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.

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.

Correction: Human T-cell lymphotropic virus type 1 transmission dynamics in rural villages in the Democratic Republic of the Congo with high nonhuman primate exposure.

[This corrects the article DOI: 10.1371/journal.pntd.0008923.].

MicrobeTrace: Retooling molecular epidemiology for rapid public health response.

Outbreak investigations use data from interviews, healthcare providers, laboratories and surveillance systems. However, integrated use of data from multiple sources requires a patchwork of software that present challenges in usability, interoperability, confidentiality, and cost. Rapid integration, visualization and analysis of data from multiple sources can guide effective public health interventions. We developed MicrobeTrace to facilitate rapid public health responses by overcoming barriers to data integration and exploration in molecular epidemiology. MicrobeTrace is a web-based, client-side, JavaScript application (https://microbetrace.cdc.gov) that runs in Chromium-based browsers and remains fully operational without an internet connection. Using publicly available data, we demonstrate the analysis of viral genetic distance networks and introduce a novel approach to minimum spanning trees that simplifies results. We also illustrate the potential utility of MicrobeTrace in support of contact tracing by analyzing and displaying data from an outbreak of SARS-CoV-2 in South Korea in early 2020. MicrobeTrace is developed and actively maintained by the Centers for Disease Control and Prevention. Users can email microbetrace@cdc.gov for support. The source code is available at https://github.com/cdcgov/microbetrace.

Accurate assembly of minority viral haplotypes from next-generation sequencing through efficient noise reduction.

Rapidly evolving RNA viruses continuously produce minority haplotypes that can become dominant if they are drug-resistant or can better evade the immune system. Therefore, early detection and identification of minority viral haplotypes may help to promptly adjust the patient's treatment plan preventing potential disease complications. Minority haplotypes can be identified using next-generation sequencing, but sequencing noise hinders accurate identification. The elimination of sequencing noise is a non-trivial task that still remains open. Here we propose CliqueSNV based on extracting pairs of statistically linked mutations from noisy reads. This effectively reduces sequencing noise and enables identifying minority haplotypes with the frequency below the sequencing error rate. We comparatively assess the performance of CliqueSNV using an in vitro mixture of nine haplotypes that were derived from the mutation profile of an existing HIV patient. We show that CliqueSNV can accurately assemble viral haplotypes with frequencies as low as 0.1% and maintains consistent performance across short and long bases sequencing platforms.

Human T-cell lymphotropic virus type 1 transmission dynamics in rural villages in the Democratic Republic of the Congo with high nonhuman primate exposure.

The Democratic Republic of the Congo (DRC) has a history of nonhuman primate (NHP) consumption and exposure to simian retroviruses yet little is known about the extent of zoonotic simian retroviral infections in DRC. We examined the prevalence of human T-lymphotropic viruses (HTLV), a retrovirus group of simian origin, in a large population of persons with frequent NHP exposures and a history of simian foamy virus infection. We screened plasma from 3,051 persons living in rural villages in central DRC using HTLV EIA and western blot (WB). PCR amplification of HTLV tax and LTR sequences from buffy coat DNA was used to confirm infection and to measure proviral loads (pVLs). We used phylogenetic analyses of LTR sequences to infer evolutionary histories and potential transmission clusters. Questionnaire data was analyzed in conjunction with serological and molecular data. A relatively high proportion of the study population (5.4%, n = 165) were WB seropositive: 128 HTLV-1-like, 3 HTLV-2-like, and 34 HTLV-positive but untypeable profiles. 85 persons had HTLV indeterminate WB profiles. HTLV seroreactivity was higher in females, wives, heads of households, and increased with age. HTLV-1 LTR sequences from 109 persons clustered strongly with HTLV-1 and STLV-1 subtype B from humans and simians from DRC, with most sequences more closely related to STLV-1 from Allenopithecus nigroviridis (Allen's swamp monkey). While 18 potential transmission clusters were identified, most were in different households, villages, and health zones. Three HTLV-1-infected persons were co-infected with simian foamy virus. The mean and median percentage of HTLV-1 pVLs were 5.72% and 1.53%, respectively, but were not associated with age, NHP exposure, village, or gender. We document high HTLV prevalence in DRC likely originating from STLV-1. We demonstrate regional spread of HTLV-1 in DRC with pVLs reported to be associated with HTLV disease, supporting local and national public health measures to prevent spread and morbidity.

Molecular Epidemiological Analysis of the Origin and Transmission Dynamics of the HIV-1 CRF01_AE Sub-Epidemic in Bulgaria.

HIV-1 subtype CRF01_AE is the second most predominant strain in Bulgaria, yet little is known about the molecular epidemiology of its origin and transmissibility. We used a phylodynamics approach to better understand this sub-epidemic by analyzing 270 HIV-1 polymerase (pol) sequences collected from persons diagnosed with HIV/AIDS between 1995 and 2019. Using network analyses at a 1.5% genetic distance threshold (d), we found a large 154-member outbreak cluster composed mostly of persons who inject drugs (PWID) that were predominantly men. At d = 0.5%, which was used to identify more recent transmission, the large cluster dissociated into three clusters of 18, 12, and 7 members, respectively, five dyads, and 107 singletons. Phylogenetic analysis of the Bulgarian sequences with publicly available global sequences showed that CRF01_AE likely originated from multiple Asian countries, with Vietnam as the likely source of the outbreak cluster between 1988 and 1990. Our findings indicate that CRF01_AE was introduced into Bulgaria multiple times since 1988, and infections then rapidly spread among PWID locally with bridging to other risk groups and countries. CRF01_AE continues to spread in Bulgaria as evidenced by the more recent large clusters identified at d = 0.5%, highlighting the importance of public health prevention efforts in the PWID communities.

Participation in Fraternity and Sorority Activities and the Spread of COVID-19 Among Residential University Communities - Arkansas, August 21-September 5, 2020.

Preventing transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), in colleges and universities requires mitigation strategies that address on- and off-campus congregate living settings as well as extracurricular activities and other social gatherings (1-4). At the start of the academic year, sorority and fraternity organizations host a series of recruitment activities known as rush week; rush week culminates with bid day, when selections are announced. At university A in Arkansas, sorority rush week (for women) was held during August 17-22, 2020, and consisted of on- and off-campus social gatherings, including an outdoor bid day event on August 22. Fraternity rush week (for men) occurred during August 27-31, with bid day scheduled for September 5. During August 22-September 5, university A-associated COVID-19 cases were reported to the Arkansas Department of Health (ADH). A total of 965 confirmed and probable COVID-19 cases associated with university A were identified, with symptom onset occurring during August 20-September 5, 2020; 31% of the patients with these cases reported involvement in any fraternity or sorority activity. Network analysis identified 54 gatherings among all linkages of cases to places of residence and cases to events, 49 (91%) were linked by participation in fraternity and sorority activities accounting for 42 (72%) links among gatherings. On September 4, university A banned gatherings of ≥10 persons, and fraternity bid day was held virtually. The rapid increase in COVID-19 cases was likely facilitated by on- and off-campus congregate living settings and activities, and health departments should work together with student organizations and university leadership to ensure compliance with mitigation measures.

Multidisciplinary Community-Based Investigation of a COVID-19 Outbreak Among Marshallese and Hispanic/Latino Communities - Benton and Washington Counties, Arkansas, March-June 2020.

By June 2020, Marshallese and Hispanic or Latino (Hispanic) persons in Benton and Washington counties of Arkansas had received a disproportionately high number of diagnoses of coronavirus disease 2019 (COVID-19). Despite representing approximately 19% of these counties' populations (1), Marshallese and Hispanic persons accounted for 64% of COVID-19 cases and 57% of COVID-19-associated deaths. Analyses of surveillance data, focus group discussions, and key-informant interviews were conducted to identify challenges and propose strategies for interrupting transmission of SARS-CoV-2, the virus that causes COVID-19. Challenges included limited native-language health messaging, high household occupancy, high employment rate in the poultry processing industry, mistrust of the medical system, and changing COVID-19 guidance. Reducing the COVID-19 incidence among communities that suffer disproportionately from COVID-19 requires strengthening the coordination of public health, health care, and community stakeholders to provide culturally and linguistically tailored public health education, community-based prevention activities, case management, care navigation, and service linkage.

Human Immunodeficiency Virus (HIV) Outbreak Investigation Among Persons Who Inject Drugs in Massachusetts Enhanced by HIV Sequence Data.

BACKGROUND: The Massachusetts Department of Public Health and the Centers for Disease Control and Prevention collaborated to characterize a human immunodeficiency virus (HIV) outbreak in northeastern Massachusetts and prevent further transmission. We determined the contributions of HIV sequence data to defining the outbreak. METHODS: Human immunodeficiency virus surveillance and partner services data were analyzed to understand social and molecular links within the outbreak. Cases were defined as HIV infections diagnosed during 2015-2018 among people who inject drugs with connections to northeastern Massachusetts or HIV infections among other persons named as partners of a case or whose HIV polymerase sequence linked to another case, regardless of diagnosis date or geography. RESULTS: Of 184 cases, 65 (35%) were first identified as part of the outbreak through molecular analysis. Twenty-nine cases outside of northeastern Massachusetts were molecularly linked to the outbreak. Large molecular clusters (75, 28, and 11 persons) were identified. Among 161 named partners, 106 had HIV; of those, 40 (38%) diagnoses occurred through partner services. CONCLUSIONS: Human immunodeficiency virus sequence data increased the case count by 55% and expanded the geographic scope of the outbreak. Human immunodeficiency virus sequence and partner services data each identified cases that the other method would not have, maximizing prevention and care opportunities for HIV-infected persons and their partners.

Phylodynamic Analysis Complements Partner Services by Identifying Acute and Unreported HIV Transmission.

Tailoring public health responses to growing HIV transmission clusters depends on accurately mapping the risk network through which it spreads and identifying acute infections that represent the leading edge of cluster growth. HIV transmission links, especially those involving persons with acute HIV infection (AHI), can be difficult to uncover, or confirm during partner services investigations. We integrated molecular, epidemiologic, serologic and behavioral data to infer and evaluate transmission linkages between participants of a prospective study of AHI conducted in North Carolina, New York City and San Francisco from 2011-2013. Among the 547 participants with newly diagnosed HIV with polymerase sequences, 465 sex partners were reported, of whom only 35 (7.5%) had HIV sequences. Among these 35 contacts, 23 (65.7%) links were genetically supported and 12 (34.3%) were not. Only five links were reported between participants with AHI but none were genetically supported. In contrast, phylodynamic inference identified 102 unreported transmission links, including 12 between persons with AHI. Importantly, all putative transmission links between persons with AHI were found among large clusters with more than five members. Taken together, the presence of putative links between acute participants who did not name each other as contacts that are found only among large clusters underscores the potential for unobserved or undiagnosed intermediaries. Phylodynamics identified many more links than partner services alone and, if routinely and rapidly integrated, can illuminate transmission patterns not readily captured by partner services investigations.

Opioid Use Fueling HIV Transmission in an Urban Setting: An Outbreak of HIV Infection Among People Who Inject Drugs-Massachusetts, 2015-2018.

Objectives. To describe and control an outbreak of HIV infection among people who inject drugs (PWID).Methods. The investigation included people diagnosed with HIV infection during 2015 to 2018 linked to 2 cities in northeastern Massachusetts epidemiologically or through molecular analysis. Field activities included qualitative interviews regarding service availability and HIV risk behaviors.Results. We identified 129 people meeting the case definition; 116 (90%) reported injection drug use. Molecular surveillance added 36 cases to the outbreak not otherwise linked. The 2 largest molecular groups contained 56 and 23 cases. Most interviewed PWID were homeless. Control measures, including enhanced field epidemiology, syringe services programming, and community outreach, resulted in a significant decline in new HIV diagnoses.Conclusions. We illustrate difficulties with identification and characterization of an outbreak of HIV infection among a population of PWID and the value of an intensive response.Public Health Implications. Responding to and preventing outbreaks requires ongoing surveillance, with timely detection of increases in HIV diagnoses, community partnerships, and coordinated services, all critical to achieving the goal of the national Ending the HIV Epidemic initiative.

A Multiplex HIV Incidence Assay for Inferring Recent HIV-1 Transmission and Time of Infection.

BACKGROUND: Laboratory assays for determining recent HIV-1 infection are an important public health tool for aiding in the estimation of HIV incidence. Some incidence assay analytes are remarkably predictive of time since seroconversion and may be useful for additional applications, such as predicting recent transmission events during HIV outbreaks and informing prevention strategies. METHODS: Plasma samples (n = 154) from a recent HIV-1 outbreak in a rural community in Indiana were tested with the customized HIV-1 Multiplex assay, based on the Bio-Rad Bio-Plex platform, which measures antibody response to HIV envelope antigens, gp120, gp160, and gp41. Assay cutoffs for each analyte were established to determine whether an individual seroconverted within 30, 60, or 90 days of the sample collection date. In addition, a novel bioinformatics method was implemented to infer infection dates of persons newly diagnosed with HIV during the outbreak. RESULTS: Sensitivity/specificity of the HIV-1 Multiplex assay for predicting seroconversion within 30, 60, and 90 days, based on a training data set, was 90.5%/95.4%, 94.1%/90%, and 89.4%/82.9%, respectively. Of 154 new diagnoses in Indiana between December 2014 and August 2016, the majority (71%) of recent infections (≤3 months since seroconversion) were identified between February and May 2016. The epidemiologic curve derived from the bioinformatics analysis indicated HIV transmission began as early as 2010, grew exponentially in 2014, and leveled off in April 2015. CONCLUSIONS: The HIV-1 Multiplex assay has the potential to identify and monitor trends in recent infection during an epidemic to assess the efficacy of programmatic or treatment interventions.

Detailed Transmission Network Analysis of a Large Opiate-Driven Outbreak of HIV Infection in the United States.

In January 2015, an outbreak of undiagnosed human immunodeficiency virus (HIV) infections among persons who inject drugs (PWID) was recognized in rural Indiana. By September 2016, 205 persons in this community of approximately 4400 had received a diagnosis of HIV infection. We report results of new approaches to analyzing epidemiologic and laboratory data to understand transmission during this outbreak. HIV genetic distances were calculated using the polymerase region. Networks were generated using data about reported high-risk contacts, viral genetic similarity, and their most parsimonious combinations. Sample collection dates and recency assay results were used to infer dates of infection. Epidemiologic and laboratory data each generated large and dense networks. Integration of these data revealed subgroups with epidemiologic and genetic commonalities, one of which appeared to contain the earliest infections. Predicted infection dates suggest that transmission began in 2011, underwent explosive growth in mid-2014, and slowed after the declaration of a public health emergency. Results from this phylodynamic analysis suggest that the majority of infections had likely already occurred when the investigation began and that early transmission may have been associated with sexual activity and injection drug use. Early and sustained efforts are needed to detect infections and prevent or interrupt rapid transmission within networks of uninfected PWID.

A population model evaluating the consequences of the evolution of double-resistance and tradeoffs on the benefits of two-drug antibiotic treatments.

The evolution of antibiotic resistance in microbes poses one of the greatest challenges to the management of human health. Because addressing the problem experimentally has been difficult, research on strategies to slow the evolution of resistance through the rational use of antibiotics has resorted to mathematical and computational models. However, despite many advances, several questions remain unsettled. Here we present a population model for rational antibiotic usage by adding three key features that have been overlooked: 1) the maximization of the frequency of uninfected patients in the human population rather than the minimization of antibiotic resistance in the bacterial population, 2) the use of cocktails containing antibiotic pairs, and 3) the imposition of tradeoff constraints on bacterial resistance to multiple drugs. Because of tradeoffs, bacterial resistance does not evolve directionally and the system reaches an equilibrium state. When considering the equilibrium frequency of uninfected patients, both cycling and mixing improve upon single-drug treatment strategies. Mixing outperforms optimal cycling regimens. Cocktails further improve upon aforementioned strategies. Moreover, conditions that increase the population frequency of uninfected patients also increase the recovery rate of infected individual patients. Thus, a rational strategy does not necessarily result in a tragedy of the commons because benefits to the individual patient and general public are not in conflict. Our identification of cocktails as the best strategy when tradeoffs between multiple-resistance are operating could also be extended to other host-pathogen systems. Cocktails or other multiple-drug treatments are additionally attractive because they allow re-using antibiotics whose utility has been negated by the evolution of single resistance.

Digital epidemiology.

Mobile, social, real-time: the ongoing revolution in the way people communicate has given rise to a new kind of epidemiology. Digital data sources, when harnessed appropriately, can provide local and timely information about disease and health dynamics in populations around the world. The rapid, unprecedented increase in the availability of relevant data from various digital sources creates considerable technical and computational challenges.