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.

African non-human primates (NHPs) are natural hosts for simian immunodeficiency viruses (SIV), the zoonotic transmission of which led to the emergence of HIV-1 and HIV-2. However, our understanding of SIV diversity and evolution is limited by incomplete taxonomic and geographic sampling of NHPs, particularly in East Africa. In this study, we screened blood specimens from nine black-and-white colobus monkeys (Colobus guereza occidentalis) from Kibale National Park, Uganda, for novel SIVs using a combination of serology and “unbiased” deep-sequencing, a method that does not rely on genetic similarity to previously characterized viruses.

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.

Human immunodeficiency virus (HIV) type 1 and 2, the causative agents of acquired immunodeficiency syndrome (AIDS), emerged from African non-human primates (NHPs) through zoonotic transmission of simian immunodeficiency viruses (SIV). Among African NHPs, the Cercopithecus genus contains the largest number of species known to harbor SIV. However, our understanding of the diversity and evolution of SIVs infecting this genus is limited by incomplete taxonomic and geographic sampling, particularly in East Africa. In this study, we screened blood specimens from red-tailed guenons (Cercopithecus ascanius schmidti) from Kibale National Park, Uganda, for the presence of novel SIVs using unbiased deep-sequencing. We describe and characterize the first full-length SIV genomes from wild red-tailed guenons in Kibale National Park, Uganda. This new virus, tentatively named SIVrtg_Kib, was detected in five out of twelve animals and is highly divergent from other Cercopithecus SIVs as well as from previously identified SIVs infecting red-tailed guenons, thus forming a new SIV lineage. Our results show that the genetic diversity of SIVs infecting red-tailed guenons is greater than previously appreciated. This diversity could be the result of cross-species transmission between different guenon species or limited gene flow due to geographic separation among guenon populations.

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.

Motivation: 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. Results: We developed MicrobeTrace to facilitate rapid public health responses by overcoming barriers to data integration and exploration in molecular epidemiology. Using publicly available HIV sequences and other 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. Availability and Implementation: 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. MicrobeTrace is developed and actively maintained by the Centers for Disease Control and Prevention. The source code is available at https://github.com/cdcgov/microbetrace . Contact: ells@cdc.gov