2020 Ebola virus disease outbreak in Équateur Province, Democratic Republic of the Congo: a retrospective genomic characterisation.

BACKGROUND: The Democratic Republic of the Congo has had 15 Ebola virus disease (EVD) outbreaks, from 1976 to 2023. On June 1, 2020, the Democratic Republic of the Congo declared an outbreak of EVD in the western Équateur Province (11th outbreak), proximal to the 2018 Tumba and Bikoro outbreak and concurrent with an outbreak in the eastern Nord Kivu Province. In this Article, we assessed whether the 11th outbreak was genetically related to previous or concurrent EVD outbreaks and connected available epidemiological and genetic data to identify sources of possible zoonotic spillover, uncover additional unreported cases of nosocomial transmission, and provide a deeper investigation into the 11th outbreak. METHODS: We analysed epidemiological factors from the 11th EVD outbreak to identify patient characteristics, epidemiological links, and transmission modes to explore virus spread through space, time, and age groups in the Équateur Province, Democratic Republic of the Congo. Trained field investigators and health professionals recorded data on suspected, probable, and confirmed cases, including demographic characteristics, possible exposures, symptom onset and signs and symptoms, and potentially exposed contacts. We used blood samples from individuals who were live suspected cases and oral swabs from individuals who were deceased to diagnose EVD. We applied whole-genome sequencing of 87 available Ebola virus genomes (from 130 individuals with EVD between May 19 and Sept 16, 2020), phylogenetic divergence versus time, and Bayesian reconstruction of phylogenetic trees to calculate viral substitution rates and study viral evolution. We linked the available epidemiological and genetic datasets to conduct a genomic and epidemiological study of the 11th EVD outbreak. FINDINGS: Between May 19 and Sept 16, 2020, 130 EVD (119 confirmed and 11 probable) cases were reported across 13 Équateur Province health zones. The individual identified as the index case reported frequent consumption of bat meat, suggesting the outbreak started due to zoonotic spillover. Sequencing revealed two circulating Ebola virus variants associated with this outbreak-a Mbandaka variant associated with the majority (97%) of cases and a Tumba-like variant with similarity to the ninth EVD outbreak in 2018. The Tumba-like variant exhibited a reduced substitution rate, suggesting transmission from a previous survivor of EVD. INTERPRETATION: Integrating genetic and epidemiological data allowed for investigative fact-checking and verified patient-reported sources of possible zoonotic spillover. These results demonstrate that rapid genetic sequencing combined with epidemiological data can inform responders of the mechanisms of viral spread, uncover novel transmission modes, and provide a deeper understanding of the outbreak, which is ultimately needed for infection prevention and control during outbreaks. FUNDING: WHO and US Centers for Disease Control and Prevention.

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.

Detection of Hantavirus during the COVID-19 Pandemic, Arizona, USA, 2020.

We identified 2 fatal cases of persons infected with hantavirus in Arizona, USA, 2020; 1 person was co-infected with SARS-CoV-2. Delayed identification of the cause of death led to a public health investigation that lasted ≈9 months after their deaths, which complicated the identification of a vector or exposure.

Detection of Sporadic Outbreaks of Rift Valley Fever in Uganda through the National Viral Hemorrhagic Fever Surveillance System, 2017-2020.

Rift Valley fever (RVF) is a zoonotic disease of public health and economic importance. Uganda has reported sporadic outbreaks of RVF in both humans and animals across the country, especially in the southwestern part of the "cattle corridor" through an established viral hemorrhagic fever surveillance system. We report 52 human cases of laboratory-confirmed RVF from 2017 to 2020. The case fatality rate was 42%. Among those infected, 92% were males and 90% were adults (≥ 18 years). Clinical symptoms were characterized by fever (69%), unexplained bleeding (69%), headache (51%), abdominal pain (49%), and nausea and vomiting (46%). Most of the cases (95%) originated from central and western districts that are part of the cattle corridor of Uganda, where the main risk factor was direct contact with livestock (P = 0.009). Other predictors of RVF positivity were determined to be male gender (P = 0.001) and being a butcher (P = 0.04). Next-generation sequencing identified the predominant Ugandan clade as Kenya-2, observed previously across East Africa. There is need for further investigation and research into the effect and spread of this neglected tropical disease in Uganda and the rest of Africa. Control measures such as promoting vaccination and limiting animal-human transmission could be explored to reduce the impact of RVF in Uganda and globally.

Correction: A global genotyping survey of Strongyloides stercoralis and Strongyloides fuelleborni using deep amplicon sequencing.

[This corrects the article DOI: 10.1371/journal.pntd.0007609.].

A global genotyping survey of Strongyloides stercoralis and Strongyloides fuelleborni using deep amplicon sequencing.

Strongyloidiasis is a neglected tropical disease caused by the human infective nematodes Strongyloides stercoralis, Strongyloides fuelleborni fuelleborni and Strongyloides fuelleborni kellyi. Previous large-scale studies exploring the genetic diversity of this important genus have focused on Southeast Asia, with a small number of isolates from the USA, Switzerland, Australia and several African countries having been genotyped. Consequently, little is known about the global distribution of geographic sub-variants of these nematodes and the genetic diversity that exists within the genus Strongyloides generally. We extracted DNA from human, dog and primate feces containing Strongyloides, collected from several countries representing all inhabited continents. Using a genotyping assay adapted for deep amplicon sequencing on the Illumina MiSeq platform, we sequenced the hyper-variable I and hyper-variable IV regions of the Strongyloides 18S rRNA gene and a fragment of the mitochondrial cytochrome c oxidase subunit 1 (cox1) gene from these specimens. We report several novel findings including unique S. stercoralis and S. fuelleborni genotypes, and the first identifications of a previously unknown S. fuelleborni infecting humans within Australia. We expand on an existing Strongyloides genotyping scheme to accommodate S. fuelleborni and these novel genotypes. In doing so, we compare our data to all 18S and cox1 sequences of S. fuelleborni and S. stercoralis available in GenBank (to our knowledge), that overlap with the sequences generated using our approach. As this analysis represents more than 1,000 sequences collected from diverse hosts and locations, representing all inhabited continents, it allows a truly global understanding of the population genetic structure of the Strongyloides species infecting humans, non-human primates, and domestic dogs.