MicrobeTrace: Retooling molecular epidemiology for rapid public health response

Threshold sensitivity analysis for HIV-1 transmission cluster detection using different genomic regions and subtypes

HIV-1 cluster analysis has been widely used in characterizing HIV-1 transmission and some countries have implemented such molecular epidemiology as part of their prevention strategy. However, HIV-1 sequences derive from varying genome regions, which affects phylogenetic clustering outputs. Here, we apply different tools to run a sensitivity analysis for assessing which threshold give the most cohesive clustering outputs for different data sources. We used a dataset of 174 full-length sequences of subtype B from the Swiss HIV Cohort Study and publicly available subtype C from South Africa. Each dataset was divided into sub-genomic sub-datasets covering gag, pol, and env. pol was further subdivided into regions commonly used in HIV-1 genotyping laboratories (pr-rt, rt-int, and pr-rt-int). Cluster analyses for each sub-genomic region was performed specifying varying distance thresholds of 0.5 %-4.5 % and tree branch support of 70 %, 90 % and 99 % in ClusterPicker. Tree topologies and clustering outputs were compared against each other to assess cluster similarity. Pylogenies using pol, pr-rt-int, or rt-int had more robust tree topologies compared to gag and env. Cluster composition changed with increasing genetic distance threshold but was not affected by branch support. Cluster identity was most similar around genetic distances of 2.5 (±0.5)% for all sub-genomic regions and for both subtype B and C. Our study demonstrated the value of performing a sensitivity analysis before setting a genetic distance threshold for clustering output and that the pol region is appropriate for clustering outputs and can be used for near real-time HIV-1 cluster detection.

Strategies and Opportunities to Improve Community Health through Advanced Molecular Detection and Genomic Surveillance of Infectious Diseases

Advanced molecular detection (AMD) refers to the integration of next-generation sequencing, epidemiologic, and bioinformatics data to drive public health actions. As new AMD technologies emerge, it is critical to ensure those methods are used in communities that are most affected by disease-induced illness and death. We describe strategies and opportunities for using AMD approaches to improve health in those communities, which include improving access to pathogen sequencing, increasing data linkages, and using pathogen sequencing for those diseases where sequencing technologies can provide the best health outcome. Such strategies can help address and prevent differences in health outcomes in various populations, such as rural and tribal communities, persons with underlying health issues, and other populations that experience higher risks for infectious disease.

Looking back into the Hepatitis C Virus epidemic dynamics from Unnao, India through phylogenetic approach

Viral hepatitis is a major public health challenge. Hepatitis C Virus (HCV) infection causes the progressive liver damage. A surprisingly high number of individuals tested positive for HCV infection during the Unnao Human Immunodeficiency Virus (HIV) outbreak investigation in 2017-2018 (more than 90% of the people living with HIV were from the Premganj township and Chakmeerapur village of the district in the northern State of Uttar Pradesh). This particular outbreak was attributed to the unsafe use of syringe & needles while seeking treatment, rendering it as an iatrogenic transmission. Earlier investigation towards, phylogenetic characterization revealed the shared ancestry for HCV sequences from the reported outbreak. In this investigation using the HCV sequences reported earlier (n = 67) we have analyzed the transmission linkages and evolutionary dynamics of this HCV outbreak using phylogenetic methods. In current analysis, time-scaled phylogenies indicated that most clusters initiated during 2015-2016. Transmission dynamics highlighted that the outbreak was experiencing an exponential phase during 2016-2017, where reproductive number was observed to be beyond documented values for HCV. Phylogeography revealed that source of the virus was Chakmeerapur and the effective population of Premganj was double the size of that in Chakmeerapur. In summary, we provide the snapshot of epidemic dynamics for HCV outbreak attributed to iatrogenic transmission.

Investigating and forecasting infectious disease dynamics using epidemiological and molecular surveillance data

The integration of viral genomic data into public health surveillance has revolutionized our ability to track and forecast infectious disease dynamics. This review addresses two critical aspects of infectious disease forecasting and monitoring: the methodological workflow for epidemic forecasting and the transformative role of molecular surveillance. We first present a detailed approach for validating epidemic models, emphasizing an iterative workflow that utilizes ordinary differential equation (ODE)-based models to investigate and forecast disease dynamics. We recommend a more structured approach to model validation, systematically addressing key stages such as model calibration, assessment of structural and practical parameter identifiability, and effective uncertainty propagation in forecasts. Furthermore, we underscore the importance of incorporating multiple data streams by applying both simulated and real epidemiological data from the COVID-19 pandemic to produce more reliable forecasts with quantified uncertainty. Additionally, we emphasize the pivotal role of viral genomic data in tracking transmission dynamics and pathogen evolution. By leveraging advanced computational tools such as Bayesian phylogenetics and phylodynamics, researchers can more accurately estimate transmission clusters and reconstruct outbreak histories, thereby improving data-driven modeling and forecasting and informing targeted public health interventions. Finally, we discuss the transformative potential of integrating molecular epidemiology with mathematical modeling to complement and enhance epidemic forecasting and optimize public health strategies.

Dimensionality reduction distills complex evolutionary relationships in seasonal influenza and SARS-CoV-2

Public health researchers and practitioners commonly infer phylogenies from viral genome sequences to understand transmission dynamics and identify clusters of genetically-related samples. However, viruses that reassort or recombine violate phylogenetic assumptions and require more sophisticated methods. Even when phylogenies are appropriate, they can be unnecessary or difficult to interpret without specialty knowledge. For example, pairwise distances between sequences can be enough to identify clusters of related samples or assign new samples to existing phylogenetic clusters. In this work, we tested whether dimensionality reduction methods could capture known genetic groups within two human pathogenic viruses that cause substantial human morbidity and mortality and frequently reassort or recombine, respectively: seasonal influenza A/H3N2 and SARS-CoV-2. We applied principal component analysis, multidimensional scaling (MDS), t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and projection to sequences with well-defined phylogenetic clades and either reassortment (H3N2) or recombination (SARS-CoV-2). For each low-dimensional embedding of sequences, we calculated the correlation between pairwise genetic and Euclidean distances in the embedding and applied a hierarchical clustering method to identify clusters in the embedding. We measured the accuracy of clusters compared to previously defined phylogenetic clades, reassortment clusters, or recombinant lineages. We found that MDS embeddings accurately represented pairwise genetic distances including the intermediate placement of recombinant SARS-CoV-2 lineages between parental lineages. Clusters from t-SNE embeddings accurately recapitulated known phylogenetic clades, H3N2 reassortment groups, and SARS-CoV-2 recombinant lineages. We show that simple statistical methods without a biological model can accurately represent known genetic relationships for relevant human pathogenic viruses. Our open source implementation of these methods for analysis of viral genome sequences can be easily applied when phylogenetic methods are either unnecessary or inappropriate.

Phylogenetic-informed graph deep learning to classify dynamic transmission clusters in infectious disease epidemics

Motivation

In the midst of an outbreak, identification of groups of individuals that represent risk for transmission of the pathogen under investigation is critical to public health efforts. Dynamic transmission patterns within these clusters, whether it be the result of changes at the level of the virus (e.g. infectivity) or host (e.g. vaccination), are critical in strategizing public health interventions, particularly when resources are limited. Phylogenetic trees are widely used not only in the detection of transmission clusters, but the topological shape of the branches within can be useful sources of information regarding the dynamics of the represented population.

Results

We evaluated the limitation of existing tree shape metrics when dealing with dynamic transmission clusters and propose instead a phylogeny-based deep learning system -DeepDynaTree- for dynamic classification. Comprehensive experiments carried out on a variety of simulated epidemic growth models and HIV epidemic data indicate that this graph deep learning approach is effective, robust, and informative for cluster dynamic prediction. Our results confirm that DeepDynaTree is a promising tool for transmission cluster characterization that can be modified to address the existing limitations and deficiencies in knowledge regarding the dynamics of transmission trajectories for groups at risk of pathogen infection.

Availability and implementation

DeepDynaTree is available under an MIT Licence in https://github.com/salemilab/DeepDynaTree.

Novel insights on unraveling dynamics of transmission clusters in outbreaks using phylogeny-based methods

Molecular data analysis is invaluable in understanding the overall behavior of a rapidly spreading virus population when epidemiological surveillance is problematic. It is also particularly beneficial in describing subgroups within the population, often identified as clades within a phylogenetic tree that represent individuals connected via direct transmission or transmission via differing risk factors in viral spread. However, transmission patterns or viral dynamics within these smaller groups should not be expected to exhibit homogeneous behavior over time. As such, standard phylogenetic approaches that identify clusters based on summary statistics would not be expected to capture dynamic clusters of transmission. We, therefore, sought to evaluate the performance of existing and adapted phylogeny-based cluster identification tools on simulated transmission clusters exhibiting dynamic transmission behavior over time. Despite the complementarity of the tools, we provide strong evidence that novel cluster identification methods are needed for reliable detection of epidemiologically linked individuals, particularly those exhibiting changing transmission dynamics during dynamic outbreak scenarios.

Dimensionality reduction distills complex evolutionary relationships in seasonal influenza and SARS-CoV-2

Public health researchers and practitioners commonly infer phylogenies from viral genome sequences to understand transmission dynamics and identify clusters of genetically-related samples. However, viruses that reassort or recombine violate phylogenetic assumptions and require more sophisticated methods. Even when phylogenies are appropriate, they can be unnecessary or difficult to interpret without specialty knowledge. For example, pairwise distances between sequences can be enough to identify clusters of related samples or assign new samples to existing phylogenetic clusters. In this work, we tested whether dimensionality reduction methods could capture known genetic groups within two human pathogenic viruses that cause substantial human morbidity and mortality and frequently reassort or recombine, respectively: seasonal influenza A/H3N2 and SARS-CoV-2. We applied principal component analysis (PCA), multidimensional scaling (MDS), t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and projection (UMAP) to sequences with well-defined phylogenetic clades and either reassortment (H3N2) or recombination (SARS-CoV-2). For each low-dimensional embedding of sequences, we calculated the correlation between pairwise genetic and Euclidean distances in the embedding and applied a hierarchical clustering method to identify clusters in the embedding. We measured the accuracy of clusters compared to previously defined phylogenetic clades, reassortment clusters, or recombinant lineages. We found that MDS embeddings accurately represented pairwise genetic distances including the intermediate placement of recombinant SARS-CoV-2 lineages between parental lineages. Clusters from t-SNE embeddings accurately recapitulated known phylogenetic clades, H3N2 reassortment groups, and SARS-CoV-2 recombinant lineages. We show that simple statistical methods without a biological model can accurately represent known genetic relationships for relevant human pathogenic viruses. Our open source implementation of these methods for analysis of viral genome sequences can be easily applied when phylogenetic methods are either unnecessary or inappropriate.

Phylogenetic Network Analyses Reveal the Influence of Transmission Clustering on the Spread of HIV Drug Resistance in Quebec from 2002 to 2022

BACKGROUND

HIV drug resistance (HIV-DR) may jeopardize the benefit of antiretroviral therapy (ART) in treatment and prevention. This study utilized viral phylogenetics to resolve the influence of transmission networks on sustaining the spread of HIV-DR in Quebec spanning 2002 to 2022.

METHODS

Time trends in acquired (ADR) and transmitted drug resistance (TDR) were delineated in treatment-experienced (n = 3500) and ART-naïve persons (n = 6011) with subtype B infections. Similarly, non-B-subtype HIV-DR networks were assessed pre- (n = 1577) and post-ART experience (n = 488). Risks of acquisition of resistance-associated mutations (RAMs) were related to clustering using 1, 2-5, vs. 6+ members per cluster as categorical variables.

RESULTS

Despite steady declines in treatment failure and ADR since 2007, rates of TDR among newly infected, ART-naive persons remained at 14% spanning the 2007-2011, 2012-2016, and 2017-2022 periods. Notably, half of new infections among men having sex with men and heterosexual groups were linked in large, clustered networks having a median of 35 (14-73 IQR) and 16 (9-26 IQR) members per cluster, respectively. Cluster membership and size were implicated in forward transmission of non-nucleoside reverse transcriptase inhibitor NNRTI RAMs (9%) and thymidine analogue mutations (TAMs) (5%). In contrast, transmission of M184V, K65R, and integrase inhibitors (1-2%) remained rare. Levels of TDR reflected viral replicative fitness. The median baseline viremia in ART-naïve groups having no RAMs, NNRTI RAMs, TAMs, and M184VI were 46.088, 38,447, 20,330, and 6811 copies/mL, respectively (p < 0.0001).

CONCLUSION

Phylogenetics emphasize the need to prioritize ART and pre-exposure prophylaxis strategies to avert the expansion of transmission cascades of HIV-DR.

HIV Immunocapture Reveals Particles Expressed in Semen Under Integrase Strand Transfer Inhibitor-Based Therapy Are Largely Myeloid Cell-Derived and Disparate

As use of human immunodeficiency virus (HIV) integrase strand transfer inhibitors (INSTI) increases and formulations are being developed for maintenance therapies and chemoprophylaxis, assessing virus suppression under INSTI-based regimens in prevention-relevant biologic compartments, such as the male genital tract, is timely. We used cell-source marker virion immunocapture to examine amplification of particle RNA then assessed the phylogenetic relatedness of seminal and blood viral sequences from men with HIV who were prescribed INSTI-based regimens. Seminal plasma immunocaptures yielded amplifiable virion RNA from 13 of 24 (54%) men, and the sequences were primarily associated with markers indicative of macrophage and resident dendritic cell sources. Genetic distances were greatest (>2%) between seminal virions and circulating proviruses, pointing to ongoing low-level expression from tissue-resident cells. While the low levels in semen predict an improbable likelihood of transmission, viruses with large genetic distances are expressed under potent INSTI therapy and have implications for determining epidemiologic linkages if adherence is suboptimal.

Investigation of SARS-CoV-2 Infection among Companion Animals in Households with Confirmed Human COVID-19 Cases

We aimed to characterize SARS-CoV-2 infection in companion animals living in households with COVID-19-positive people and understand the dynamics surrounding how these animals become infected. Public health investigators contacted households with at least one confirmed, symptomatic person with COVID-19 for study recruitment. Blood, nasal, and rectal swab specimens were collected from pet dogs and cats and a questionnaire was completed. Specimens were tested for SARS-CoV-2 by RT-PCR, and for neutralizing antibodies; genomic sequencing was performed on viral-positive samples. A total of 36.4% of 110 pets enrolled had evidence of infection with SARS-CoV-2. Pets were more likely to test positive if the pet was immunocompromised, and if more than one person in the home was positive for COVID-19. Among 12 multi-pet households where at least one pet was positive, 10 had at least one other pet test positive. Whole-genome sequencing revealed the genomes of viral lineages circulating in the community during the time of sample collection. Our findings suggest a high likelihood of viral transmission in households with multiple pets and when pets had very close interactions with symptomatic humans. Further surveillance studies are needed to characterize how new variants impact animals and to understand opportunities for infection and spillover in susceptible species.

DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction

In the midst of an outbreak or sustained epidemic, reliable prediction of transmission risks and patterns of spread is critical to inform public health programs. Projections of transmission growth or decline among specific risk groups can aid in optimizing interventions, particularly when resources are limited. Phylogenetic trees have been widely used in the detection of transmission chains and high-risk populations. Moreover, tree topology and the incorporation of population parameters (phylodynamics) can be useful in reconstructing the evolutionary dynamics of an epidemic across space and time among individuals. We now demonstrate the utility of phylodynamic trees for transmission modeling and forecasting, developing a phylogeny-based deep learning system, referred to as DeepDynaForecast. Our approach leverages a primal-dual graph learning structure with shortcut multi-layer aggregation, which is suited for the early identification and prediction of transmission dynamics in emerging high-risk groups. We demonstrate the accuracy of DeepDynaForecast using simulated outbreak data and the utility of the learned model using empirical, large-scale data from the human immunodeficiency virus epidemic in Florida between 2012 and 2020. Our framework is available as open-source software (MIT license) at github.com/lab-smile/DeepDynaForcast.

High HIV diversity, recombination, and superinfection revealed in a large outbreak among persons who inject drugs in Kentucky and Ohio, USA

We investigated transmission dynamics of a large human immunodeficiency virus (HIV) outbreak among persons who inject drugs (PWID) in KY and OH during 2017-20 by using detailed phylogenetic, network, recombination, and cluster dating analyses. Using polymerase (pol) sequences from 193 people associated with the investigation, we document high HIV-1 diversity, including Subtype B (44.6 per cent); numerous circulating recombinant forms (CRFs) including CRF02_AG (2.5 per cent) and CRF02_AG-like (21.8 per cent); and many unique recombinant forms composed of CRFs with major subtypes and sub-subtypes [CRF02_AG/B (24.3 per cent), B/CRF02_AG/B (0.5 per cent), and A6/D/B (6.4 per cent)]. Cluster analysis of sequences using a 1.5 per cent genetic distance identified thirteen clusters, including a seventy-five-member cluster composed of CRF02_AG-like and CRF02_AG/B, an eighteen-member CRF02_AG/B cluster, Subtype B clusters of sizes ranging from two to twenty-three, and a nine-member A6/D and A6/D/B cluster. Recombination and phylogenetic analyses identified CRF02_AG/B variants with ten unique breakpoints likely originating from Subtype B and CRF02_AG-like viruses in the largest clusters. The addition of contact tracing results from OH to the genetic networks identified linkage between persons with Subtype B, CRF02_AG, and CRF02_AG/B sequences in the clusters supporting de novo recombinant generation. Superinfection prevalence was 13.3 per cent (8/60) in persons with multiple specimens and included infection with B and CRF02_AG; B and CRF02_AG/B; or B and A6/D/B. In addition to the presence of multiple, distinct molecular clusters associated with this outbreak, cluster dating inferred transmission associated with the largest molecular cluster occurred as early as 2006, with high transmission rates during 2017-8 in certain other molecular clusters. This outbreak among PWID in KY and OH was likely driven by rapid transmission of multiple HIV-1 variants including de novo viral recombinants from circulating viruses within the community. Our findings documenting the high HIV-1 transmission rate and clustering through partner services and molecular clusters emphasize the importance of leveraging multiple different data sources and analyses, including those from disease intervention specialist investigations, to better understand outbreak dynamics and interrupt HIV spread.

Changing genomic epidemiology of COVID-19 in long-term care facilities during the 2020-2022 pandemic, Washington State

BACKGROUND

Long-term care facilities (LTCFs) are vulnerable to disease outbreaks. Here, we jointly analyze SARS-CoV-2 genomic and paired epidemiologic data from LTCFs and surrounding communities in Washington state (WA) to assess transmission patterns during 2020-2022, in a setting of changing policy. We describe sequencing efforts and genomic epidemiologic findings across LTCFs and perform in-depth analysis in a single county.

METHODS

We assessed genomic data representativeness, built phylogenetic trees, and conducted discrete trait analysis to estimate introduction sizes over time, and explored selected outbreaks to further characterize transmission events.

RESULTS

We found that transmission dynamics among cases associated with LTCFs in WA changed over the course of the COVID-19 pandemic, with variable introduction rates into LTCFs, but decreasing amplification within LTCFs. SARS-CoV-2 lineages circulating in LTCFs were similar to those circulating in communities at the same time. Transmission between staff and residents was bi-directional.

CONCLUSIONS

Understanding transmission dynamics within and between LTCFs using genomic epidemiology on a broad scale can assist in targeting policies and prevention efforts. Tracking facility-level outbreaks can help differentiate intra-facility outbreaks from high community transmission with repeated introduction events. Based on our study findings, methods for routine tree building and overlay of epidemiologic data for hypothesis generation by public health practitioners are recommended. Discrete trait analysis added valuable insight and can be considered when representative sequencing is performed. Cluster detection tools, especially those that rely on distance thresholds, may be of more limited use given current data capture and timeliness. Importantly, we noted a decrease in data capture from LTCFs over time. Depending on goals for use of genomic data, sentinel surveillance should be increased or targeted surveillance implemented to ensure available data for analysis.

Molecular analysis and geographic distribution of the recent Indonesian rabies virus

Background and Aim

Some Indonesian islands, including Sumatra, Kalimantan, Sulawesi, Java, and East Nusa Tenggara, have endemic rabies. Rabies outbreaks in Bali began from 2008 to 2011 and continue to occur sporadically. This study aimed to study the molecular analysis and geographical distribution of Indonesian rabies virus (RABV) from 2016 to 2021 and compare to previous periods.

Materials and Methods

Virus isolates from 2016 to 2021 were extracted from dog brains and sequenced at the nucleoprotein gene locus. They were compared with data sequences available in the GenBank database. Indonesian RABV from the previous three periods (before 1989, 1997-2003, and 2008-2010) was extracted from the GenBank database. The genetic diversity in this study was based on the N gene of Indonesian RABV.

Results

Asian RABV, which is genetically close to the Indonesian virus, is a virus from China (ASIA-3 cluster) and from the Southeast Asia region, namely, virus isolates from Sarawak and Malaysia and some Cambodian isolates. Rabies virus, which was isolated from the Bali islands, was the new cluster first detected and published in Bali, Indonesia, in 2008, while RABV from West Sumatra Province, which was isolated from 2016 to 2021, was also considered a new cluster that is genetically distant from other clusters in Indonesia.

Conclusion

The RABV in Indonesia is divided into five clusters. The isolates from West Sumatra Province from 2016 to 2021 were a new cluster genetically distant from other Indonesian viruses.

HantaNet: A New MicrobeTrace Application for Hantavirus Classification, Genomic Surveillance, Epidemiology and Outbreak Investigations

Hantaviruses zoonotically infect humans worldwide with pathogenic consequences and are mainly spread by rodents that shed aerosolized virus particles in urine and feces. Bioinformatics methods for hantavirus diagnostics, genomic surveillance and epidemiology are currently lacking a comprehensive approach for data sharing, integration, visualization, analytics and reporting. With the possibility of hantavirus cases going undetected and spreading over international borders, a significant reporting delay can miss linked transmission events and impedes timely, targeted public health interventions. To overcome these challenges, we built HantaNet, a standalone visualization engine for hantavirus genomes that facilitates viral surveillance and classification for early outbreak detection and response. HantaNet is powered by MicrobeTrace, a browser-based multitool originally developed at the Centers for Disease Control and Prevention (CDC) to visualize HIV clusters and transmission networks. HantaNet integrates coding gene sequences and standardized metadata from hantavirus reference genomes into three separate gene modules for dashboard visualization of phylogenetic trees, viral strain clusters for classification, epidemiological networks and spatiotemporal analysis. We used 85 hantavirus reference datasets from GenBank to validate HantaNet as a classification and enhanced visualization tool, and as a public repository to download standardized sequence data and metadata for building analytic datasets. HantaNet is a model on how to deploy MicrobeTrace-specific tools to advance pathogen surveillance, epidemiology and public health globally.

Clinical Whole-Genome Sequencing Assay for Rapid Mycobacterium tuberculosis Complex First-Line Drug Susceptibility Testing and Phylogenetic Relatedness Analysis

The global rise of drug resistant tuberculosis has highlighted the need for improved diagnostic technologies that provide rapid and reliable drug resistance results. Here, we develop and validate a whole genome sequencing (WGS)-based test for identification of mycobacterium tuberculosis complex (MTB) drug resistance to rifampin, isoniazid, pyrazinamide, ethambutol, and streptomycin. Through comparative analysis of drug resistance results from WGS-based testing and phenotypic drug susceptibility testing (DST) of 38 clinical MTB isolates from patients receiving care in Los Angeles, CA, we found an overall concordance between methods of 97.4% with equivalent performance across culture media. Critically, prospective analysis of 11 isolates showed that WGS-based testing provides results an average of 36 days faster than phenotypic culture-based methods. We showcase the additional benefits of WGS data by investigating a suspected laboratory contamination event and using phylogenetic analysis to search for cryptic local transmission, finding no evidence of community spread amongst our patient population in the past six years. WGS-based testing for MTB drug resistance has the potential to greatly improve diagnosis of drug resistant MTB by accelerating turnaround time while maintaining accuracy and providing additional benefits for infection control, lab safety, and public health applications.

High Genetic Diversity of HIV-1 and Active Transmission Clusters among Male-to-Male Sexual Contacts (MMSCs) in Zhuhai, China

Monitoring genetic diversity and recent HIV infections (RHIs) is critical for understanding HIV epidemiology. Here, we report HIV-1 genetic diversity and RHIs in blood samples from 190 HIV-positive MMSCs in Zhuhai, China. MMSCs with newly reported HIV were enrolled from January 2020 to June 2022. A nested PCR was performed to amplify the HIV polymerase gene fragments at HXB2 positions 2604-3606. We constructed genetic transmission network at both 0.5% and 1.5% distance thresholds using the Tamura-Nei93 model. RHIs were identified using a recent infection testing algorithm (RITA) combining limiting antigen avidity enzyme immunoassay (LAg-EIA) assay with clinical data. The results revealed that 19.5% (37/190) were RHIs and 48.4% (92/190) were CRF07_BC. Two clusters were identified at a 0.5% distance threshold. Among them, one was infected with CRF07_BC for the long term, and the other was infected with CRF55_01B recently. We identified a total of 15 clusters at a 1.5% distance threshold. Among them, nine were infected with CRF07_BC subtype, and RHIs were found in 38.8% (19/49) distributed in eight genetic clusters. We identified a large active transmission cluster (n = 10) infected with a genetic variant, CRF79_0107. The multivariable logistic regression model showed that clusters were more likely to be RHIs (adjusted OR: 3.64, 95% CI: 1.51~9.01). The RHI algorithm can help to identify recent or ongoing transmission clusters where the prevention tools are mostly needed. Prompt public health measures are needed to contain the further spread of active transmission clusters.

Transmitted HIV Drug Resistance in Bulgaria Occurs in Clusters of Individuals from Different Transmission Groups and Various Subtypes (2012-2020)

Transmitted HIV drug resistance in Bulgaria was first reported in 2015 using data from 1988-2011. We determined the prevalence of surveillance drug resistance mutations (SDRMs) and HIV-1 genetic diversity in Bulgaria during 2012-2020 using polymerase sequences from 1053 of 2010 (52.4%) antiretroviral therapy (ART)-naive individuals. Sequences were analyzed for DRM using the WHO HIV SDRM list implemented in the calculated population resistance tool at Stanford University. Genetic diversity was inferred using automated subtyping tools and phylogenetics. Cluster detection and characterization was performed using MicrobeTrace. The overall rate of SDRMs was 5.7% (60/1053), with 2.2% having resistance to nucleoside reverse transcriptase inhibitors (NRTIs), 1.8% to non-nucleoside reverse transcriptase inhibitors (NNRTIs), 2.1% to protease inhibitors (PIs), and 0.4% with dual-class SDRMs. We found high HIV-1 diversity, with the majority being subtype B (60.4%), followed by F1 (6.9%), CRF02_AG (5.2%), A1 (3.7%), CRF12_BF (0.8%), and other subtypes and recombinant forms (23%). Most (34/60, 56.7%) of the SDRMs were present in transmission clusters of different subtypes composed mostly of male-to-male sexual contact (MMSC), including a 14-member cluster of subtype B sequences from 12 MMSC and two males reporting heterosexual contact; 13 had the L90M PI mutation and one had the T215S NRTI SDRM. We found a low SDRM prevalence amid high HIV-1 diversity among ART-naive patients in Bulgaria during 2012-2020. The majority of SDRMs were found in transmission clusters containing MMSC, indicative of onward spread of SDRM in drug-naive individuals. Our study provides valuable information on the transmission dynamics of HIV drug resistance in the context of high genetic diversity in Bulgaria, for the development of enhanced prevention strategies to end the epidemic.

Baseline Sequencing Surveillance of Public Clinical Testing, Hospitals, and Community Wastewater Reveals Rapid Emergence of SARS-CoV-2 Omicron Variant of Concern in Arizona, USA

The adaptive evolution of SARS-CoV-2 variants is driven by selection for increased viral fitness in transmissibility and immune evasion. Understanding the dynamics of how an emergent variant sweeps across populations can better inform public health response preparedness for future variants. Here, we investigated the state-level genomic epidemiology of SARS-CoV-2 through baseline genomic sequencing surveillance of 27,071 public testing specimens and 1,125 hospital inpatient specimens diagnosed between November 1, 2021, and January 31, 2022, in Arizona. We found that the Omicron variant rapidly displaced Delta variant in December 2021, leading to an "Omicron surge" of COVID-19 cases in early 2022. Wastewater sequencing surveillance of 370 samples supported the synchronous sweep of Omicron in the community. Hospital inpatient COVID-19 cases of Omicron variant presented to three major hospitals 10.51 days after its detection from public clinical testing. Nonsynonymous mutations in nsp3, nsp12, and nsp13 genes were significantly associated with Omicron hospital cases compared to community cases. To model SARS-CoV-2 transmissions across the state population, we developed a scalable sequence network methodology and showed that the Omicron variant spread through intracounty and intercounty transmissions. Finally, we demonstrated that the temporal emergence of Omicron BA.1 to become the dominant variant (17.02 days) was 2.3 times faster than the prior Delta variant (40.70 days) or subsequent Omicron sublineages BA.2 (39.65 days) and BA.5 (35.38 days). Our results demonstrate the uniquely rapid sweep of Omicron BA.1. These findings highlight how integrated public health surveillance can be used to enhance preparedness and response to future variants. IMPORTANCE SARS-CoV-2 continues to evolve new variants throughout the pandemic. However, the temporal dynamics of how SARS-CoV-2 variants emerge to become the dominant circulating variant is not precisely known. Genomic sequencing surveillance offers unique insights into how SARS-CoV-2 spreads in communities and the lead-up to hospital cases during a surge. Specifically, baseline sequencing surveillance through random selection of positive diagnostic specimens provides a representative outlook of the virus lineages circulating in a geographic region. Here, we investigated the emergence of the Omicron variant of concern in Arizona by leveraging baseline genomic sequence surveillance of public clinical testing, hospitals, and community wastewater. We tracked the spread and evolution of the Omicron variant as it first emerged in the general public, and its rapid shift in hospital admissions in the state health system. This study demonstrates the timescale of public health preparedness needed to respond to an antigenic shift in SARS-CoV-2.

Transmission Analysis of COVID-19 Outbreaks Associated with Places of Worship, Arkansas, May 2020-December 2020

The purpose of this study was to describe a statewide COVID-19 transmission involving places of worship (POWs) during the early phase of the pandemic. During the period of May 2020-December 2020, this analysis evaluated COVID-19 cases in Arkansas reported in REDCap for overall cases associated with POWs, cluster detection, and network analysis of one POW utilizing Microbetrace. A total of 9904 COVID-19 cases reported attending an in-person POW service during the early phase of the pandemic with 353 probable POW-associated clusters identified. Network analysis for 'POW A' showed at least 60 COVID-19 cases were traced to at least 4 different settings. The pandemic gave an opportunity to observe and stress the importance of public health and POWs working closely together with a shared goal of facilitating worship in a manner that optimizes congregational and community safety during a public health emergency.

Molecular Epidemiology of HIV-1 Subtype B Infection across Florida Reveals Few Large Superclusters with Metropolitan Origin

Florida is considered an epicenter of HIV in the United States. The U.S. federal plan for Ending the HIV Epidemic (EHE) within 10 years prioritizes seven of Florida's 67 counties for intervention. We applied molecular epidemiology methods to characterize the HIV infection networks in the state and infer whether the results support the EHE. HIV sequences (N = 34,446) and associated clinical/demographic metadata of diagnosed people with HIV (PWH), during 2007 to 2017, were retrieved from the Florida Department of Health. HIV genetic networks were investigated using MicrobeTrace. Associates of clustering were identified through boosted logistic regression. Assortative trait mixing was also assessed. Bayesian phylogeographic methods were applied to evaluate evidence of imported HIV-1 lineages and illustrate spatiotemporal flows within Florida. We identified nine large clusters spanning all seven EHE counties but little evidence of external introductions, suggesting-in the absence of undersampling-an epidemic that evolved independently from the rest of the country or other external influences. Clusters were highly assortative by geography. Most of the sampled infections (82%) did not cluster with others in the state using standard molecular surveillance methods despite satisfactory sequence sampling in the state. The odds of being unclustered were higher among PWH in rural regions, and depending on demographics. A significant number of unclustered sequences were observed in counties omitted from EHE. The large number of missing sequence links may impact timely detection of emerging transmission clusters and ultimately hinder the success of EHE in Florida. Molecular epidemiology may help better understand infection dynamics at the population level and underlying disparities in disease transmission among subpopulations; however, there is also a continuous need to conduct ethical discussions to avoid possible harm of advanced methodologies to vulnerable groups, especially in the context of HIV stigmatization. IMPORTANCE The large number of missing phylogenetic linkages in rural Florida counties and among women and Black persons with HIV may impact timely detection of ongoing and emerging transmission clusters and ultimately hinder the success of epidemic elimination goals in Florida.

Integrated genomic surveillance enables tracing of person-to-person SARS-CoV-2 transmission chains during community transmission and reveals extensive onward transmission of travel-imported infections, Germany, June to July 2021

Background

Tracking person-to-person SARS-CoV-2 transmission in the population is important to understand the epidemiology of community transmission and may contribute to the containment of SARS-CoV-2. Neither contact tracing nor genomic surveillance alone, however, are typically sufficient to achieve this objective.

Aim

We demonstrate the successful application of the integrated genomic surveillance (IGS) system of the German city of Düsseldorf for tracing SARS-CoV-2 transmission chains in the population as well as detecting and investigating travel-associated SARS-CoV-2 infection clusters.

Methods

Genomic surveillance, phylogenetic analysis, and structured case interviews were integrated to elucidate two genetically defined clusters of SARS-CoV-2 isolates detected by IGS in Düsseldorf in July 2021.

Results

Cluster 1 (n = 67 Düsseldorf cases) and Cluster 2 (n = 36) were detected in a surveillance dataset of 518 high-quality SARS-CoV-2 genomes from Düsseldorf (53% of total cases, sampled mid-June to July 2021). Cluster 1 could be traced back to a complex pattern of transmission in nightlife venues following a putative importation by a SARS-CoV-2-infected return traveller (IP) in late June; 28 SARS-CoV-2 cases could be epidemiologically directly linked to IP. Supported by viral genome data from Spain, Cluster 2 was shown to represent multiple independent introduction events of a viral strain circulating in Catalonia and other European countries, followed by diffuse community transmission in Düsseldorf.

Conclusion

IGS enabled high-resolution tracing of SARS-CoV-2 transmission in an internationally connected city during community transmission and provided infection chain-level evidence of the downstream propagation of travel-imported SARS-CoV-2 cases.

SOPHIE: Viral outbreak investigation and transmission history reconstruction in a joint phylogenetic and network theory framework

Genomic epidemiology is now widely used for viral outbreak investigations. Still, this methodology faces many challenges. First, few methods account for intra-host viral diversity. Second, maximum parsimony principle continues to be employed for phylogenetic inference of transmission histories, even though maximum likelihood or Bayesian models are usually more consistent. Third, many methods utilize case-specific data, such as sampling times or infection exposure intervals. This impedes study of persistent infections in vulnerable groups, where such information has a limited use. Finally, most methods implicitly assume that transmission events are independent, although common source outbreaks violate this assumption. We propose a maximum likelihood framework, SOPHIE, based on the integration of phylogenetic and random graph models. It infers transmission networks from viral phylogenies and expected properties of inter-host social networks modeled as random graphs with given expected degree distributions. SOPHIE is scalable, accounts for intra-host diversity, and accurately infers transmissions without case-specific epidemiological data.

Tracing the trajectories of SARS-CoV-2 variants of concern between December 2020 and September 2021 in the Canary Islands (Spain)

Several variants of concern (VOCs) explain most of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic waves in Europe. We aimed to dissect the spread of the SARS-CoV-2 VOCs in the Canary Islands (Spain) between December 2020 and September 2021 at a micro-geographical level. We sequenced the viral genome of 8,224 respiratory samples collected in the archipelago. We observed that Alpha (B.1.1.7) and Delta (B.1.617.2 and sublineages) were ubiquitously present in the islands, while Beta (B.1.351) and Gamma (P.1/P.1.1) had a heterogeneous distribution and were responsible for fewer and more controlled outbreaks. This work represents the largest effort for viral genomic surveillance in the Canary Islands so far, helping the public health bodies in decision-making throughout the pandemic.

Identifying HIV-1 Transmission Clusters in Uzbekistan through Analysis of Molecular Surveillance Data

The CRF02_AG and sub-subtype A6 are currently the predominant HIV-1 variants in the Republic of Uzbekistan, but little is known about their time-spatial clustering patterns in high-risk populations. We have applied molecular evolution methods and network analyses to better understand the transmission patterns of these subtypes by analyzing 316 pol sequences obtained during the surveillance study of HIV drug resistance. Network analysis showed that about one third of the HIV infected persons were organized into clusters, including large clusters with more than 35 members. These clusters were composed mostly of injecting drug users and/or heterosexuals, with women having mainly high centrality within networks identified in both subtypes. Phylogenetic analyses of the 'Uzbek' sequences, including those publicly available, show that Russia and Ukraine played a role as the main sources of the current subtype A6 epidemic in the Republic. At the same time, Uzbekistan has been a local center of the CRF02_AG epidemic spread in the former USSR since the early 2000s. Both of these HIV-1 variants continue to spread in Uzbekistan, highlighting the importance of identifying transmission networks and transmission clusters to prevent further HIV spread, and the need for HIV prevention and education campaigns in high-risk groups.

Exploring the Evolutionary History and Phylodynamics of Human Immunodeficiency Virus Type 1 Outbreak From Unnao, India Using Phylogenetic Approach

Certain rural and semiurban settings in the Unnao district, Uttar Pradesh, India observed an unprecedented increase in the detection of HIV cases during July 2017. Subsequent investigations through health camps and a follow-up case-control study attributed the outbreak to the unsafe injection exposures during treatment. In this study, we have undertaken a secondary analysis to understand the phylogenetic aspects of the outbreak-associated HIV-1 sequences along with the origin and phylodynamics of these sequences. The initial phylogenetic analysis indicated separate monophyletic grouping and there was no mixing of outbreak-associated sequences with sequences from other parts of India. Transmission network analysis using distance-based and non-distance-based methods revealed the existence of transmission clusters within the monophyletic Unnao clade. The median time to the most recent common ancestor (tMRCA) for sequences from Unnao using the pol gene region was observed to be 2011.87 [95% highest posterior density (HPD): 2010.09–2013.53], while the estimates using envelope (env) gene region sequences traced the tMRCA to 2010.33 (95% HPD: 2007.76–2012.99). Phylodynamics estimates demonstrated that the pace of this local epidemic has slowed down in recent times before the time of sampling, but was certainly on an upward track since its inception till 2014.

Unlocking capacities of genomics for the COVID-19 response and future pandemics

During the COVID-19 pandemic, genomics and bioinformatics have emerged as essential public health tools. The genomic data acquired using these methods have supported the global health response, facilitated development of testing methods, and allowed timely tracking of novel SARS-CoV-2 variants. Yet the virtually unlimited potential for rapid generation and analysis of genomic data is also coupled with unique technical, scientific, and organizational challenges. Here, we discuss the application of genomic and computational methods for the efficient data driven COVID-19 response, advantages of democratization of viral sequencing around the world, and challenges associated with viral genome data collection and processing.

Genotyping Cyclospora cayetanensis from multiple outbreak clusters with an emphasis on a cluster linked to bagged salad mix - United States, 2020

Cyclosporiasis is a diarrheal illness caused by the foodborne parasite Cyclospora cayetanensis. Annually reported cases have been increasing in the United States prompting development of genotyping tools to aid cluster detection. A recently developed Cyclospora genotyping system based on 8 genetic markers was applied to clinical samples collected during the cyclosporiasis peak period of 2020, facilitating assessment of its epidemiologic utility. While the system performed well and helped inform epidemiologic investigations, inclusion of additional markers to improve cluster detection was supported. Consequently, investigations have commenced to identify additional markers to enhance performance.

Investigation of US Cyclospora cayetanensis outbreaks in 2019 and evaluation of an improved Cyclospora genotyping system against 2019 cyclosporiasis outbreak clusters

Cyclosporiasis is an illness characterised by watery diarrhoea caused by the food-borne parasite Cyclospora cayetanensis. The increase in annual US cyclosporiasis cases led public health agencies to develop genotyping tools that aid outbreak investigations. A team at the Centers for Disease Control and Prevention (CDC) developed a system based on deep amplicon sequencing and machine learning, for detecting genetically-related clusters of cyclosporiasis to aid epidemiologic investigations. An evaluation of this system during 2018 supported its robustness, indicating that it possessed sufficient utility to warrant further evaluation. However, the earliest version of CDC's system had some limitations from a bioinformatics standpoint. Namely, reliance on proprietary software, the inability to detect novel haplotypes and absence of a strategy to select an appropriate number of discrete genetic clusters would limit the system's future deployment potential. We recently introduced several improvements that address these limitations and the aim of this study was to reassess the system's performance to ensure that the changes introduced had no observable negative impacts. Comparison of epidemiologically-defined cyclosporiasis clusters from 2019 to analogous genetic clusters detected using CDC's improved system reaffirmed its excellent sensitivity (90%) and specificity (99%), and confirmed its high discriminatory power. This C. cayetanensis genotyping system is robust and with ongoing improvement will form the basis of a US-wide C. cayetanensis genotyping network for clinical specimens.