surveillance teams in South Africa and Botswana detected a new SARS-CoV-2 variant associated with a rapid resurgence of infections in Gauteng province, South Africa. Within three days of the first genome being uploaded, it was designated a variant of concern (Omicron, B.1.1.529) by the World Health Organization and, within three weeks, had been identified in 87 countries. The Omicron variant is exceptional for carrying over 30 mutations in the spike glycoprotein, which are predicted to influence antibody neutralization and spike function4. Here we describe the genomic profile and early transmission dynamics of Omicron, highlighting the rapid spread in regions with high levels of population immunity.

There are outstanding evolutionary questions on the recent emergence of coronavirus in Hubei province that caused the COVID-19 pandemic, including (1) the relationship of the new virus to the SARS-related coronaviruses, (2) the role of bats as a reservoir species, (3) the potential role of other mammals in the emergence event, and (4) the role of recombination in viral emergence. Here, we address these questions and find that the sarbecoviruses -the viral subgenus responsible for the emergence of SARS-CoV and SARS-CoV-2 -exhibit frequent recombination, but the SARS-CoV-2 lineage itself is not a recombinant of any viruses detected to date. In order to employ phylogenetic methods to date the divergence events between SARS-CoV-2 and the bat sarbecovirus reservoir, recombinant regions of a 68-genome sarbecovirus alignment were removed with three independent methods. Bayesian evolutionary rate and divergence date estimates were consistent for all three recombination-free alignments and robust to two different prior specifications based on HCoV-OC43 and MERS-CoV evolutionary rates. Divergence dates between SARS-CoV-2 and the bat sarbecovirus reservoir were estimated as 1948 (95% HPD: 1879-1999), 1969 (95% HPD: 1930-2000), and 1982 (95% HPD: 1948-2009). Despite intensified characterization of sarbecoviruses since SARS, the lineage giving rise to SARS-CoV-2 has been circulating unnoticed for decades in bats and been transmitted to other hosts such as pangolins. The occurrence of a third significant coronavirus emergence in 17 years together with the high prevalence and virus diversity in bats implies that these viruses are likely to cross species boundaries again. : bioRxiv preprint 3 / 25 In BriefThe Betacoronavirus SARS-CoV-2 is a member of the sarbecovirus subgenus which shows frequent recombination in its evolutionary history. We characterize the extent of this genetic exchange and identify non-recombining regions of the sarbecovirus genome using three independent methods to remove the effects of recombination. Using these non-recombining genome regions and prior information on coronavirus evolutionary rates, we obtain estimates from three approaches that the most likely divergence date of SARS-CoV-2 from its most closely related available bat sequences ranges from 1948 to 1982. Key Points RaTG13 is the closest available bat virus to SARS-CoV-2; a sub-lineage of these bat viruses is able to infect humans. Two sister lineages of the RaTG13/SARS-CoV-2 lineage infect Malayan pangolins. The sarbecoviruses show a pattern of deep recombination events, indicating that there are high levels of co-infection in horseshoe bats and that the viral pool can generate novel allele combinations and substantial genetic diversity; the sarbecoviruses are efficient 'explorers' of phenotype space. The SARS-CoV-2 lineage is not a recent recombinant, at least not involving any of the bat or pangolin viruses sampled to date. Non-recombinant regions of the sarbecoviruses can be identified, allowing for phylogenetic inference and dating...

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Statistical tests for detecting mosaic structure or recombination among nucleotide sequences usually rely on identifying a pattern or a signal that would be unlikely to appear under clonal reproduction. Dozens of such tests have been described, but many are hampered by long running times, confounding of selection and recombination, and/or inability to isolate the mosaic-producing event. We introduce a test that is exact, nonparametric, rapidly computable, free of the infinite-sites assumption, able to distinguish between recombination and variation in mutation/fixation rates, and able to identify the breakpoints and sequences involved in the mosaic-producing event. Our test considers three sequences at a time: two parent sequences that may have recombined, with one or two breakpoints, to form the third sequence (the child sequence). Excess similarity of the child sequence to a candidate recombinant of the parents is a sign of recombination; we take the maximum value of this excess similarity as our test statistic D m,n,b . We present a method for rapidly calculating the distribution of D m,n,b and demonstrate that it has comparable power to and a much improved running time over previous methods, especially in detecting recombination in large data sets. M OSAIC structure exists in a nucleotide sequence if different segments of the sequence descend from different ancestors. A nucleotide sequence can be a mosaic of other sequences as a result of recombination or gene conversion; mosaic structure in bacterial DNA can also result from transduction, transformation, or conjugation, which are collectively referred to as horizontal gene transfer. The detection of mosaic structure has received much attention over the past two decades as a result of both a proliferation of sequence data and leaps in computing power, which together have allowed for the inference of multiple ancestral contributions to a nucleotide sequence. The biological questions at the source of this recent attention range from interest in the evolution of pathogens (Awadalla 2003;Moya et al. 2004;Wilson et al. 2005) and the characterization of linkage disequilibrium in large genomes (Pritchard and Przeworski 2001;Ardlie et al. 2002;Gabriel et al. 2002) to theoretical questions about clonality and the definitions of clonal and nearly clonal organisms (Maynard Smith et al. 1993;Halkett et al. 2005). For reviews on the methods and results in this field, see Posada et al. (2002) and Stumpf and McVean (2003).Maynard recognized that the continuum between completely clonal and freely recombining organisms naturally gives rise to two distinct problems: determining whether recombination occurs and measuring its frequency. In this investigation, we focus on the former. Detecting recombination usually involves searching groups of sequences for candidate recombinants or recombination signals and testing whether these represent statistically significant departures from expectation under a null hypothesis of no recombination. Dozens of statistical tests have ...

The recent emergence of dengue viruses into new susceptible human populations throughout Asia and the Middle East, driven in part by human travel on both local and global scales, represents a significant global health risk, particularly in areas with changing climatic suitability for the mosquito vector. In Pakistan, dengue has been endemic for decades in the southern port city of Karachi, but large epidemics in the northeast have emerged only since 2011. Pakistan is therefore representative of many countries on the verge of countrywide endemic dengue transmission, where prevention, surveillance, and preparedness are key priorities in previously dengue-free regions. We analyze spatially explicit dengue case data from a large outbreak in Pakistan in 2013 and compare the dynamics of the epidemic to an epidemiological model of dengue virus transmission based on climate and mobility data from ∼40 million mobile phone subscribers. We find that mobile phone-based mobility estimates predict the geographic spread and timing of epidemics in both recently epidemic and emerging locations. We combine transmission suitability maps with estimates of seasonal dengue virus importation to generate fine-scale dynamic risk maps with direct application to dengue containment and epidemic preparedness.dengue | human mobility | Pakistan | mobile phones | epidemiology

The widespread distribution and relapsing nature of Plasmodium vivax infection present major challenges for malaria elimination. To characterise the genetic diversity of this parasite within individual infections and across the population, we performed deep genome sequencing of >200 clinical samples collected across the Asia-Pacific region, and analysed data on >300,000 SNPs and 9 regions of the genome with large copy number variations. Individual infections showed complex patterns of genetic structure, with variation not only in the number of dominant clones but also in their level of relatedness and inbreeding. At the population level, we observed strong signals of recent evolutionary selection both in known drug resistance genes and at novel loci, and these varied markedly between geographical locations. These findings reveal a dynamic landscape of local evolutionary adaptation in P. vivax populations, and provide a foundation for genomic surveillance to guide effective strategies for control and elimination.

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Since the first reports of a novel SARS-like coronavirus in December 2019 in Wuhan, China, there has been intense interest in understanding how SARS-CoV-2 emerged in the human population. Recent debate has coalesced around two competing ideas: a “laboratory escape” scenario and zoonotic emergence. Here, we critically review the current scientific evidence that may help clarify the origin of SARS-CoV-2.