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.

Emergence and rapid transmission of SARS-CoV-2 B.1.1.7 in the United States.

The highly transmissible B.1.1.7 variant of SARS-CoV-2, first identified in the United Kingdom, has gained a foothold across the world. Using S gene target failure (SGTF) and SARS-CoV-2 genomic sequencing, we investigated the prevalence and dynamics of this variant in the United States (US), tracking it back to its early emergence. We found that, while the fraction of B.1.1.7 varied by state, the variant increased at a logistic rate with a roughly weekly doubling rate and an increased transmission of 40%-50%. We revealed several independent introductions of B.1.1.7 into the US as early as late November 2020, with community transmission spreading it to most states within months. We show that the US is on a similar trajectory as other countries where B.1.1.7 became dominant, requiring immediate and decisive action to minimize COVID-19 morbidity and mortality.

Emergence of an early SARS-CoV-2 epidemic in the United States.

The emergence of the COVID-19 epidemic in the United States (U.S.) went largely undetected due to inadequate testing. New Orleans experienced one of the earliest and fastest accelerating outbreaks, coinciding with Mardi Gras. To gain insight into the emergence of SARS-CoV-2 in the U.S. and how large-scale events accelerate transmission, we sequenced SARS-CoV-2 genomes during the first wave of the COVID-19 epidemic in Louisiana. We show that SARS-CoV-2 in Louisiana had limited diversity compared to other U.S. states and that one introduction of SARS-CoV-2 led to almost all of the early transmission in Louisiana. By analyzing mobility and genomic data, we show that SARS-CoV-2 was already present in New Orleans before Mardi Gras, and the festival dramatically accelerated transmission. Our study provides an understanding of how superspreading during large-scale events played a key role during the early outbreak in the U.S. and can greatly accelerate epidemics.

Travel Surveillance and Genomics Uncover a Hidden Zika Outbreak during the Waning Epidemic.

The Zika epidemic in the Americas has challenged surveillance and control. As the epidemic appears to be waning, it is unclear whether transmission is still ongoing, which is exacerbated by discrepancies in reporting. To uncover locations with lingering outbreaks, we investigated travel-associated Zika cases to identify transmission not captured by reporting. We uncovered an unreported outbreak in Cuba during 2017, a year after peak transmission in neighboring islands. By sequencing Zika virus, we show that the establishment of the virus was delayed by a year and that the ensuing outbreak was sparked by long-lived lineages of Zika virus from other Caribbean islands. Our data suggest that, although mosquito control in Cuba may initially have been effective at mitigating Zika virus transmission, such measures need to be maintained to be effective. Our study highlights how Zika virus may still be "silently" spreading and provides a framework for understanding outbreak dynamics. VIDEO ABSTRACT.

Systematic Analysis of Monoclonal Antibodies against Ebola Virus GP Defines Features that Contribute to Protection.

Antibodies are promising post-exposure therapies against emerging viruses, but which antibody features and in vitro assays best forecast protection are unclear. Our international consortium systematically evaluated antibodies against Ebola virus (EBOV) using multidisciplinary assays. For each antibody, we evaluated epitopes recognized on the viral surface glycoprotein (GP) and secreted glycoprotein (sGP), readouts of multiple neutralization assays, fraction of virions left un-neutralized, glycan structures, phagocytic and natural killer cell functions elicited, and in vivo protection in a mouse challenge model. Neutralization and induction of multiple immune effector functions (IEFs) correlated most strongly with protection. Neutralization predominantly occurred via epitopes maintained on endosomally cleaved GP, whereas maximal IEF mapped to epitopes farthest from the viral membrane. Unexpectedly, sGP cross-reactivity did not significantly influence in vivo protection. This comprehensive dataset provides a rubric to evaluate novel antibodies and vaccine responses and a roadmap for therapeutic development for EBOV and related viruses.

Outbreak.info Research Library: a standardized, searchable platform to discover and explore COVID-19 resources.

Outbreak.info Research Library is a standardized, searchable interface of coronavirus disease 2019 (COVID-19) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) publications, clinical trials, datasets, protocols and other resources, built with a reusable framework. We developed a rigorous schema to enforce consistency across different sources and resource types and linked related resources. Researchers can quickly search the latest research across data repositories, regardless of resource type or repository location, via a search interface, public application programming interface (API) and R package.

Outbreak.info genomic reports: scalable and dynamic surveillance of SARS-CoV-2 variants and mutations.

In response to the emergence of SARS-CoV-2 variants of concern, the global scientific community, through unprecedented effort, has sequenced and shared over 11 million genomes through GISAID, as of May 2022. This extraordinarily high sampling rate provides a unique opportunity to track the evolution of the virus in near real-time. Here, we present outbreak.info , a platform that currently tracks over 40 million combinations of Pango lineages and individual mutations, across over 7,000 locations, to provide insights for researchers, public health officials and the general public. We describe the interpretable visualizations available in our web application, the pipelines that enable the scalable ingestion of heterogeneous sources of SARS-CoV-2 variant data and the server infrastructure that enables widespread data dissemination via a high-performance API that can be accessed using an R package. We show how outbreak.info can be used for genomic surveillance and as a hypothesis-generation tool to understand the ongoing pandemic at varying geographic and temporal scales.

Many-core algorithms for high-dimensional gradients on phylogenetic trees.

MOTIVATION: Advancements in high-throughput genomic sequencing are delivering genomic pathogen data at an unprecedented rate, positioning statistical phylogenetics as a critical tool to monitor infectious diseases globally. This rapid growth spurs the need for efficient inference techniques, such as Hamiltonian Monte Carlo (HMC) in a Bayesian framework, to estimate parameters of these phylogenetic models where the dimensions of the parameters increase with the number of sequences N. HMC requires repeated calculation of the gradient of the data log-likelihood with respect to (wrt) all branch-length-specific (BLS) parameters that traditionally takes O(N2) operations using the standard pruning algorithm. A recent study proposes an approach to calculate this gradient in O(N), enabling researchers to take advantage of gradient-based samplers such as HMC. The CPU implementation of this approach makes the calculation of the gradient computationally tractable for nucleotide-based models but falls short in performance for larger state-space size models, such as Markov-modulated and codon models. Here, we describe novel massively parallel algorithms to calculate the gradient of the log-likelihood wrt all BLS parameters that take advantage of graphics processing units (GPUs) and result in many fold higher speedups over previous CPU implementations. RESULTS: We benchmark these GPU algorithms on three computing systems using three evolutionary inference examples exploring complete genomes from 997 dengue viruses, 62 carnivore mitochondria and 49 yeasts, and observe a >128-fold speedup over the CPU implementation for codon-based models and >8-fold speedup for nucleotide-based models. As a practical demonstration, we also estimate the timing of the first introduction of West Nile virus into the continental Unites States under a codon model with a relaxed molecular clock from 104 full viral genomes, an inference task previously intractable. AVAILABILITY AND IMPLEMENTATION: We provide an implementation of our GPU algorithms in BEAGLE v4.0.0 (https://github.com/beagle-dev/beagle-lib), an open-source library for statistical phylogenetics that enables parallel calculations on multi-core CPUs and GPUs. We employ a BEAGLE-implementation using the Bayesian phylogenetics framework BEAST (https://github.com/beast-dev/beast-mcmc).

Ebola Virus Transmission Initiated by Relapse of Systemic Ebola Virus Disease.

During the 2018-2020 Ebola virus disease (EVD) outbreak in North Kivu province in the Democratic Republic of Congo, EVD was diagnosed in a patient who had received the recombinant vesicular stomatitis virus-based vaccine expressing a ZEBOV glycoprotein (rVSV-ZEBOV) (Merck). His treatment included an Ebola virus (EBOV)-specific monoclonal antibody (mAb114), and he recovered within 14 days. However, 6 months later, he presented again with severe EVD-like illness and EBOV viremia, and he died. We initiated epidemiologic and genomic investigations that showed that the patient had had a relapse of acute EVD that led to a transmission chain resulting in 91 cases across six health zones over 4 months. (Funded by the Bill and Melinda Gates Foundation and others.).

Genomic epidemiology reveals multiple introductions of Zika virus into the United States.

Zika virus (ZIKV) is causing an unprecedented epidemic linked to severe congenital abnormalities. In July 2016, mosquito-borne ZIKV transmission was reported in the continental United States; since then, hundreds of locally acquired infections have been reported in Florida. To gain insights into the timing, source, and likely route(s) of ZIKV introduction, we tracked the virus from its first detection in Florida by sequencing ZIKV genomes from infected patients and Aedes aegypti mosquitoes. We show that at least 4 introductions, but potentially as many as 40, contributed to the outbreak in Florida and that local transmission is likely to have started in the spring of 2016-several months before its initial detection. By analysing surveillance and genetic data, we show that ZIKV moved among transmission zones in Miami. Our analyses show that most introductions were linked to the Caribbean, a finding corroborated by the high incidence rates and traffic volumes from the region into the Miami area. Our study provides an understanding of how ZIKV initiates transmission in new regions.

Zika virus evolution and spread in the Americas.

Although the recent Zika virus (ZIKV) epidemic in the Americas and its link to birth defects have attracted a great deal of attention, much remains unknown about ZIKV disease epidemiology and ZIKV evolution, in part owing to a lack of genomic data. Here we address this gap in knowledge by using multiple sequencing approaches to generate 110 ZIKV genomes from clinical and mosquito samples from 10 countries and territories, greatly expanding the observed viral genetic diversity from this outbreak. We analysed the timing and patterns of introductions into distinct geographic regions; our phylogenetic evidence suggests rapid expansion of the outbreak in Brazil and multiple introductions of outbreak strains into Puerto Rico, Honduras, Colombia, other Caribbean islands, and the continental United States. We find that ZIKV circulated undetected in multiple regions for many months before the first locally transmitted cases were confirmed, highlighting the importance of surveillance of viral infections. We identify mutations with possible functional implications for ZIKV biology and pathogenesis, as well as those that might be relevant to the effectiveness of diagnostic tests.

High crossreactivity of human T cell responses between Lassa virus lineages.

Lassa virus infects hundreds of thousands of people each year across rural West Africa, resulting in a high number of cases of Lassa fever (LF), a febrile disease associated with high morbidity and significant mortality. The lack of approved treatments or interventions underscores the need for an effective vaccine. At least four viral lineages circulate in defined regions throughout West Africa with substantial interlineage nucleotide and amino acid diversity. An effective vaccine should be designed to elicit Lassa virus specific humoral and cell mediated immunity across all lineages. Most current vaccine candidates use only lineage IV antigens encoded by Lassa viruses circulating around Sierra Leone, Liberia, and Guinea but not Nigeria where lineages I-III are found. As previous infection is known to protect against disease from subsequent exposure, we sought to determine whether LF survivors from Nigeria and Sierra Leone harbor memory T cells that respond to lineage IV antigens. Our results indicate a high degree of cross-reactivity of CD8+ T cells from Nigerian LF survivors to lineage IV antigens. In addition, we identified regions within the Lassa virus glycoprotein complex and nucleoprotein that contributed to these responses while T cell epitopes were not widely conserved across our study group. These data are important for current efforts to design effective and efficient vaccine candidates that can elicit protective immunity across all Lassa virus lineages.

Identification of Common CD8+ T Cell Epitopes from Lassa Fever Survivors in Nigeria and Sierra Leone.

Early and robust T cell responses have been associated with survival from Lassa fever (LF), but the Lassa virus-specific memory responses have not been well characterized. Regions within the virus surface glycoprotein (GPC) and nucleoprotein (NP) are the main targets of the Lassa virus-specific T cell responses, but, to date, only a few T cell epitopes within these proteins have been identified. We identified GPC and NP regions containing T cell epitopes and HLA haplotypes from LF survivors and used predictive HLA-binding algorithms to identify putative epitopes, which were then experimentally tested using autologous survivor samples. We identified 12 CD8-positive (CD8+) T cell epitopes, including epitopes common to both Nigerian and Sierra Leonean survivors. These data should be useful for the identification of dominant Lassa virus-specific T cell responses in Lassa fever survivors and vaccinated individuals as well as for designing vaccines that elicit cell-mediated immunity.IMPORTANCE The high morbidity and mortality associated with clinical cases of Lassa fever, together with the lack of licensed vaccines and limited and partially effective interventions, make Lassa virus (LASV) an important health concern in its regions of endemicity in West Africa. Previous infection with LASV protects from disease after subsequent exposure, providing a framework for designing vaccines to elicit similar protective immunity. Multiple major lineages of LASV circulate in West Africa, and therefore, ideal vaccine candidates should elicit immunity to all lineages. We therefore sought to identify common T cell epitopes between Lassa fever survivors from Sierra Leone and Nigeria, where distinct lineages circulate. We identified three such epitopes derived from highly conserved regions within LASV proteins. In this process, we also identified nine other T cell epitopes. These data should help in the design of an effective pan-LASV vaccine.

Analysis of CD8+ T cell response during the 2013-2016 Ebola epidemic in West Africa.

The recent Ebola epidemic exemplified the importance of understanding and controlling emerging infections. Despite the importance of T cells in clearing virus during acute infection, little is known about Ebola-specific CD8+ T cell responses. We investigated immune responses of individuals infected with Ebola virus (EBOV) during the 2013-2016 West Africa epidemic in Sierra Leone, where the majority of the >28,000 EBOV disease (EVD) cases occurred. We examined T cell memory responses to seven of the eight Ebola proteins (GP, sGP, NP, VP24, VP30, VP35, and VP40) and associated HLA expression in survivors. Of the 30 subjects included in our analysis, CD8+ T cells from 26 survivors responded to at least one EBOV antigen. A minority, 10 of 26 responders (38%), made CD8+ T cell responses to the viral GP or sGP. In contrast, 25 of the 26 responders (96%) made response to viral NP, 77% to VP24 (20 of 26), 69% to VP40 (18 of 26), 42% (11 of 26) to VP35, with no response to VP30. Individuals making CD8+ T cells to EBOV VP24, VP35, and VP40 also made CD8+ T cells to NP, but rarely to GP. We identified 34 CD8+ T cell epitopes for Ebola. Our data indicate the immunodominance of the EBOV NP-specific T cell response and suggest that its inclusion in a vaccine along with the EBOV GP would best mimic survivor responses and help boost cell-mediated immunity during vaccination.

Branch: an interactive, web-based tool for testing hypotheses and developing predictive models.

UNLABELLED: Branch is a web application that provides users with the ability to interact directly with large biomedical datasets. The interaction is mediated through a collaborative graphical user interface for building and evaluating decision trees. These trees can be used to compose and test sophisticated hypotheses and to develop predictive models. Decision trees are built and evaluated based on a library of imported datasets and can be stored in a collective area for sharing and re-use. AVAILABILITY AND IMPLEMENTATION: Branch is hosted at http://biobranch.org/ and the open source code is available at http://bitbucket.org/sulab/biobranch/ CONTACTS: asu@scripps.edu or bgood@scripps.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Molecular epidemiology of recurrent zoonotic transmission of mpox virus in West Africa.

Nigeria and Cameroon reported their first mpox cases in over three decades in 2017 and 2018 respectively. The outbreak in Nigeria is recognised as an ongoing human epidemic. However, owing to sparse surveillance and genomic data, it is not known whether the increase in cases in Cameroon is driven by zoonotic or sustained human transmission. Notably, the frequency of zoonotic transmission remains unknown in both Cameroon and Nigeria. To address these uncertainties, we investigated the zoonotic transmission dynamics of the mpox virus (MPXV) in Cameroon and Nigeria, with a particular focus on the border regions. We show that in these regions mpox cases are still driven by zoonotic transmission of a newly identified Clade IIb.1. We identify two distinct zoonotic lineages that circulate across the Nigeria-Cameroon border, with evidence of recent and historic cross border dissemination. Our findings support that the complex cross-border forest ecosystems likely hosts shared animal populations that drive cross-border viral spread, which is likely where extant Clade IIb originated. We identify that the closest zoonotic outgroup to the human epidemic circulated in southern Nigeria in October 2013. We also show that the zoonotic precursor lineage circulated in an animal population in southern Nigeria for more than 45 years. This supports findings that southern Nigeria was the origin of the human epidemic. Our study highlights the ongoing MPXV zoonotic transmission in Cameroon and Nigeria, underscoring the continuous risk of MPXV (re)emergence.

Genomic epidemiology uncovers the timing and origin of the emergence of mpox in humans.

Five years before the 2022-2023 global mpox outbreak Nigeria reported its first cases in nearly 40 years, with the ongoing epidemic since driven by sustained human-to-human transmission. However, limited genomic data has left questions about the timing and origin of the mpox virus' (MPXV) emergence. Here we generated 112 MPXV genomes from Nigeria from 2021-2023. We identify the closest zoonotic outgroup to the human epidemic in southern Nigeria, and estimate that the lineage transmitting from human-to-human emerged around July 2014, circulating cryptically until detected in September 2017. The epidemic originated in Southern Nigeria, particularly Rivers State, which also acted as a persistent and dominant source of viral dissemination to other states. We show that APOBEC3 activity increased MPXV's evolutionary rate twenty-fold during human-to-human transmission. We also show how Delphy, a tool for near-real-time Bayesian phylogenetics, can aid rapid outbreak analytics. Our study sheds light on MPXV's establishment in West Africa before the 2022-2023 global outbreak and highlights the need for improved pathogen surveillance and response.

Automatic Differentiation is no Panacea for Phylogenetic Gradient Computation.

Gradients of probabilistic model likelihoods with respect to their parameters are essential for modern computational statistics and machine learning. These calculations are readily available for arbitrary models via "automatic differentiation" implemented in general-purpose machine-learning libraries such as TensorFlow and PyTorch. Although these libraries are highly optimized, it is not clear if their general-purpose nature will limit their algorithmic complexity or implementation speed for the phylogenetic case compared to phylogenetics-specific code. In this paper, we compare six gradient implementations of the phylogenetic likelihood functions, in isolation and also as part of a variational inference procedure. We find that although automatic differentiation can scale approximately linearly in tree size, it is much slower than the carefully implemented gradient calculation for tree likelihood and ratio transformation operations. We conclude that a mixed approach combining phylogenetic libraries with machine learning libraries will provide the optimal combination of speed and model flexibility moving forward.

Many-core algorithms for high-dimensional gradients on phylogenetic trees.

The rapid growth in genomic pathogen data spurs the need for efficient inference techniques, such as Hamiltonian Monte Carlo (HMC) in a Bayesian framework, to estimate parameters of these phylogenetic models where the dimensions of the parameters increase with the number of sequences $N$. HMC requires repeated calculation of the gradient of the data log-likelihood with respect to (wrt) all branch-length-specific (BLS) parameters that traditionally takes $\mathcal{O}(N^2)$ operations using the standard pruning algorithm. A recent study proposes an approach to calculate this gradient in $\mathcal{O}(N)$, enabling researchers to take advantage of gradient-based samplers such as HMC. The CPU implementation of this approach makes the calculation of the gradient computationally tractable for nucleotide-based models but falls short in performance for larger state-space size models, such as codon models. Here, we describe novel massively parallel algorithms to calculate the gradient of the log-likelihood wrt all BLS parameters that take advantage of graphics processing units (GPUs) and result in many fold higher speedups over previous CPU implementations. We benchmark these GPU algorithms on three computing systems using three evolutionary inference examples: carnivores, dengue and yeast, and observe a greater than 128-fold speedup over the CPU implementation for codon-based models and greater than 8-fold speedup for nucleotide-based models. As a practical demonstration, we also estimate the timing of the first introduction of West Nile virus into the continental Unites States under a codon model with a relaxed molecular clock from 104 full viral genomes, an inference task previously intractable. We provide an implementation of our GPU algorithms in BEAGLE v4.0.0, an open source library for statistical phylogenetics that enables parallel calculations on multi-core CPUs and GPUs.

Genetic tracing of market wildlife and viruses at the epicenter of the COVID-19 pandemic.

Zoonotic spillovers of viruses have occurred through the animal trade worldwide. The start of the COVID-19 pandemic was traced epidemiologically to the Huanan Wholesale Seafood Market, the site with the most reported wildlife vendors in the city of Wuhan, China. Here, we analyze publicly available qPCR and sequencing data from environmental samples collected in the Huanan market in early 2020. We demonstrate that the SARS-CoV-2 genetic diversity linked to this market is consistent with market emergence, and find increased SARS-CoV-2 positivity near and within a particular wildlife stall. We identify wildlife DNA in all SARS-CoV-2 positive samples from this stall. This includes species such as civets, bamboo rats, porcupines, hedgehogs, and one species, raccoon dogs, known to be capable of SARS-CoV-2 transmission. We also detect other animal viruses that infect raccoon dogs, civets, and bamboo rats. Combining metagenomic and phylogenetic approaches, we recover genotypes of market animals and compare them to those from other markets. This analysis provides the genetic basis for a short list of potential intermediate hosts of SARS-CoV-2 to prioritize for retrospective serological testing and viral sampling.