Decoding the Fundamental Drivers of Phylodynamic Inference.

Despite its increasing role in the understanding of infectious disease transmission at the applied and theoretical levels, phylodynamics lacks a well-defined notion of ideal data and optimal sampling. We introduce a method to visualize and quantify the relative impact of pathogen genome sequence and sampling times-two fundamental sources of data for phylodynamics under birth-death-sampling models-to understand how each drives phylodynamic inference. Applying our method to simulated data and real-world SARS-CoV-2 and H1N1 Influenza data, we use this insight to elucidate fundamental trade-offs and guidelines for phylodynamic analyses to draw the most from sequence data. Phylodynamics promises to be a staple of future responses to infectious disease threats globally. Continuing research into the inherent requirements and trade-offs of phylodynamic data and inference will help ensure phylodynamic tools are wielded in ever more targeted and efficient ways.

Evolutionary rate of SARS-CoV-2 increases during zoonotic infection of farmed mink.

To investigate genetic signatures of adaptation to the mink host, we characterised the evolutionary rate heterogeneity in mink-associated severe acute respiratory syndrome coronaviruses (SARS-CoV-2). In 2020, the first detected anthropozoonotic spillover event of SARS-CoV-2 occurred in mink farms throughout Europe and North America. Both spill-back of mink-associated lineages into the human population and the spread into the surrounding wildlife were reported, highlighting the potential formation of a zoonotic reservoir. Our findings suggest that the evolutionary rate of SARS-CoV-2 underwent an episodic increase upon introduction into the mink host before returning to the normal range observed in humans. Furthermore, SARS-CoV-2 lineages could have circulated in the mink population for a month before detection, and during this period, evolutionary rate estimates were between 3 × 10-3 and 1.05 × 10-2 (95 per cent HPD, with a mean rate of 6.59 × 10-3) a four- to thirteen-fold increase compared to that in humans. As there is evidence for unique mutational patterns within mink-associated lineages, we explored the emergence of four mink-specific Spike protein amino acid substitutions Y453F, S1147L, F486L, and Q314K. We found that mutation Y453F emerged early in multiple mink outbreaks and that mutations F486L and Q314K may co-occur. We suggest that SARS-CoV-2 undergoes a brief, but considerable, increase in evolutionary rate in response to greater selective pressures during species jumps, which may lead to the occurrence of mink-specific mutations. These findings emphasise the necessity of ongoing surveillance of zoonotic SARS-CoV-2 infections in the future.

Phytest: quality control for phylogenetic analyses.

MOTIVATION: The ability to automatically conduct quality control checks on phylogenetic analyses is becoming more important with the increase in genetic sequencing and the use of real-time pipelines e.g. in the SARS-CoV-2 era. Implementations of real-time phylogenetic analyses require automated testing to make sure that problems in the data are caught automatically within analysis pipelines and in a timely manner. Here, we present Phytest (version 1.1) a tool for automating quality control checks on sequences, trees and metadata during phylogenetic analyses. RESULTS: Phytest is a phylogenetic analysis testing program that easily integrates into existing phylogenetic pipelines. We demonstrate the utility of Phytest with real-world examples. AVAILABILITY AND IMPLEMENTATION: Phytest source code is available on GitHub (https://github.com/phytest-devs/phytest) and can be installed via PyPI with the command 'pip install phytest'. Extensive documentation can be found at https://phytest-devs.github.io/phytest/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genomic characterisation reveals a dominant lineage of SARS-CoV-2 in Papua New Guinea.

The coronavirus disease pandemic has highlighted the utility of pathogen genomics as a key part of comprehensive public health response to emerging infectious diseases threats, however, the ability to generate, analyse, and respond to pathogen genomic data varies around the world. Papua New Guinea (PNG), which has limited in-country capacity for genomics, has experienced significant outbreaks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with initial genomics data indicating a large proportion of cases were from lineages that are not well defined within the current nomenclature. Through a partnership between in-country public health agencies and academic organisations, industry, and a public health genomics reference laboratory in Australia a system for routine SARS-CoV-2 genomics from PNG was established. Here we aim to characterise and describe the genomics of PNG's second wave and examine the sudden expansion of a lineage that is not well defined but very prevalent in the Western Pacific region. We generated 1797 sequences from cases in PNG and performed phylogenetic and phylodynamic analyses to examine the outbreak and characterise the circulating lineages and clusters present. Our results reveal the rapid expansion of the B.1.466.2 and related lineages within PNG, from multiple introductions into the country. We also highlight the difficulties that unstable lineage assignment causes when using genomics to assist with rapid cluster definitions.

Epidemiological inference from pathogen genomes: A review of phylodynamic models and applications.

Phylodynamics requires an interdisciplinary understanding of phylogenetics, epidemiology, and statistical inference. It has also experienced more intense application than ever before amid the SARS-CoV-2 pandemic. In light of this, we present a review of phylodynamic models beginning with foundational models and assumptions. Our target audience is public health researchers, epidemiologists, and biologists seeking a working knowledge of the links between epidemiology, evolutionary models, and resulting epidemiological inference. We discuss the assumptions linking evolutionary models of pathogen population size to epidemiological models of the infected population size. We then describe statistical inference for phylodynamic models and list how output parameters can be rearranged for epidemiological interpretation. We go on to cover more sophisticated models and finish by highlighting future directions.

The International Virus Bioinformatics Meeting 2022.

The International Virus Bioinformatics Meeting 2022 took place online, on 23-25 March 2022, and has attracted about 380 participants from all over the world. The goal of the meeting was to provide a meaningful and interactive scientific environment to promote discussion and collaboration and to inspire and suggest new research directions and questions. The participants created a highly interactive scientific environment even without physical face-to-face interactions. This meeting is a focal point to gain an insight into the state-of-the-art of the virus bioinformatics research landscape and to interact with researchers in the forefront as well as aspiring young scientists. The meeting featured eight invited and 18 contributed talks in eight sessions on three days, as well as 52 posters, which were presented during three virtual poster sessions. The main topics were: SARS-CoV-2, viral emergence and surveillance, virus-host interactions, viral sequence analysis, virus identification and annotation, phages, and viral diversity. This report summarizes the main research findings and highlights presented at the meeting.

Assessment of Coronavirus Disease 2019 Intervention Strategies in the Nordic Countries Using Genomic Epidemiology.

We explored how the duration, size, and number of virus transmission clusters, defined as country-specific monophyletic groups in a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) phylogenetic tree, differed among the Nordic countries of Norway, Sweden, Denmark, Finland, and Iceland. Our results suggest that although geographical connectivity, population density, and openness influence the spread and the size of SARS-CoV-2 transmission clusters, the different country-specific intervention strategies had the largest impact. We also found a significant positive association between the size and duration of transmission clusters in the Nordic countries, suggesting that the rapid deployment of contact tracing is a key response measure in reducing virus transmission.

The Emergence of SARS-CoV-2 Variants of Concern Is Driven by Acceleration of the Substitution Rate.

The ongoing SARS-CoV-2 pandemic has seen an unprecedented amount of rapidly generated genome data. These data have revealed the emergence of lineages with mutations associated to transmissibility and antigenicity, known as variants of concern (VOCs). A striking aspect of VOCs is that many of them involve an unusually large number of defining mutations. Current phylogenetic estimates of the substitution rate of SARS-CoV-2 suggest that its genome accrues around two mutations per month. However, VOCs can have 15 or more defining mutations and it is hypothesized that they emerged over the course of a few months, implying that they must have evolved faster for a period of time. We analyzed genome sequence data from the GISAID database to assess whether the emergence of VOCs can be attributed to changes in the substitution rate of the virus and whether this pattern can be detected at a phylogenetic level using genome data. We fit a range of molecular clock models and assessed their statistical performance. Our analyses indicate that the emergence of VOCs is driven by an episodic increase in the substitution rate of around 4-fold the background phylogenetic rate estimate that may have lasted several weeks or months. These results underscore the importance of monitoring the molecular evolution of the virus as a means of understanding the circumstances under which VOCs may emerge.

Assessment of COVID-19 intervention strategies in the Nordic countries using genomic epidemiology

We explored how the duration, size and number of virus transmission clusters, defined as country-specific monophyletic groups in a SARS-CoV-2 phylogenetic tree, differed between the Nordic countries of Norway, Sweden, Denmark, Finland and Iceland. Our results suggest that although geographical connectivity, population density and openness influence the spread and the size of SARS-CoV-2 transmission clusters, the differing country-specific intervention strategies had the largest impact. We also found a significant positive association between the size and duration of transmission clusters in the Nordic countries, suggesting that the rapid deployment of contact tracing is a key response measure in reducing virus transmission.

The impact of public health interventions in the Nordic countries during the first year of SARS-CoV-2 transmission and evolution.

BackgroundMany countries have attempted to mitigate and control COVID-19 through non-pharmaceutical interventions, particularly with the aim of reducing population movement and contact. However, it remains unclear how the different control strategies impacted the local phylodynamics of the causative SARS-CoV-2 virus.AimWe aimed to assess the duration of chains of virus transmission within individual countries and the extent to which countries exported viruses to their geographical neighbours.MethodsWe analysed complete SARS-CoV-2 genomes to infer the relative frequencies of virus importation and exportation, as well as virus transmission dynamics, in countries of northern Europe. We examined virus evolution and phylodynamics in Denmark, Finland, Iceland, Norway and Sweden during the first year of the COVID-19 pandemic.ResultsThe Nordic countries differed markedly in the invasiveness of control strategies, which we found reflected in transmission chain dynamics. For example, Sweden, which compared with the other Nordic countries relied more on recommendation-based rather than legislation-based mitigation interventions, had transmission chains that were more numerous and tended to have more cases. This trend increased over the first 8 months of 2020. Together with Denmark, Sweden was a net exporter of SARS-CoV-2. Norway and Finland implemented legislation-based interventions; their transmission chain dynamics were in stark contrast to their neighbouring country Sweden.ConclusionSweden constituted an epidemiological and evolutionary refugium that enabled the virus to maintain active transmission and spread to other geographical locations. Our analysis reveals the utility of genomic surveillance where monitoring of active transmission chains is a key metric.

The first wave of the COVID-19 epidemic in Spain was associated with early introductions and fast spread of a dominating genetic variant.

The coronavirus disease 2019 (COVID-19) pandemic has affected the world radically since 2020. Spain was one of the European countries with the highest incidence during the first wave. As a part of a consortium to monitor and study the evolution of the epidemic, we sequenced 2,170 samples, diagnosed mostly before lockdown measures. Here, we identified at least 500 introductions from multiple international sources and documented the early rise of two dominant Spanish epidemic clades (SECs), probably amplified by superspreading events. Both SECs were related closely to the initial Asian variants of SARS-CoV-2 and spread widely across Spain. We inferred a substantial reduction in the effective reproductive number of both SECs due to public-health interventions (Re < 1), also reflected in the replacement of SECs by a new variant over the summer of 2020. In summary, we reveal a notable difference in the initial genetic makeup of SARS-CoV-2 in Spain compared with other European countries and show evidence to support the effectiveness of lockdown measures in controlling virus spread, even for the most successful genetic variants.

Genomics-informed responses in the elimination of COVID-19 in Victoria, Australia: an observational, genomic epidemiological study.

BACKGROUND: A cornerstone of Australia's ability to control COVID-19 has been effective border control with an extensive supervised quarantine programme. However, a rapid recrudescence of COVID-19 was observed in the state of Victoria in June, 2020. We aim to describe the genomic findings that located the source of this second wave and show the role of genomic epidemiology in the successful elimination of COVID-19 for a second time in Australia. METHODS: In this observational, genomic epidemiological study, we did genomic sequencing of all laboratory-confirmed cases of COVID-19 diagnosed in Victoria, Australia between Jan 25, 2020, and Jan 31, 2021. We did phylogenetic analyses, genomic cluster discovery, and integrated results with epidemiological data (detailed information on demographics, risk factors, and exposure) collected via interview by the Victorian Government Department of Health. Genomic transmission networks were used to group multiple genomic clusters when epidemiological and genomic data suggested they arose from a single importation event and diversified within Victoria. To identify transmission of emergent lineages between Victoria and other states or territories in Australia, all publicly available SARS-CoV-2 sequences uploaded before Feb 11, 2021, were obtained from the national sequence sharing programme AusTrakka, and epidemiological data were obtained from the submitting laboratories. We did phylodynamic analyses to estimate the growth rate, doubling time, and number of days from the first local infection to the collection of the first sequenced genome for the dominant local cluster, and compared our growth estimates to previously published estimates from a similar growth phase of lineage B.1.1.7 (also known as the Alpha variant) in the UK. FINDINGS: Between Jan 25, 2020, and Jan 31, 2021, there were 20 451 laboratory-confirmed cases of COVID-19 in Victoria, Australia, of which 15 431 were submitted for sequencing, and 11 711 met all quality control metrics and were included in our analysis. We identified 595 genomic clusters, with a median of five cases per cluster (IQR 2-11). Overall, samples from 11 503 (98·2%) of 11 711 cases clustered with another sample in Victoria, either within a genomic cluster or transmission network. Genomic analysis revealed that 10 426 cases, including 10 416 (98·4%) of 10 584 locally acquired cases, diagnosed during the second wave (between June and October, 2020) were derived from a single incursion from hotel quarantine, with the outbreak lineage (transmission network G, lineage D.2) rapidly detected in other Australian states and territories. Phylodynamic analyses indicated that the epidemic growth rate of the outbreak lineage in Victoria during the initial growth phase (samples collected between June 4 and July 9, 2020; 47·4 putative transmission events, per branch, per year [1/years; 95% credible interval 26·0-85·0]), was similar to that of other reported variants, such as B.1.1.7 in the UK (mean approximately 71·5 1/years). Strict interventions were implemented, and the outbreak lineage has not been detected in Australia since Oct 29, 2020. Subsequent cases represented independent international or interstate introductions, with limited local spread. INTERPRETATION: Our study highlights how rapid escalation of clonal outbreaks can occur from a single incursion. However, strict quarantine measures and decisive public health responses to emergent cases are effective, even with high epidemic growth rates. Real-time genomic surveillance can alter the way in which public health agencies view and respond to COVID-19 outbreaks. FUNDING: The Victorian Government, the National Health and Medical Research Council Australia, and the Medical Research Future Fund.

The Impacts of Low Diversity Sequence Data on Phylodynamic Inference during an Emerging Epidemic.

Phylodynamic inference is a pivotal tool in understanding transmission dynamics of viral outbreaks. These analyses are strongly guided by the input of an epidemiological model as well as sequence data that must contain sufficient intersequence variability in order to be informative. These criteria, however, may not be met during the early stages of an outbreak. Here we investigate the impact of low diversity sequence data on phylodynamic inference using the birth-death and coalescent exponential models. Through our simulation study, estimating the molecular evolutionary rate required enough sequence diversity and is an essential first step for any phylodynamic inference. Following this, the birth-death model outperforms the coalescent exponential model in estimating epidemiological parameters, when faced with low diversity sequence data due to explicitly exploiting the sampling times. In contrast, the coalescent model requires additional samples and therefore variability in sequence data before accurate estimates can be obtained. These findings were also supported through our empirical data analyses of an Australian and a New Zealand cluster outbreaks of SARS-CoV-2. Overall, the birth-death model is more robust when applied to datasets with low sequence diversity given sampling is specified and this should be considered for future viral outbreak investigations.

Genomic Epidemiology of the First Wave of SARS-CoV-2 in Italy.

Italy was one of the first countries to experience a major epidemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with >1000 cases confirmed by 1 March 2020. However, virus genome sequence data is sparse and there has been only limited investigation of virus transmission across the country. Here, we provide the most extensive study to date of the genomic epidemiology of SARS-CoV-2 in Italy covering the first wave of infection. We generated 191 new full-length genomes, largely sampled from central Italy (Abruzzo), before, during, and after the enforcement of a nationwide "lockdown" (8 March-3 June). These were combined with 460 published SARS-CoV-2 sequences sampled across Italy. Phylogenetic analysis including global sequence data revealed multiple independent introductions into Italy, with at least 124 instances of sequence clusters representing longer chains of transmission. Eighteen of these transmission clusters emerged before the nation-wide lockdown was implemented on 8 March, and an additional 18 had evidence for transmission between different Italian regions. Extended transmission periods between infections of up to 104 days were observed in five clusters. In addition, we found seven clusters that persisted throughout the lockdown period. Overall, we show how importations were an important driver of the first wave of SARS-CoV-2 in Italy.

Genomic epidemiology reveals transmission patterns and dynamics of SARS-CoV-2 in Aotearoa New Zealand.

New Zealand, a geographically remote Pacific island with easily sealable borders, implemented a nationwide 'lockdown' of all non-essential services to curb the spread of COVID-19. Here, we generate 649 SARS-CoV-2 genome sequences from infected patients in New Zealand with samples collected during the 'first wave', representing 56% of all confirmed cases in this time period. Despite its remoteness, the viruses imported into New Zealand represented nearly all of the genomic diversity sequenced from the global virus population. These data helped to quantify the effectiveness of public health interventions. For example, the effective reproductive number, Re of New Zealand's largest cluster decreased from 7 to 0.2 within the first week of lockdown. Similarly, only 19% of virus introductions into New Zealand resulted in ongoing transmission of more than one additional case. Overall, these results demonstrate the utility of genomic pathogen surveillance to inform public health and disease mitigation.

Tracking the COVID-19 pandemic in Australia using genomics.

Genomic sequencing has significant potential to inform public health management for SARS-CoV-2. Here we report high-throughput genomics for SARS-CoV-2, sequencing 80% of cases in Victoria, Australia (population 6.24 million) between 6 January and 14 April 2020 (total 1,333 COVID-19 cases). We integrate epidemiological, genomic and phylodynamic data to identify clusters and impact of interventions. The global diversity of SARS-CoV-2 is represented, consistent with multiple importations. Seventy-six distinct genomic clusters were identified, including large clusters associated with social venues, healthcare and cruise ships. Sequencing sequential samples from 98 patients reveals minimal intra-patient SARS-CoV-2 genomic diversity. Phylodynamic modelling indicates a significant reduction in the effective viral reproductive number (Re) from 1.63 to 0.48 after implementing travel restrictions and physical distancing. Our data provide a concrete framework for the use of SARS-CoV-2 genomics in public health responses, including its use to rapidly identify SARS-CoV-2 transmission chains, increasingly important as social restrictions ease globally.

Temporal signal and the phylodynamic threshold of SARS-CoV-2.

The ongoing SARS-CoV-2 outbreak marks the first time that large amounts of genome sequence data have been generated and made publicly available in near real time. Early analyses of these data revealed low sequence variation, a finding that is consistent with a recently emerging outbreak, but which raises the question of whether such data are sufficiently informative for phylogenetic inferences of evolutionary rates and time scales. The phylodynamic threshold is a key concept that refers to the point in time at which sufficient molecular evolutionary change has accumulated in available genome samples to obtain robust phylodynamic estimates. For example, before the phylodynamic threshold is reached, genomic variation is so low that even large amounts of genome sequences may be insufficient to estimate the virus's evolutionary rate and the time scale of an outbreak. We collected genome sequences of SARS-CoV-2 from public databases at eight different points in time and conducted a range of tests of temporal signal to determine if and when the phylodynamic threshold was reached, and the range of inferences that could be reliably drawn from these data. Our results indicate that by 2 February 2020, estimates of evolutionary rates and time scales had become possible. Analyses of subsequent data sets, that included between 47 and 122 genomes, converged at an evolutionary rate of about 1.1 × 10-3 subs/site/year and a time of origin of around late November 2019. Our study provides guidelines to assess the phylodynamic threshold and demonstrates that establishing this threshold constitutes a fundamental step for understanding the power and limitations of early data in outbreak genome surveillance.