Establishment and lineage dynamics of the SARS-CoV-2 epidemic in the UK

Differential protection against SARS-CoV-2 reinfection pre- and post-Omicron

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly evolved over short timescales, leading to the emergence of more transmissible variants such as Alpha and Delta1-3. The arrival of the Omicron variant marked a major shift, introducing numerous extra mutations in the spike gene compared with earlier variants1,2. These evolutionary changes have raised concerns regarding their potential impact on immune evasion, disease severity and the effectiveness of vaccines and treatments1,3. In this epidemiological study, we identified two distinct patterns in the protective effect of natural infection against reinfection in the Omicron versus pre-Omicron eras. Before Omicron, natural infection provided strong and durable protection against reinfection, with minimal waning over time. However, during the Omicron era, protection was robust only for those recently infected, declining rapidly over time and diminishing within a year. These results demonstrate that SARS-CoV-2 immune protection is shaped by a dynamic interaction between host immunity and viral evolution, leading to contrasting reinfection patterns before and after Omicron's first wave. This shift in patterns suggests a change in evolutionary pressures, with intrinsic transmissibility driving adaptation pre-Omicron and immune escape becoming dominant post-Omicron, underscoring the need for periodic vaccine updates to sustain immunity.

Disruption of seasonal influenza circulation and evolution during the 2009 H1N1 and COVID-19 pandemics in Southeastern Asia

East, South, and Southeast Asia (together referred to as Southeastern Asia hereafter) have been recognized as critical areas fuelling the global circulation of seasonal influenza. However, the seasonal influenza migration network within Southeastern Asia remains unclear, including how pandemic-related disruptions altered this network. We leveraged genetic, epidemiological, and airline travel data between 2007-2023 to characterise the dispersal patterns of influenza A/H3N2 and B/Victoria viruses both out of and within Southeastern Asia, including during perturbations by the 2009 A/H1N1 and COVID-19 pandemics. During the COVID-19 pandemic, consistent autumn-winter movement waves from Southeastern Asia to temperate regions were interrupted for both subtype/lineages, however the A/H1N1 pandemic only disrupted A/H3N2 spread. We find a higher persistence of A/H3N2 than B/Victoria circulation in Southeastern Asia and identify distinct pandemic-related disruptions in A/H3N2 antigenic evolution between two pandemics, compared to interpandemic levels; similar patterns are observed in B/Victoria using genetic distance. The internal movement structure within Southeastern Asia markedly diverged during the COVID-19 pandemic season, and to a lesser extent, during the 2009 A/H1N1 pandemic season. Our findings provide insights into the heterogeneous impact of two distinct pandemic-related disruptions on influenza circulation, which can help anticipate the effects of future pandemics and potential mitigation strategies on influenza dynamics.

Global wastewater surveillance for pathogens with pandemic potential: opportunities and challenges

Wastewater surveillance holds great promise as a sensitive method to detect spillover of zoonotic infections and early pandemic emergence, thereby informing risk mitigation and public health response. Known viruses with pandemic potential are shed in human stool or urine, or both, and the experiences with SARS-CoV-2, monkeypox virus, and Zika virus highlight the feasibility of community-based wastewater surveillance for pandemic viruses that have different transmission routes. We reviewed human shedding and wastewater surveillance data for prototype viruses representing viral families of concern to estimate the likely sensitivity of wastewater surveillance compared with that of clinical surveillance. We examined how data on wastewater surveillance detection, together with viral genetic sequences and animal faecal biomarkers, could be used to identify spillover infections or early human transmission and adaptation. The opportunities and challenges associated with global wastewater surveillance for the prevention of pandemics are described in this Personal View, focusing on low-income and middle-income countries, where the risk of pandemic emergence is the highest. We propose a research and public health agenda to ensure an equitable and sustainable solution to these challenges.

Dynamics of SARS-CoV-2 lineages in children and adults in 2021 and 2022

PURPOSE

We aimed to describe SARS-CoV-2 lineages and diversity in children and adults in Estonia and similarity to travel-related cases and neighbouring countries.

METHODS

SARS-CoV-2 sequences in 2021-2022 from a nationwide study were included. The proportion of predominant lineages in Estonian regions and among travel-related cases was described by multinomial logistic regression. Simpson's indices of diversity were compared using linear regression. Dynamics of Bray-Curtis dissimilarity was described by applying fuzzy clustering to non-metrical dimensional scaling results.

RESULTS

A total of 2,630 sequences from children (<15 years) and 23,031 from adults (≥15 years) were included. The increase in the proportion of Alpha/Delta/Omicron BA.1/BA.2 lineages was delayed in smaller regions (by 3.5-27.5 days). The proportion of Alpha/Delta/Omicron BA.1 increased earlier among travel-related (n = 4,654) than non-travel-related cases (10.5 days). Diversity was lower in non-travel-related than travel-related cases until Delta period by 0.066. Dynamics of lineages and diversity were similar in adults and children. Similarity of lineages was delayed compared to Finland during Alpha/Omicron BA.1/BA.2 periods and different from all neighbouring countries during Delta period.

CONCLUSION

SARS-CoV-2 lineages in children and adults were similar. Differences between regions and travel-related cases and varying similarity to neighbouring countries suggest the importance of mobility in the spread.

SARS-CoV-2 introductions to the island of Ireland: a phylogenetic and geospatiotemporal study of infection dynamics

BACKGROUND

Ireland's COVID-19 response combined extensive SARS-CoV-2 testing to estimate incidence, with whole genome sequencing (WGS) for genome surveillance. As an island with two political jurisdictions-Northern Ireland (NI) and Republic of Ireland (RoI)-and access to detailed passenger travel data, Ireland provides a unique setting to study virus introductions and evaluate public health measures. Using a substantial Irish genomic dataset alongside global data from GISAID, this study aimed to trace the introduction and spread of SARS-CoV-2 across the island.

METHODS

We recursively searched for 29,518 SARS-CoV-2 genome sequences collected in Ireland from March 2020 to June 2022 within the global SARS-CoV-2 phylogenetic tree and identified clusters based on shared last common non-Irish ancestors. A maximum parsimony approach was used to assign a likely country of origin to each cluster. The geographic locations and collection dates of the samples in each introduction cluster were used to map the spread of the virus across Ireland. Downsampling was used to model the impact of varying levels of sequencing and normalisation for population permitted comparison between jurisdictions.

RESULTS

Six periods spanning the early introductions and the emergence of Alpha, Delta, and Omicron variants were studied in detail. Among 4439 SARS-CoV-2 introductions to Ireland, 2535 originated in England, with additional cases largely from the rest of Great Britain, United States of America, and Northwestern Europe. Introduction clusters ranged in size from a single to thousands of cases. Introductions were concentrated in the densely populated Dublin and Belfast areas, with many clusters spreading islandwide. Genetic phylogeny was able to effectively trace localised transmission patterns. Introduction rates were similar in NI and RoI for most variants, except for Delta, which was more frequently introduced to NI.

CONCLUSIONS

Tracking individual introduction events enables detailed modelling of virus spread patterns and clearer assessment of the effectiveness of control measures. Stricter travel restrictions in RoI likely reduced Delta introductions but not infection rates, which were similar across jurisdictions. Local and global sequencing levels influence the information available from phylogenomic analyses and we describe an approach to assess the ability of a chosen WGS level to detect virus introductions.

HIPSTR: highest independent posterior subtree reconstruction in TreeAnnotator X

In Bayesian phylogenetic and phylodynamic studies it is common to summarise the posterior distribution of trees with a time-calibrated consensus phylogeny. While the maximum clade credibility (MCC) tree is often used for this purpose, we here show that a novel consensus tree method - the highest independent posterior subtree reconstruction, or HIPSTR - contains consistently higher supported clades over MCC. We also provide faster computational routines for estimating both consensus trees in an updated version of TreeAnnotator X, an open-source software program that summarizes the information from a sample of trees and returns many helpful statistics such as individual clade credibilities contained in the consensus tree. HIPSTR and MCC reconstructions on two Ebola virus and two SARS-CoV-2 data sets show that HIPSTR yields consensus trees that consistently contain clades with higher support compared to MCC trees. The MCC trees regularly fail to include several clades with very high posterior probability (≥ 0.95) as well as a large number of clades with moderate to high posterior probability (≥ 0.50), whereas HIPSTR achieves near-perfect performance in this respect. HIPSTR also exhibits favorable computational performance over MCC in TreeAnnotator X. Comparison to the recently developed CCD0-MAP algorithm yielded mixed results, and requires more in-depth exploration in follow-up studies. TreeAnnotator X - which is part of the BEAST X (v10.5.0) software package - is available at https://github.com/beast-dev/beast-mcmc/releases.

Patterns and drivers of excess mortality during the COVID-19 pandemic in 13 Western European countries

BACKGROUND

Important differences in excess mortality between European countries during the COVID-19 pandemic have been reported. Understanding the drivers of these differences is essential to pandemic preparedness.

METHODS

We examined patterns in age- and sex-standardized cumulative excess mortality in 13 Western European countries during the first 30 months of the COVID-19 pandemic and the correlation of country-level characteristics of interest with excess mortality.

RESULTS

In a timeline analysis, we identified notable differences in seeding events, particularly in early 2020 and when the Alpha variant emerged, likely contributing to notable differences in excess mortality between countries (lowest in Denmark during that period). These differences were more limited from July 2021 onwards. Lower excess mortality was associated with implementing stringent non-pharmaceutical interventions (NPIs) when hospital admissions were still low in 2020 (correlation coefficient rho = 0.65, p = 0.03) and rapid rollout of vaccines in the elderly in early 2021 (rho =  - 0.76, p = 0.002). Countries which implemented NPIs while hospital admissions were low tended to experience lower gross domestic product (GDP) losses in 2020 (rho =  - 0.55, p = 0.08). Structural factors, such as high trust in the national government (rho =  - 0.77, p = 0.002) and low ratio of population at risk of poverty (rho = 0.55, p = 0.05), were also associated with lower excess mortality.

CONCLUSIONS

These results suggest the benefit of early implementation of NPIs and swift rollout of vaccines to the most vulnerable. Further analyses are required at a more granular level to better understand how these factors impacted excess mortality and help guide pandemic preparedness plans.

How fast are viruses spreading in the wild?

Genomic data collected from viral outbreaks can be exploited to reconstruct the dispersal history of viral lineages in a two-dimensional space using continuous phylogeographic inference. These spatially explicit reconstructions can subsequently be used to estimate dispersal metrics that can be informative of the dispersal dynamics and the capacity to spread among hosts. Heterogeneous sampling efforts of genomic sequences can however impact the accuracy of phylogeographic dispersal metrics. While the impact of spatial sampling bias on the outcomes of continuous phylogeographic inference has previously been explored, the impact of sampling intensity (i.e., sampling size) when aiming to characterise dispersal patterns through continuous phylogeographic reconstructions has not yet been thoroughly evaluated. In our study, we use simulations to evaluate the robustness of 3 dispersal metrics - a lineage dispersal velocity, a diffusion coefficient, and an isolation-by-distance (IBD) signal metric - to the sampling intensity. Our results reveal that both the diffusion coefficient and IBD signal metrics appear to be the most robust to the number of samples considered for the phylogeographic reconstruction. We then use these 2 dispersal metrics to compare the dispersal pattern and capacity of various viruses spreading in animal populations. Our comparative analysis reveals a broad range of IBD patterns and diffusion coefficients mostly reflecting the dispersal capacity of the main infected host species but also, in some cases, the likely signature of rapid and/or long-distance dispersal events driven by human-mediated movements through animal trade. Overall, our study provides key recommendations for the use of lineage dispersal metrics to consider in future studies and illustrates their application to compare the spread of viruses in various settings.

A retrospective genomic characterisation of the 2022 mpox outbreak in Belgium, and in vitro assessment of three antiviral compounds

BACKGROUND

Since the beginning of May 2022, cases of mpox have been reported in several European and American countries where the disease is nonendemic. We performed a retrospective genomic characterisation of the 2022 mpox outbreak in Belgium, and assessed the in vitro sensitivity of three antiviral compounds to a monkeypox virus (MPXV) strain from the 2022 outbreak.

METHODS

We sequenced the complete genomes of MPXV isolated from skin-, throat-, anorectal- and genital swab samples using the Oxford Nanopore Technologies (ONT) GridION. We subsequently analysed high-quality complete MPXV genomes and conducted a genomic analysis of MPXV complete genomes from this study with all other complete MPXV genomes available on GISAID up to October 28th, 2022. The in vitro activity of tecovirimat, brincidofovir, and cidofovir was also tested in human and monkey cell lines.

FINDINGS

We produced 248 complete MPXV genomes. Phylogenetic analysis of the complete MPXV genomes revealed that they all belong to MPXV Clade IIb B.1. Surprisingly, through phylogeographic analysis we identified a minimum number of 79 introduction events into Belgium, along with sustained local transmission. We also demonstrated the superior in vitro efficacy and selectivity of tecovirimat to the 2022 MPXV clinical strain.

INTERPRETATION

The number of sequences provides sufficient information about the MPXV lineages that were circulating in Belgium. The 2022 mpox outbreak, in Belgium, was mainly characterised by many introduction events that were promptly contained and resulted in limited human-to-human transmission of MPXV. The in vitro efficacy of antivirals against a 2022 MPXV Belgian strain highlights the potent activity and specificity of tecovirimat and its ability to prevent the formation of the extracellular enveloped viruses.

FUNDING

None.

Genomic analysis of the early COVID-19 pandemic in Haiti reveals Caribbean-specific variant dynamics

Pathogen sequencing during the COVID-19 pandemic has generated more whole genome sequencing data than for any other epidemic, allowing epidemiologists to monitor the transmission and evolution of SARS-CoV-2. However, large parts of the world are heavily underrepresented in sequencing efforts, including the Caribbean islands. We performed genome sequencing of SARS-CoV-2 from upper respiratory tract samples collected in Haiti during the spring of 2020. We used phylogenetic analysis to assess the pandemic dynamics in the Caribbean region and observed that the epidemic in Haiti was seeded by multiple introductions, primarily from the United States. We identified the emergence of a SARS-CoV-2 lineage (B.1.478) from Haiti that spread into North America, as well as evidence of the undocumented spread of SARS-CoV-2 within the Caribbean. We demonstrate that the genomic analysis of a relatively modest number of samples from a severely under-sampled region can provide new insight on a previously unobserved spread of a specific lineage, demonstrating the importance of geographically widespread genomic epidemiology.

Genomic epidemiology of early SARS-CoV-2 transmission dynamics in Bangladesh

BACKGROUND

Genomic epidemiology has helped reconstruct the global and regional movement of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, there is still a lack of understanding of SARS-CoV-2 spread in some of the world's least developed countries (LDCs).

METHODS

To begin to address this disparity, we studied the transmission dynamics of the virus in Bangladesh during the country's first COVID-19 wave by analysing case reports and whole-genome sequences from all eight divisions of the country.

RESULTS

We detected > 50 virus introductions to the country during the period, including during a period of national lockdown. Additionally, through discrete phylogeographic analyses, we identified that geographical distance and population -density and/or -size influenced virus spatial dispersal in Bangladesh.

CONCLUSIONS

Overall, this study expands our knowledge of SARS-CoV-2 genomic epidemiology in Bangladesh, shedding light on crucial transmission characteristics within the country, while also acknowledging resemblances and differences to patterns observed in other nations.

Routes of importation and spatial dynamics of SARS-CoV-2 variants during localized interventions in Chile

Human mobility is strongly associated with the spread of SARS-CoV-2 via air travel on an international scale and with population mixing and the number of people moving between locations on a local scale. However, these conclusions are drawn mostly from observations in the context of the global north where international and domestic connectivity is heavily influenced by the air travel network; scenarios where land-based mobility can also dominate viral spread remain understudied. Furthermore, research on the effects of nonpharmaceutical interventions (NPIs) has mostly focused on national- or regional-scale implementations, leaving gaps in our understanding of the potential benefits of implementing NPIs at higher granularity. Here, we use Chile as a model to explore the role of human mobility on disease spread within the global south; the country implemented a systematic genomic surveillance program and NPIs at a very high spatial granularity. We combine viral genomic data, anonymized human mobility data from mobile phones and official records of international travelers entering the country to characterize the routes of importation of different variants, the relative contributions of airport and land border importations, and the real-time impact of the country's mobility network on the diffusion of SARS-CoV-2. The introduction of variants which are dominant in neighboring countries (and not detected through airport genomic surveillance) is predicted by land border crossings and not by air travelers, and the strength of connectivity between comunas (Chile's lowest administrative divisions) predicts the time of arrival of imported lineages to new locations. A higher stringency of local NPIs was also associated with fewer domestic viral importations. Our analysis sheds light on the drivers of emerging respiratory infectious disease spread outside of air travel and on the consequences of disrupting regular movement patterns at lower spatial scales.

A phylogenetics and variant calling pipeline to support SARS-CoV-2 genomic epidemiology in the UK

In response to the escalating SARS-CoV-2 pandemic, in March 2020 the COVID-19 Genomics UK (COG-UK) consortium was established to enable national-scale genomic surveillance in the UK. By the end of 2020, 49% of all SARS-CoV-2 genome sequences globally had been generated as part of the COG-UK programme, and to date, this system has generated >3 million SARS-CoV-2 genomes. Rapidly and reliably analysing this unprecedented number of genomes was an enormous challenge. To fulfil this need and to inform public health decision-making, we developed a centralized pipeline that performs quality control, alignment, and variant calling and provides the global phylogenetic context of sequences. We present this pipeline and describe how we tailored it as the pandemic progressed to scale with the increasing amounts of data and to provide the most relevant analyses on a daily basis.

Early unrecognised SARS-CoV-2 introductions shaped the first pandemic wave, Sweden, 2020

BackgroundDespite the unprecedented measures implemented globally in early 2020 to prevent the spread of SARS-CoV-2, Sweden, as many other countries, experienced a severe first wave during the COVID-19 pandemic.AimWe investigated the introduction and spread of SARS-CoV-2 into Sweden.MethodsWe analysed stored respiratory specimens (n = 1,979), sampled 7 February-2 April 2020, by PCR for SARS-CoV-2 and sequenced PCR-positive specimens. Sequences generated from newly detected cases and stored positive specimens February-June 2020 (n = 954) were combined with sequences (Sweden: n = 730; other countries: n = 129,913) retrieved from other sources for Nextstrain clade assignment and phylogenetic analyses.ResultsTwelve previously unrecognised SARS-CoV-2 cases were identified: the earliest was sampled on 3 March, 1 week before recognised community transmission. We showed an early influx of clades 20A and 20B from Italy (201/328, 61% of cases exposed abroad) and clades 19A and 20C from Austria (61/328, 19%). Clade 20C dominated the first wave (20C: 908/1,684, 54%; 20B: 438/1,684, 26%; 20A: 263/1,684, 16%), and 800 of 1,684 (48%) Swedish sequences formed a country-specific 20C cluster defined by a spike mutation (G24368T). At the regional level, the proportion of clade 20C sequences correlated with an earlier weighted mean date of COVID-19 deaths.ConclusionCommunity transmission in Sweden started when mitigation efforts still focused on preventing influx. This created a transmission advantage for clade 20C, likely introduced from ongoing cryptic spread in Austria. Therefore, pandemic preparedness should have a comprehensive approach, including capacity for large-scale diagnostics to allow early detection of travel-related cases and community transmission.

Mathematical modelling for pandemic preparedness in Canada: Learning from COVID-19

Background

The COVID-19 pandemic underlined the need for pandemic planning but also brought into focus the use of mathematical modelling to support public health decisions. The types of models needed (compartment, agent-based, importation) are described. Best practices regarding biological realism (including the need for multidisciplinary expert advisors to modellers), model complexity, consideration of uncertainty and communications to decision-makers and the public are outlined.

Methods

A narrative review was developed from the experiences of COVID-19 by members of the Public Health Agency of Canada External Modelling Network for Infectious Diseases (PHAC EMN-ID), a national community of practice on mathematical modelling of infectious diseases for public health.

Results

Modelling can best support pandemic preparedness in two ways: 1) by modelling to support decisions on resource needs for likely future pandemics by estimating numbers of infections, hospitalized cases and cases needing intensive care, associated with epidemics of "hypothetical-yet-plausible" pandemic pathogens in Canada; and 2) by having ready-to-go modelling methods that can be readily adapted to the features of an emerging pandemic pathogen and used for long-range forecasting of the epidemic in Canada, as well as to explore scenarios to support public health decisions on the use of interventions.

Conclusion

There is a need for modelling expertise within public health organizations in Canada, linked to modellers in academia in a community of practice, within which relationships built outside of times of crisis can be applied to enhance modelling during public health emergencies. Key challenges to modelling for pandemic preparedness include the availability of linked public health, hospital and genomic data in Canada.

Clockor2: Inferring Global and Local Strict Molecular Clocks Using Root-to-Tip Regression

Molecular sequence data from rapidly evolving organisms are often sampled at different points in time. Sampling times can then be used for molecular clock calibration. The root-to-tip (RTT) regression is an essential tool to assess the degree to which the data behave in a clock-like fashion. Here, we introduce Clockor2, a client-side web application for conducting RTT regression. Clockor2 allows users to quickly fit local and global molecular clocks, thus handling the increasing complexity of genomic datasets that sample beyond the assumption of homogeneous host populations. Clockor2 is efficient, handling trees of up to the order of 104 tips, with significant speed increases compared with other RTT regression applications. Although clockor2 is written as a web application, all data processing happens on the client-side, meaning that data never leave the user's computer. Clockor2 is freely available at https://clockor2.github.io/.

SARS-CoV-2 surveillance in a hospital and control of an outbreak on a geriatric ward using whole genome sequencing

Background

During the SARS-CoV-2 pandemic, dominant viral variants were repeatedly replaced by new variants with altered properties, frequently changing the dynamics of the infection event, as well as the effectiveness of vaccines and therapeutics. SARS-CoV-2 variant monitoring by whole genome sequencing was established at the University Medical Center Mainz, Germany to support patient management during the pandemic.

Methods

SARS-CoV-2 RNA samples from the University Medical Center were analysed weekly with whole genome sequencing. The genome sequences obtained were aligned with sequences from public databases to perform variant assignment. For classification purposes, phylogenetic trees were constructed to map the variant distribution in the clinical settings and the current outbreak events at that time. We describe the surveillance procedures using an example from a geriatric ward.

Results

For monitoring, a time series was created covering two years of the pandemic. The changes from the Alpha to the Delta and the Omicron variants of SARS-CoV-2 could thus be precisely observed. The increasingly rapid switch of Omicron subvariants in the recent past could be tracked. The elucidation of phylogenetic relationships between circulating strains allowed conclusions about transmission pathways. Using an example from a geriatric ward, we demonstrated how variant monitoring by whole genome sequencing supported the infection prevention and control procedures on a ward and contribute to the control of outbreaks.

Conclusions

This example of SARS-CoV-2 demonstrates the effectiveness of targeted, local monitoring by molecular variant analysis. The program proved to be instrumental in controlling an outbreak on a geriatric ward.

Spatial scales of COVID-19 transmission in Mexico

During outbreaks of emerging infectious diseases, internationally connected cities often experience large and early outbreaks, while rural regions follow after some delay. This hierarchical structure of disease spread is influenced primarily by the multiscale structure of human mobility. However, during the COVID-19 epidemic, public health responses typically did not take into consideration the explicit spatial structure of human mobility when designing nonpharmaceutical interventions (NPIs). NPIs were applied primarily at national or regional scales. Here, we use weekly anonymized and aggregated human mobility data and spatially highly resolved data on COVID-19 cases at the municipality level in Mexico to investigate how behavioral changes in response to the pandemic have altered the spatial scales of transmission and interventions during its first wave (March-June 2020). We find that the epidemic dynamics in Mexico were initially driven by exports of COVID-19 cases from Mexico State and Mexico City, where early outbreaks occurred. The mobility network shifted after the implementation of interventions in late March 2020, and the mobility network communities became more disjointed while epidemics in these communities became increasingly synchronized. Our results provide dynamic insights into how to use network science and epidemiological modeling to inform the spatial scale at which interventions are most impactful in mitigating the spread of COVID-19 and infectious diseases in general.

Inference of epidemic dynamics in the COVID-19 era and beyond

The COVID-19 pandemic demonstrated the key role that epidemiology and modelling play in analysing infectious threats and supporting decision making in real-time. Motivated by the unprecedented volume and breadth of data generated during the pandemic, we review modern opportunities for analysis to address questions that emerge during a major modern epidemic. Following the broad chronology of insights required - from understanding initial dynamics to retrospective evaluation of interventions, we describe the theoretical foundations of each approach and the underlying intuition. Through a series of case studies, we illustrate real life applications, and discuss implications for future work.

High-resolution epidemiological landscape from ~290,000 SARS-CoV-2 genomes from Denmark

Vast amounts of pathogen genomic, demographic and spatial data are transforming our understanding of SARS-CoV-2 emergence and spread. We examined the drivers of molecular evolution and spread of 291,791 SARS-CoV-2 genomes from Denmark in 2021. With a sequencing rate consistently exceeding 60%, and up to 80% of PCR-positive samples between March and November, the viral genome set is broadly whole-epidemic representative. We identify a consistent rise in viral diversity over time, with notable spikes upon the importation of novel variants (e.g., Delta and Omicron). By linking genomic data with rich individual-level demographic data from national registers, we find that individuals aged  < 15 and  > 75 years had a lower contribution to molecular change (i.e., branch lengths) compared to other age groups, but similar molecular evolutionary rates, suggesting a lower likelihood of introducing novel variants. Similarly, we find greater molecular change among vaccinated individuals, suggestive of immune evasion. We also observe evidence of transmission in rural areas to follow predictable diffusion processes. Conversely, urban areas are expectedly more complex due to their high mobility, emphasising the role of population structure in driving virus spread. Our analyses highlight the added value of integrating genomic data with detailed demographic and spatial information, particularly in the absence of structured infection surveys.

Epidemiological inference at the threshold of data availability: an influenza A(H1N2)v spillover event in the United Kingdom

Viruses that infect animals regularly spill over into the human population, but individual events may lead to anything from a single case to a novel pandemic. Rapidly gaining an understanding of a spillover event is critical to calibrating a public health response. We here propose a novel method, using likelihood-free rejection sampling, to evaluate the properties of an outbreak of swine-origin influenza A(H1N2)v in the United Kingdom, detected in November 2023. From the limited data available, we generate historical estimates of the probability that the outbreak had died out in the days following the detection of the first case. Our method suggests that the outbreak could have been said to be over with 95% certainty between 19 and 29 days after the first case was detected, depending upon the probability of a case being detected. We further estimate the number of undetected cases conditional upon the outbreak still being live, the epidemiological parameter R 0, and the date on which the spillover event itself occurred. Our method requires minimal data to be effective. While our calculations were performed after the event, the real-time application of our method has potential value for public health responses to cases of emerging viral infection.

Dispersal history of SARS-CoV-2 variants Alpha, Delta, and Omicron (BA.1) in Spain

Different factors influence the spread of SARS-CoV-2, from the inherent transmission capabilities of the different variants to the control measurements put in place. Here we studied the introduction of the Alpha, Delta, and Omicron-BA.1 variants of concern (VOCs) into Spain. For this, we collected genomic data from the GISAID database and combined it with connectivity data from different countries with Spain to perform a phylodynamic Bayesian analysis of the introductions. Our findings reveal that the introductions of these VOCs predominantly originated from France, especially in the case of Alpha. As travel restrictions were eased during the Delta and Omicron-BA.1 waves, the number of introductions from distinct countries increased, with the United Kingdom and Germany becoming significant sources of the virus. The largest number of introductions detected corresponded to the Delta wave, which was associated with fewer restrictions and the summer period, when Spain receives a considerable number of tourists. This research underscores the importance of monitoring international travel patterns and implementing targeted public health measures to manage the spread of SARS-CoV-2.

Dispersal history of SARS-CoV-2 in Galicia, Spain

The dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission are influenced by a variety of factors, including social restrictions and the emergence of distinct variants. In this study, we delve into the origins and dissemination of the Alpha, Delta, and Omicron-BA.1 variants of concern in Galicia, northwest Spain. For this, we leveraged genomic data collected by the EPICOVIGAL Consortium and from the GISAID database, along with mobility information from other Spanish regions and foreign countries. Our analysis indicates that initial introductions during the Alpha phase were predominantly from other Spanish regions and France. However, as the pandemic progressed, introductions from Portugal and the United States became increasingly significant. The number of detected introductions varied from 96 and 101 for Alpha and Delta to 39 for Omicron-BA.1. Most of these introductions left a low number of descendants (<10), suggesting a limited impact on the evolution of the pandemic in Galicia. Notably, Galicia's major coastal cities emerged as critical hubs for viral transmission, highlighting their role in sustaining and spreading the virus. This research emphasizes the critical role of regional connectivity in the spread of SARS-CoV-2 and offers essential insights for enhancing public health strategies and surveillance measures.

Importations of SARS-CoV-2 lineages decline after nonpharmaceutical interventions in phylogeographic analyses

During the early stages of the SARS-CoV-2 pandemic, before vaccines were available, nonpharmaceutical interventions (NPIs) such as reducing contacts or antigenic testing were used to control viral spread. Quantifying their success is therefore key for future pandemic preparedness. Using 1.8 million SARS-CoV-2 genomes from systematic surveillance, we study viral lineage importations into Germany for the third pandemic wave from late 2020 to early 2021, using large-scale Bayesian phylogenetic and phylogeographic analysis with a longitudinal assessment of lineage importation dynamics over multiple sampling strategies. All major nationwide NPIs were followed by fewer importations, with the strongest decreases seen for free rapid tests, the strengthening of regulations on mask-wearing in public transport and stores, as well as on internal movements and gatherings. Most SARS-CoV-2 lineages first appeared in the three most populous states with most cases, and spread from there within the country. Importations rose before and peaked shortly after the Christmas holidays. The substantial effects of free rapid tests and obligatory medical/surgical mask-wearing suggests these as key for pandemic preparedness, given their relatively few negative socioeconomic effects. The approach relates environmental factors at the host population level to viral lineage dissemination, facilitating similar analyses of rapidly evolving pathogens in the future.

Phylodynamics unveils invading and diffusing patterns of dengue virus serotype-1 in Guangdong, China from 1990 to 2019 under a global genotyping framework

BACKGROUND

The strong invasiveness and rapid expansion of dengue virus (DENV) pose a great challenge to global public health. However, dengue epidemic patterns and mechanisms at a genetic scale, particularly in term of cross-border transmissions, remain poorly understood. Importation is considered as the primary driver of dengue outbreaks in China, and since 1990 a frequent occurrence of large outbreaks has been triggered by the imported cases and subsequently spread to the western and northern parts of China. Therefore, this study aims to systematically reveal the invasion and diffusion patterns of DENV-1 in Guangdong, China from 1990 to 2019.

METHODS

These analyses were performed on 179 newly assembled genomes from indigenous dengue cases in Guangdong, China and 5152 E gene complete sequences recorded in Chinese mainland. The genetic population structure and epidemic patterns of DENV-1 circulating in Chinese mainland were characterized by phylogenetics, phylogeography, phylodynamics based on DENV-1 E-gene-based globally unified genotyping framework.

RESULTS

Multiple serotypes of DENV were co-circulating in Chinese mainland, particularly in Guangdong and Yunnan provinces. A total of 189 transmission clusters in 38 clades belonging to 22 subgenotypes of genotype I, IV and V of DENV-1 were identified, with 7 Clades of Concern (COCs) responsible for the large outbreaks since 1990. The epidemic periodicity was inferred from the data to be approximately 3 years. Dengue transmission events mainly occurred from Great Mekong Subregion-China (GMS-China), Southeast Asia (SEA), South Asia Subcontinent (SASC), and Oceania (OCE) to coastal and land border cities respectively in southeastern and southwestern China. Specially, Guangzhou was found to be the most dominant receipting hub, where DENV-1 diffused to other cities within the province and even other parts of the country. Genome phylogeny combined with epidemiological investigation demonstrated a clear local consecutive transmission process of a 5C1 transmission cluster (5C1-CN4) of DENV-1 in Guangzhou from 2013 to 2015, while the two provinces of Guangdong and Yunnan played key roles in ongoing transition of dengue epidemic patterns. In contextualizing within Invasion Biology theories, we have proposed a derived three-stage model encompassing the stages of invasion, colonization, and dissemination, which is supposed to enhance our understanding of dengue spreading patterns.

CONCLUSIONS

This study demonstrates the invasion and diffusion process of DENV-1 in Chinese mainland within a global genotyping framework, characterizing the genetic diversities of viral populations, multiple sources of importation, and periodic dynamics of the epidemic. These findings highlight the potential ongoing transition trends from epidemic to endemic status offering a valuable insight into early warning, prevention and control of rapid spreading of dengue both in China and worldwide.

How fast are viruses spreading in the wild?

Genomic data collected from viral outbreaks can be exploited to reconstruct the dispersal history of viral lineages in a two-dimensional space using continuous phylogeographic inference. These spatially explicit reconstructions can subsequently be used to estimate dispersal metrics allowing to unveil the dispersal dynamics and evaluate the capacity to spread among hosts. Heterogeneous sampling intensity of genomic sequences can however impact the accuracy of dispersal insights gained through phylogeographic inference. In our study, we implement a simulation framework to evaluate the robustness of three dispersal metrics - a lineage dispersal velocity, a diffusion coefficient, and an isolation-by-distance signal metric - to the sampling effort. Our results reveal that both the diffusion coefficient and isolation-by-distance signal metrics appear to be robust to the number of samples considered for the phylogeographic reconstruction. We then use these two dispersal metrics to compare the dispersal pattern and capacity of various viruses spreading in animal populations. Our comparative analysis reveals a broad range of isolation-by-distance patterns and diffusion coefficients mostly reflecting the dispersal capacity of the main infected host species but also, in some cases, the likely signature of rapid and/or long-distance dispersal events driven by human-mediated movements through animal trade. Overall, our study provides key recommendations for the lineage dispersal metrics to consider in future studies and illustrates their application to compare the spread of viruses in various settings.

Wastewater sequencing as a powerful tool to reveal SARS-CoV-2 variant introduction and spread in French Guiana, South America

The origin of introduction of a new pathogen in a country, the evolutionary dynamics of an epidemic within a country, and the role of cross-border areas on pathogen dynamics remain complex to disentangle and are often poorly understood. For instance, cross-border areas represent the ideal location for the sharing of viral variants between countries, with international air travel, land travel and waterways playing an important role in the cross-border spread of infectious diseases. Unfortunately, monitoring the point of entry and the evolutionary dynamics of viruses in space and time within local populations remain challenging. Here we tested the efficiency of wastewater-based epidemiology and genotyping in monitoring Covid-19 epidemiology and SARS-CoV-2 variant dynamics in French Guiana, a tropical country located in South America. Our results suggest that wastewater-based epidemiology and genotyping are powerful tools to monitor variant introduction and disease evolution within a tropical country but the inclusion of both clinical and wastewater samples could still improve our understanding of genetic diversity co-circulating. Wastewater sequencing also revealed the cryptic transmission of SARS-CoV-2 variants within the country. Interestingly, we found some amino acid changes specific to the variants co-circulating in French Guiana, suggesting a local evolution of the SARS-CoV-2 variants after their introduction. More importantly, our results showed that the proximity to bordering countries was not the origin of the emergence of the French Guianese B.1.160.25 variant, but rather that this variant emerged from an ancestor B.1.160 variant introduced by European air plane travelers, suggesting thus that air travel remains a significant risk for cross-border spread of infectious diseases. Overall, we suggest that wastewater-based epidemiology and genotyping provides a cost effective and non-invasive approach for pathogen monitoring and an early-warning tool for disease emergence and spread within a tropical country.

Lineage frequency time series reveal elevated levels of genetic drift in SARS-CoV-2 transmission in England

Genetic drift in infectious disease transmission results from randomness of transmission and host recovery or death. The strength of genetic drift for SARS-CoV-2 transmission is expected to be high due to high levels of superspreading, and this is expected to substantially impact disease epidemiology and evolution. However, we don't yet have an understanding of how genetic drift changes over time or across locations. Furthermore, noise that results from data collection can potentially confound estimates of genetic drift. To address this challenge, we develop and validate a method to jointly infer genetic drift and measurement noise from time-series lineage frequency data. Our method is highly scalable to increasingly large genomic datasets, which overcomes a limitation in commonly used phylogenetic methods. We apply this method to over 490,000 SARS-CoV-2 genomic sequences from England collected between March 2020 and December 2021 by the COVID-19 Genomics UK (COG-UK) consortium and separately infer the strength of genetic drift for pre-B.1.177, B.1.177, Alpha, and Delta. We find that even after correcting for measurement noise, the strength of genetic drift is consistently, throughout time, higher than that expected from the observed number of COVID-19 positive individuals in England by 1 to 3 orders of magnitude, which cannot be explained by literature values of superspreading. Our estimates of genetic drift suggest low and time-varying establishment probabilities for new mutations, inform the parametrization of SARS-CoV-2 evolutionary models, and motivate future studies of the potential mechanisms for increased stochasticity in this system.

Properties and Mechanisms of Deletions, Insertions, and Substitutions in the Evolutionary History of SARS-CoV-2

SARS-CoV-2 has accumulated many mutations since its emergence in late 2019. Nucleotide substitutions leading to amino acid replacements constitute the primary material for natural selection. Insertions, deletions, and substitutions appear to be critical for coronavirus's macro- and microevolution. Understanding the molecular mechanisms of mutations in the mutational hotspots (positions, loci with recurrent mutations, and nucleotide context) is important for disentangling roles of mutagenesis and selection. In the SARS-CoV-2 genome, deletions and insertions are frequently associated with repetitive sequences, whereas C>U substitutions are often surrounded by nucleotides resembling the APOBEC mutable motifs. We describe various approaches to mutation spectra analyses, including the context features of RNAs that are likely to be involved in the generation of recurrent mutations. We also discuss the interplay between mutations and natural selection as a complex evolutionary trend. The substantial variability and complexity of pipelines for the reconstruction of mutations and the huge number of genomic sequences are major problems for the analyses of mutations in the SARS-CoV-2 genome. As a solution, we advocate for the development of a centralized database of predicted mutations, which needs to be updated on a regular basis.

Drivers and impact of the early silent invasion of SARS-CoV-2 Alpha

SARS-CoV-2 variants of concern (VOCs) circulated cryptically before being identified as a threat, delaying interventions. Here we studied the drivers of such silent spread and its epidemic impact to inform future response planning. We focused on Alpha spread out of the UK. We integrated spatio-temporal records of international mobility, local epidemic growth and genomic surveillance into a Bayesian framework to reconstruct the first three months after Alpha emergence. We found that silent circulation lasted from days to months and decreased with the logarithm of sequencing coverage. Social restrictions in some countries likely delayed the establishment of local transmission, mitigating the negative consequences of late detection. Revisiting the initial spread of Alpha supports local mitigation at the destination in case of emerging events.

Scalable neighbour search and alignment with uvaia

Despite millions of SARS-CoV-2 genomes being sequenced and shared globally, manipulating such data sets is still challenging, especially selecting sequences for focused phylogenetic analysis. We present a novel method, uvaia, which is based on partial and exact sequence similarity for quickly extracting database sequences similar to query sequences of interest. Many SARS-CoV-2 phylogenetic analyses rely on very low numbers of ambiguous sites as a measure of quality since ambiguous sites do not contribute to single nucleotide polymorphism (SNP) differences. Uvaia overcomes this limitation by using measures of sequence similarity which consider partially ambiguous sites, allowing for more ambiguous sequences to be included in the analysis if needed. Such fine-grained definition of similarity allows not only for better phylogenetic analyses, but could also lead to improved classification and biogeographical inferences. Uvaia works natively with compressed files, can use multiple cores and efficiently utilises memory, being able to analyse large data sets on a standard desktop.

Resilience to emerging infectious diseases and the importance of scientific innovation

This opinion piece emphasies the critical role of translational research in enhancing the UK's resilience against future pandemics. The COVID-19 pandemic demonstrated the lifesaving potential of scientific innovation, including genomic tracking of SARS-CoV-2, vaccine development, data linkage, modelling, and new treatments. These advances, achieved through collaborations between academic institutions, industry, government, public health bodies, and the NHS, occurred at an unprecedented pace. However, the UK's pandemic preparedness planning, as reflected in the 2016 Exercise Cygnus report, notably lacked provision for scientific innovation. This oversight highlights the necessity of integrating innovation and research into future preparedness strategies, not as a luxury but as a vital component of the healthcare infrastructure. The COVID-19 pandemic has underlined the importance of surge capacity for diagnostic labs, vaccine development and deployment strategies, real-time research embedded within the NHS, efficient data sharing, clear public communication, and the use of genomic tools for outbreak surveillance and monitoring pathogen response. Despite world-leading aspects of some of the UK's research response, the need to build much of the infrastructure in real-time led to avoidable delays. A proactive approach in incorporating research and innovation into the NHS's operational framework will be needed to ensure swift, evidence-based responses to future pandemics.

Using early detection data to estimate the date of emergence of an epidemic outbreak

While the first infection of an emerging disease is often unknown, information on early cases can be used to date it. In the context of the COVID-19 pandemic, previous studies have estimated dates of emergence (e.g., first human SARS-CoV-2 infection, emergence of the Alpha SARS-CoV-2 variant) using mainly genomic data. Another dating attempt used a stochastic population dynamics approach and the date of the first reported case. Here, we extend this approach to use a larger set of early reported cases to estimate the delay from first infection to the Nth case. We first validate our framework by running our model on simulated data. We then apply our model using data on Alpha variant infections in the UK, dating the first Alpha infection at (median) August 21, 2020 (95% interpercentile range across retained simulations (IPR): July 23-September 5, 2020). Next, we apply our model to data on COVID-19 cases with symptom onset before mid-January 2020. We date the first SARS-CoV-2 infection in Wuhan at (median) November 28, 2019 (95% IPR: November 2-December 9, 2019). Our results fall within ranges previously estimated by studies relying on genomic data. Our population dynamics-based modelling framework is generic and flexible, and thus can be applied to estimate the starting time of outbreaks in contexts other than COVID-19.

Whole genome sequencing and phylogenetic analysis of SARS-CoV-2 strains isolated during the COVID-19 pandemic in Nigeria

Objectives

The emergence and spread of SARS-CoV-2 have stimulated ongoing research into the virus transmission dynamics, circulating variants, and potential mutations. This study was conducted to understand the genomic dynamics of the epidemic in Nigeria.

Design

Whole genome sequencing was conducted on SARS-CoV-2 samples collected during the first and second outbreaks using the Oxford Nanopore MinION sequencing platform. Phylogenetic analysis was conducted, and genomes were grouped into different pangolin lineages.

Results

The study revealed four circulating SARS-CoV-2 variants. The Alpha (B.1.1.7) variant was the most prevalent (32.7%), followed by Beta (B.1 B.1.1, L.3, and B.1.1.318) (30.8%), Eta (B.1.525) (28.9%), and Delta (B.1.617, AY.1, AY.109, and AY.36) (7.7%). Phylogenetic analysis revealed three clusters with four Nextstrain clades (20I, 20B, 21D, and 21J). The Alpha lineages (B.1.1.7) clustered with references from Italy. The Beta lineages (Clade 20B) (B.11, B.11318, and L3) and sub-lineage B.11 were distinct. Sub-lineage B.11318 is clustered with references from the USA, whereas sub-lineage L3 is clustered with references from Russia, the Philippines, Australia, and Japan. The 21D and 21J, belonging to two Pango lineages, Eta (B.1525) and Delta (B.1.617 and AY.109), showed high genetic similarity.

Conclusion

The phylogenetic relatedness of the lineages suggests multiple virus introduction, which could be a source of more virulent, locally adapted variants.

Dispersal history of SARS-CoV-2 in Galicia, Spain

The dynamics of SARS-CoV-2 transmission are influenced by a variety of factors, including social restrictions and the emergence of distinct variants. In this study, we delve into the origins and dissemination of the Alpha, Delta, and Omicron variants of concern in Galicia, northwest Spain. For this, we leveraged genomic data collected by the EPICOVIGAL Consortium and from the GISAID database, along with mobility information from other Spanish regions and foreign countries. Our analysis indicates that initial introductions during the Alpha phase were predominantly from other Spanish regions and France. However, as the pandemic progressed, introductions from Portugal and the USA became increasingly significant. Notably, Galicia's major coastal cities emerged as critical hubs for viral transmission, highlighting their role in sustaining and spreading the virus. This research emphasizes the critical role of regional connectivity in the spread of SARS-CoV-2 and offers essential insights for enhancing public health strategies and surveillance measures.

B cell phylogenetics in the single cell era

The widespread availability of single-cell RNA sequencing (scRNA-seq) has led to the development of new methods for understanding immune responses. Single-cell transcriptome data can now be paired with B cell receptor (BCR) sequences. However, RNA from BCRs cannot be analyzed like most other genes because BCRs are genetically diverse within individuals. In humans, BCRs are shaped through recombination followed by mutation and selection for antigen binding. As these processes co-occur with cell division, B cells can be studied using phylogenetic trees representing the mutations within a clone. B cell trees can link experimental timepoints, tissues, or cellular subtypes. Here, we review the current state and potential of how B cell phylogenetics can be combined with single-cell data to understand immune responses.

Approaches and challenges to inferring the geographical source of infectious disease outbreaks using genomic data

Genomic data hold increasing potential in the elucidation of transmission dynamics and geographical sources of infectious disease outbreaks. Phylogeographic methods that use epidemiological and genomic data obtained from surveillance enable us to infer the history of spatial transmission that is naturally embedded in the topology of phylogenetic trees as a record of the dispersal of infectious agents between geographical locations. In this Review, we provide an overview of phylogeographic approaches widely used for reconstructing the geographical sources of outbreaks of interest. These approaches can be classified into ancestral trait or state reconstruction and structured population models, with structured population models including popular structured coalescent and birth-death models. We also describe the major challenges associated with sequencing technologies, surveillance strategies, data sharing, and analysis frameworks that became apparent during the generation of large-scale genomic data in recent years, extending beyond inference approaches. Finally, we highlight the role of genomic data in geographical source inference and clarify how this enhances understanding and molecular investigations of outbreak sources.

Genomic surveillance reveals dynamic shifts in the connectivity of COVID-19 epidemics

The maturation of genomic surveillance in the past decade has enabled tracking of the emergence and spread of epidemics at an unprecedented level. During the COVID-19 pandemic, for example, genomic data revealed that local epidemics varied considerably in the frequency of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lineage importation and persistence, likely due to a combination of COVID-19 restrictions and changing connectivity. Here, we show that local COVID-19 epidemics are driven by regional transmission, including across international boundaries, but can become increasingly connected to distant locations following the relaxation of public health interventions. By integrating genomic, mobility, and epidemiological data, we find abundant transmission occurring between both adjacent and distant locations, supported by dynamic mobility patterns. We find that changing connectivity significantly influences local COVID-19 incidence. Our findings demonstrate a complex meaning of "local" when investigating connected epidemics and emphasize the importance of collaborative interventions for pandemic prevention and mitigation.

Evolutionary and spatiotemporal analyses reveal multiple introductions and cryptic transmission of SARS-CoV-2 VOC/VOI in Malta

IMPORTANCE

Our study provides insights into the evolution of the coronavirus disease 2019 (COVID-19) pandemic in Malta, a highly connected and understudied country. We combined epidemiological and phylodynamic analyses to analyze trends in the number of new cases, deaths, tests, positivity rates, and evolutionary and dispersal patterns from August 2020 to January 2022. Our reconstructions inferred 173 independent severe acute respiratory syndrome coronavirus 2 introductions into Malta from various global regions. Our study demonstrates that characterizing epidemiological trends coupled with phylodynamic modeling can inform the implementation of public health interventions to help control COVID-19 transmission in the community.

Rural populations facilitated early SARS-CoV-2 evolution and transmission in Missouri, USA

In the United States, rural populations comprise 60 million individuals and suffered from high COVID-19 disease burdens. Despite this, surveillance efforts are biased toward urban centers. Consequently, how rurally circulating SARS-CoV-2 viruses contribute toward emerging variants remains poorly understood. In this study, we aim to investigate the role of rural communities in the evolution and transmission of SARS-CoV-2 during the early pandemic. We collected 544 urban and 435 rural COVID-19-positive respiratory specimens from an overall vaccine-naïve population in Southwest Missouri between July and December 2020. Genomic analyses revealed 53 SARS-CoV-2 Pango lineages in our study samples, with 14 of these lineages identified only in rural samples. Phylodynamic analyses showed that frequent bi-directional diffusions occurred between rural and urban communities in Southwest Missouri, and that four out of seven Missouri rural-origin lineages spread globally. Further analyses revealed that the nucleocapsid protein (N):R203K/G204R paired substitutions, which were detected disproportionately across multiple Pango lineages, were more associated with urban than rural sequences. Positive selection was detected at N:204 among rural samples but was not evident in urban samples, suggesting that viruses may encounter distinct selection pressures in rural versus urban communities. This study demonstrates that rural communities may be a crucial source of SARS-CoV-2 evolution and transmission, highlighting the need to expand surveillance and resources to rural populations for COVID-19 mitigation.

Reconstructing relative transmission rates in Bayesian phylodynamics: Two-fold transmission advantage of Omicron in Berlin, Germany during December 2021

Phylodynamic methods have lately played a key role in understanding the spread of infectious diseases. During the coronavirus disease (COVID-19) pandemic, large scale genomic surveillance has further increased the potential of dynamic inference from viral genomes. With the continual emergence of novel severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) variants, explicitly allowing transmission rate differences between simultaneously circulating variants in phylodynamic inference is crucial. In this study, we present and empirically validate an extension to the BEAST2 package birth-death skyline model (BDSKY), BDSKY[Formula: see text], which introduces a scaling factor for the transmission rate between independent, jointly inferred trees. In an extensive simulation study, we show that BDSKY[Formula: see text] robustly infers the relative transmission rates under different epidemic scenarios. Using publicly available genome data of SARS-CoV-2, we apply BDSKY[Formula: see text] to quantify the transmission advantage of the Omicron over the Delta variant in Berlin, Germany. We find the overall transmission rate of Omicron to be scaled by a factor of two with pronounced variation between the individual clusters of each variant. These results quantify the transmission advantage of Omicron over the previously circulating Delta variant, in a crucial period of pre-established non-pharmaceutical interventions. By inferring variant- as well as cluster-specific transmission rate scaling factors, we show the differences in transmission dynamics for each variant. This highlights the importance of incorporating lineage-specific transmission differences in phylodynamic inference.

Open questions in the social lives of viruses

Social interactions among viruses occur whenever multiple viral genomes infect the same cells, hosts, or populations of hosts. Viral social interactions range from cooperation to conflict, occur throughout the viral world, and affect every stage of the viral lifecycle. The ubiquity of these social interactions means that they can determine the population dynamics, evolutionary trajectory, and clinical progression of viral infections. At the same time, social interactions in viruses raise new questions for evolutionary theory, providing opportunities to test and extend existing frameworks within social evolution. Many opportunities exist at this interface: Insights into the evolution of viral social interactions have immediate implications for our understanding of the fundamental biology and clinical manifestation of viral diseases. However, these opportunities are currently limited because evolutionary biologists only rarely study social evolution in viruses. Here, we bridge this gap by (1) summarizing the ways in which viruses can interact socially, including consequences for social evolution and evolvability; (2) outlining some open questions raised by viruses that could challenge concepts within social evolution theory; and (3) providing some illustrative examples, data sources, and conceptual questions, for studying the natural history of social viruses.

SCORPIO: a utility for defining and classifying mutation constellations of virus genomes

SUMMARY

Scorpio provides a set of command line utilities for classifying, haplotyping, and defining constellations of mutations for an aligned set of genome sequences. It was developed to enable exploration and classification of variants of concern within the SARS-CoV-2 pandemic, but can be applied more generally to other species.

AVAILABILITY AND IMPLEMENTATION

Scorpio is an open-source project distributed under the GNU GPL version 3 license. Source code and binaries are available at https://github.com/cov-lineages/scorpio, and binaries are also available from Bioconda. SARS-CoV-2 specific definitions can be installed as a separate dependency from https://github.com/cov-lineages/constellations.

Multiple thermocycles followed by LAMP with only two primers for ultrasensitive colorimetric viral RNA testing and tracking at single-base resolution

Rapid, accurate and high throughput measurement of infectious viruses is an urgent need to prevent viral transmission. Loop-mediated isothermal amplification (LAMP) is an attractive isothermal amplification method for nucleic acid detection, especially for point-of-care (POC) testing, but it needs at least four primers and its sensitivity is also limited when integrating with visual detection methods. Herein, by designing only two primers to precisely recognize the four regions of the target, we developed a multiple thermocycles-based LAMP method (MTC-LAMP) for sensitive and specific testing and tracking of viral RNA. We also introduced a novel SYBR Green I (SG)-assisted stable colorimetric assay induced by the amplification products through the charge neutralization effect of positively charged SG toward gold nanoparticles (AuNPs). The ultralow nonspecific background of the double exponential amplification improved the detection sensitivity to near single-molecule level (1 aM, 3 copies in 5 μL solution), which was higher than RT-PCR and RT-LAMP. After adding AuNPs, a significant color difference between target and blank was immediately observed by naked eye. By introducing a peptide nucleic acid (PNA) clamp into our colorimetric MTC-LAMP assay, the specific distinguish of virus variants at single-base resolution was observed without the requirement of any equipment. This assay shows great potential for large-scale screening and tracking of the threatening viruses with ultrahigh sensitivity and pronounced colorimetric output, which is of great importance for pandemic control.

Fitness, growth and transmissibility of SARS-CoV-2 genetic variants

The massive scale of the global SARS-CoV-2 sequencing effort created new opportunities and challenges for understanding SARS-CoV-2 evolution. Rapid detection and assessment of new variants has become one of the principal objectives of genomic surveillance of SARS-CoV-2. Because of the pace and scale of sequencing, new strategies have been developed for characterizing fitness and transmissibility of emerging variants. In this Review, I discuss a wide range of approaches that have been rapidly developed in response to the public health threat posed by emerging variants, ranging from new applications of classic population genetics models to contemporary synthesis of epidemiological models and phylodynamic analysis. Many of these approaches can be adapted to other pathogens and will have increasing relevance as large-scale pathogen sequencing becomes a regular feature of many public health systems.

Representativeness of whole-genome sequencing approaches in England: the importance for understanding inequalities associated with SARS-CoV-2 infection

Whole-genome sequencing (WGS) information has played a crucial role in the SARS-CoV-2 (COVID-19) pandemic by providing evidence about variants to inform public health policy. The purpose of this study was to assess the representativeness of sequenced cases compared with all COVID-19 cases in England, between March 2020 and August 2021, by demographic and socio-economic characteristics, to evaluate the representativeness and utility of these data in epidemiological analyses. To achieve this, polymerase chain reaction (PCR)-confirmed COVID-19 cases were extracted from the national laboratory system and linked with WGS data. During the study period, over 10% of COVID-19 cases in England had WGS data available for epidemiological analysis. With sequencing capacity increasing throughout the period, sequencing representativeness compared to all reported COVID-19 cases increased over time, allowing for valuable epidemiological analyses using demographic and socio-economic characteristics, particularly during periods with emerging novel SARS-CoV-2 variants. This study demonstrates the comprehensiveness of England's sequencing throughout the COVID-19 pandemic, rapidly detecting variants of concern, and enabling representative epidemiological analyses to inform policy.

Accelerated evolution of SARS-CoV-2 in free-ranging white-tailed deer

The zoonotic origin of the COVID-19 pandemic virus highlights the need to fill the vast gaps in our knowledge of SARS-CoV-2 ecology and evolution in non-human hosts. Here, we detected that SARS-CoV-2 was introduced from humans into white-tailed deer more than 30 times in Ohio, USA during November 2021-March 2022. Subsequently, deer-to-deer transmission persisted for 2-8 months, disseminating across hundreds of kilometers. Newly developed Bayesian phylogenetic methods quantified how SARS-CoV-2 evolution is not only three-times faster in white-tailed deer compared to the rate observed in humans but also driven by different mutational biases and selection pressures. The long-term effect of this accelerated evolutionary rate remains to be seen as no critical phenotypic changes were observed in our animal models using white-tailed deer origin viruses. Still, SARS-CoV-2 has transmitted in white-tailed deer populations for a relatively short duration, and the risk of future changes may have serious consequences for humans and livestock.

Association of vaccination, international travel, public health and social measures with lineage dynamics of SARS-CoV-2

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.