Biomedical subjects

Andrew Rambaut

Publications and source records attributed to Andrew Rambaut.

6 recordsLinked to original sources

PubMed2026

Emergence of a Bundibugyo virus variant in the 2026 outbreak in the Democratic Republic of the Congo and Uganda.

In May 2026, an outbreak of Ebola disease caused by Bundibugyo virus (BDBV, species Orthoebolavirus bundibugyoense) was declared in the Democratic Republic of the Congo (DRC), with cases originating from DRC and locally transmitted cases reported in Uganda. Bundibugyo virus disease (BVD) outbreaks were previously recorded in 2007-2008 in Bundibugyo District, Uganda, and in 2012 in Isiro, DRC. Here, we generated 22 genomes from samples obtained from individuals with BVD in DRC and Uganda. These genomes form a well-supported phylogenetic cluster separate from BDBV variants associated with the 2007 and 2012 outbreaks, together with evidence for sustained human transmission. This is consistent with the emergence of a new zoonotic spillover event rather than resurgence from previously reported variants. Besides ongoing efforts in strengthening surveillance systems, community engagement, establishing Ebola treatment centers, and developing targeted medical countermeasures; our report advocates to specifically increase decentralized laboratory diagnostics capacity, with pan-Orthoebolavirus assays, including genomic sequencing capacity, for limiting further outbreak expansion, timely detection and control of future outbreaks.

Journal Article↗

PubMed2026

Deciphering the etiology of the 2024 outbreak of undiagnosed febrile illness in Panzi, Democratic Republic of the Congo.

In late 2024, an outbreak of over 400 cases of undiagnosed febrile illness, predominantly presenting as fever and cough, was reported in Panzi Health Zone, southwestern Democratic Republic of the Congo. Here we conducted an epidemiological and laboratory investigation to determine the etiology of the outbreak. Clinical data and specimens were prospectively collected from 108 individuals, of whom 59/108 (54.6%) were female. Children aged <5 years were the most affected (47/108, 43.5%); 14/32 (43.7%) were malnourished. Oro/nasopharyngeal swabs from 96/108 individuals were PCR tested; 26 blood samples were sequenced. Plasmodium falciparum was detected in 56/108 (51.8%) individuals. Co-infections were also detected, with influenza A(H1N1)pdm09 virus in 16/56 (28.6%) and severe acute respiratory syndrome coronavirus 2 in 10/56 (17.9%) individuals. No novel pathogens were detected via metagenomics. Our findings suggest that the outbreak was primarily associated with a surge in malaria cases, with concurrent viral respiratory infections. Increasing decentralized laboratory capacity and strengthening broader health systems remain crucial for faster outbreak detection and investigation.

Disease Outbreaks↗

PubMed2025

Parallel algorithms for phylogenetic inference under a structured coalescent approximation.

While advances in molecular epidemiology and computational modeling have enhanced our capacity to track pathogen evolution, the accurate reconstruction of spatiotemporal transmission dynamics remains essential for developing epidemic preparedness frameworks and implementing outbreak response measures. Structured coalescent models offer a phylogeographic framework by restricting lineage coalescence events to geographically proximate host populations. Although the Bayesian structured coalescent approximation (BASTA) provides a tractable approach, contemporary phylogeographic analyses involving dozens of geographic localities and hundreds to thousands of viral genomes substantially exceed the computational capacity of existing implementations. The BASTA likelihood scales cubically with deme count and quadratically with sequence count due to matrix exponentiation and pairwise coalescent probability calculations. Here, we introduce a comprehensive algorithmic restructuring of the structured coalescent likelihood that eliminates redundancies, optimizes memory access, and exposes parallelization opportunities. Our approach reorganizes computations along three dimensions: (i) independent calculation of deme-transition probability matrices across time intervals; (ii) simultaneous evaluation of partial likelihood vectors within temporal slices; and (iii) concurrent aggregation of coalescent probabilities. Algorithmic restructuring cuts average coalescent likelihood computation by 7-8 fold, and parallelization further boosts performance to 10-26 fold, enabling joint phylogeographic analyses of dengue virus across 10 South American countries and H5N1 avian influenza across 20 Eurasian regions to finish in a fraction of prior time. This computational efficiency also enables comparison between backward-in-time structured coalescent approximations and forward-in-time phylogeographic methods, revealing that the former provides appropriately conservative posterior estimates, particularly at intermediate phylogenetic depths. We integrate our implementation into the popular BEAST X and BEAGLE software packages, with an accompanying interface in BEAUti X to easily set up the analyses, providing researchers with an accessible and scalable tool for real-time phylogeographic surveillance of rapidly evolving pathogens.

Journal Article↗

PubMed2025

Metagenomics reveals cryptic circulation of zoonotic viruses in Nigeria.

Zoonotic spillover events pose an ongoing threat to global health, with historic and recent viral diseases of international concern emerging from animal reservoirs 1-6. In Nigeria, limited surveillance of animal hosts at the human and animal interface continues to hinder our understanding of viruses that are cryptically circulating in animals near human dwellings with potential for consequential spillover events. We performed unbiased metagenomic next-generation sequencing (mNGS) on tissue and swab samples collected from 240 individual animals across 11 taxa (rodents, shrews, bats, goats, sheep, pigs, dogs, cats, chickens, cattle egrets, and lizards) in two Lassa-affected Nigerian states (Ondo and Ebonyi). Host-depleted sequencing reads were assembled into contigs, taxonomically classified, and subjected to phylogenetic analyses to characterize viral diversity, host associations, and evidence of cross-species transmission. Across all samples, we identified 214 distinct viral taxa spanning 33 families, of which 41% (n = 83) represent novel species by ICTV criteria. Positive-sense RNA viruses dominated (Coronaviridae, Picornaviridae, Astroviridae), followed by negative-sense RNA, single- and double-stranded DNA, and double-stranded RNA viruses. Notably, human-associated enteroviruses-including Hepatitis A virus (genotype 1b), echoviruses, coxsackieviruses, and noroviruses-were detected in goats, pigs, dogs, and chickens, indicating cryptic circulation of human pathogens in peridomestic and domesticated animals. Phylogenetic reconstructions revealed multiple cross-species viral sharing events, particularly among rodents, goats, sheep, and pigs, and extensive recombination within Nigerian Betacoronavirus 1 lineages. Interestingly we found a putative novel avian like coronavirus in rodents, goats and sheep. Ecological modelling demonstrated that host species identity, sample type, and sampling effort were primary drivers of viral richness and abundance, and that higher overall viral diversity strongly predicted cross-species transmission potential. Our integrated mNGS approach uncovered a rich and dynamic virome within animals inhabiting human-dominated environments in Nigeria, including undetected circulation of human enteric viruses. These findings underscore the importance of broad-taxonomic, real-time surveillance at human-animal interfaces to inform early-warning systems and pandemic preparedness, particularly in low-resource settings.

Journal Article↗

PubMed2025

Genomic epidemiology of clade Ia monkeypox viruses circulating in the Central African Republic in 2022-24: a retrospective cross-sectional study.

BACKGROUND: The spread of monkeypox virus (Orthopoxvirus monkeypox) clade Ib from the Democratic Republic of the Congo to neighbouring countries has raised global concerns, leading to WHO declaring mpox a public health emergency on Aug 14, 2024. We applied genomic epidemiology to investigate the causes of recurrent mpox outbreaks in the Central African Republic. We aimed to determine whether frequent zoonotic spillovers or increased human-to-human transmissions are driving mpox epidemiology. METHODS: We performed a retrospective cross-sectional study of monkeypox virus genomic sequences among PCR-confirmed mpox cases detected in the Central African Republic between Feb 17, 2022, and Sept 17, 2024. We used hybridisation capture coupled to high throughput sequencing to analyse 46 samples from mpox outbreaks that occurred in eight of the 20 prefectures (14 of 35 health districts). Near-complete genomes were used for phylogenomic analyses. FINDINGS: Between Jan 10, 2022, and Sept 15, 2024, 89 mpox cases were confirmed, including 53 cases in the first 9 months of 2024. We generated 41 near-complete genomes from this period, including 33 from 2024. All new and already published monkeypox virus genomes from the Central African Republic belonged to clade Ia. These genomes spanned the phylogenetic diversity of clade Ia viruses, and most likely represented several dozen independent transmission events to humans. The monkeypox virus phylogenetic diversity was geographically structured within the country. Plausibly linked cases often showed indistinguishable genomes. Conversely, we detected identical genomes in cases that epidemiological information would suggest were independent outbreaks. Finally, we found that three distinct viruses caused cases in the capital city of Bangui in July, 2024, with all three detected on the same day (July 24, 2024). We did not detect substantial enrichment of APOBEC3 editing, suggesting limited human-to-human transmission. INTERPRETATION: The data indicate that mpox epidemiology in the Central African Republic is primarily driven by short-lived outbreaks resulting from many independent zoonotic spillover events, particularly in rural areas. Although evidence remains limited, in Bangui additional factors such as movement of people and importation of bushmeat from other regions might be introducing the virus into urban settings. Similar spillover patterns have been observed in the Democratic Republic of the Congo. The poorly understood nature of monkeypox virus reservoirs in both countries is a regional concern, as frequent spillovers increase the risk of outbreaks leading to sustained human transmission. Beyond strengthening surveillance and developing countermeasures, it is important to better understand the reservoirs and focus on reducing transmission opportunities to prevent further outbreaks. FUNDING: Pasteur Institute of Bangui, Africa CDC, AFROSCREEN, WHO, the Helmholtz Institute for One Health, and the Deutsche Forschungsgemeinschaft.

Humans↗

PubMed2024

A framework for automated scalable designation of viral pathogen lineages from genomic data.

Pathogen lineage nomenclature systems are a key component of effective communication and collaboration for researchers and public health workers. Since February 2021, the Pango dynamic lineage nomenclature for SARS-CoV-2 has been sustained by crowdsourced lineage proposals as new isolates were sequenced. This approach is vulnerable to time-critical delays as well as regional and personal bias. Here we developed a simple heuristic approach for dividing phylogenetic trees into lineages, including the prioritization of key mutations or genes. Our implementation is efficient on extremely large phylogenetic trees consisting of millions of sequences and produces similar results to existing manually curated lineage designations when applied to SARS-CoV-2 and other viruses including chikungunya virus, Venezuelan equine encephalitis virus complex and Zika virus. This method offers a simple, automated and consistent approach to pathogen nomenclature that can assist researchers in developing and maintaining phylogeny-based classifications in the face of ever-increasing genomic datasets.

Animals↗