Daniel J. Park

Background: During the 2014 Ebola outbreak in Sierra Leone, widespread infections among healthcare workers at Kenema Government Hospital severely disrupted clinical operations. Whether these infections primarily arose from repeated patient exposures or from transmission among colleagues remained unclear, particularly as patient admissions exceeded the hospital’s capacity for safe isolation and care. Methods: We revisited the outbreak using expanded viral surveillance and classical genomic analysis, Bayesian transmission inference with juniper , and firsthand epidemiologic accounts from hospital staff. We analyzed 279 Ebola virus genomes from Kenema District, including 35 from infected healthcare workers, and 514 genomes in the broader transmission reconstruction, explicitly accounting for unsampled cases. Results: The 35 healthcare worker genomes comprised 28 haplotypes, including five shared by multiple cases. In the reconstructed transmission network, 22 candidate healthcare worker-to-healthcare worker links exceeded a posterior probability threshold of 5%, representing 6.1-fold enrichment over expectation. Healthcare workers accounted for 33.2% of expected onward transmissions at their peak in the two-week window beginning June 23, 2014. Firsthand anecdotal accounts situated inferred relationships within specific wards, clinical procedures, and working relationships of KGH, including care of infected colleagues and infections among laboratory, pharmacy, and other personnel beyond routine bedside care. Conclusions: Ebola transmission involved interconnected groups of healthcare workers across multiple hospital departments during a period of severe resource constraints and expanding patient demand. These findings underscore the importance of protecting and surveilling healthcare workers as an interconnected workforce during emerging outbreaks.

Pathogen genomic analysis is central to tracking, understanding and containing outbreaks ^1–13 , but the complexity and cost of state-of-the-art phylogenetic tools limit global access and impact. Here we introduce Delphy, an exact reformulation of Bayesian phylogenetics ^14–17 designed to transform its speed, scalability and accessibility while retaining Bayesian state-of-the-art accuracy. Delphy’s central data structure, an explicit mutation-annotated tree, takes advantage of the high sequence similarity of large-scale epidemic datasets ^18–20 for efficient tree exploration and convergence. By reproducing key analyses from recent major epidemics, including Ebola ^1,21 , Zika ² , SARS-CoV-2 (ref. ²² ), mpox ^3,4 and H5N1 (refs. ^23,24 ), we demonstrate state-of-the-art accuracy with up to 2–3 orders of magnitude improvements in speed. Assessing Delphy’s scalability, we show that a simulated dataset of 100,000 sequences can be analysed within a day. We distribute Delphy as a client-side web application that enables local, interactive analysis of raw data on the user’s machine. Delphy automatically identifies key viral lineages and mutations, as well as their emergence and prevalence through time, with quantified uncertainties grounded in Bayesian theory. Delphy establishes Bayesian phylogenetics as a fast, accessible frontline tool for future outbreak response.

Mpox is a re-emerging zoonotic disease caused by MPXV, which has led to outbreaks across multiple countries in recent years. Sierra Leone reported its first mpox case in 8 years in January 2025, rapidly becoming the epicenter of a continental outbreak with more than 5,000 confirmed cases by August, a surge with unknown origins, timings and drivers. Phylodynamic analyses using 338 genomes generated from 14 districts suggests that the outbreak was caused by lineage G.1 (A.2.2.1) which descended from lineages circulating in Nigeria. Here we observed a strong APOBEC3 mutational enrichment, consistent with sustained human transmission that circulated undetected for ~3 months before the first confirmed case in January 2025. The Western Area Urban district served as the primary hub for nationwide spread and persistence, as well as multiple international export events. We further estimated that the true epidemic size was nearly double official case counts, highlighting substantial surveillance gaps. These findings underscore the urgent need for strengthened genomic and diagnostic surveillance systems across West Africa to pre-empt epidemics.

Yellow fever virus (YFV), a mosquito-borne flavivirus that causes severe hemorrhagic disease, is endemic in parts of South America and Africa, yet genomic data from Sierra Leone is lacking despite ongoing surveillance. Using hybrid-capture metagenomic sequencing, we generated a complete 10,611-bp YFV genome (98% coverage) from an adult male patient who reported to the Kailahun Government Hospital with fever and muscle pain. Phylogenetic analysis assigned the genome to West African Clade V via the YFV Nextstrain build. Compared with other West African isolates, the Sierra Leone genome contained 57 substitutions, three of which were non-synonymous (NS2b: N79S, NS3: V515I, and RdRp: A643V). Bayesian phylogenetics estimated the time to the most recent common ancestor with the closest sequences from Senegal and the Netherlands as January 14, 2001 (95% HPD: December 17, 1987 - April 28, 2009), suggesting long-standing, cryptic transmission. Together, these findings underscore the need for expanded genomic surveillance to monitor YFV spread, evolution, and potential impacts on vaccine performance.

In January 2025, Sierra Leone reported its first mpox case in eight years, followed by a rapid nationwide surge that made it the epicenter of the continental mpox virus (MPXV) Clade IIb A.2.2 outbreak, with more than 5,000 confirmed cases reported by August. To investigate the origin, timing, and spread of this epidemic, we generated 338 high-quality MPXV genomes from cases across 14 districts and conducted Bayesian phylogeographic analyses. We found that transmission was driven by a newly emerging Clade IIb lineage, G.1, descended from lineages circulating in the ongoing Nigerian epidemic. Phylogeographic reconstructions indicate that G.1 arose in late September 2024 and circulated undetected for approximately three months before establishing sustained transmission in the densely populated Western Area Urban and Rural districts, which became the principal source of repeated viral introductions that drove epidemics in other regions. Together, these findings reveal the origins and dispersal dynamics of the 2025 mpox outbreak in Sierra Leone and underscore that effective control remains achievable through targeted vaccination, strengthened early warning systems, and improved access to genomic and diagnostic surveillance.

Five years before the 2022 multi-country mpox outbreak, Nigeria and Cameroon reported their first cases in more than three decades^1,2. Whereas the outbreak in Nigeria is recognized as an ongoing human epidemic, the drivers of the resurgence in Cameroon remain unclear^3,4. The rate of zoonoses remains uncertain in both countries, and gaps in genomic data obscure the timing and zoonotic and geographic origin of monkeypox virus (MPXV) emergence in humans. Here, to address these uncertainties, we sequenced 118 MPXV genomes isolated from cases in Nigeria and Cameroon between 2018 and 2023. We show that in contrast to cases in Nigeria, cases in Cameroon are the result of repeated zoonoses, with two distinct zoonotic lineages circulating across the Nigeria–Cameroon border. Our findings suggest that shared animal populations in the cross-border forest ecosystems drive the emergence and spread of the virus. Accordingly, we identify the closest zoonotic outgroup to the Nigerian human epidemic lineage (hMPXV-1) in a southern Nigerian border state. We estimate that the shared ancestor of the zoonotic outgroup and hMPXV-1 circulated in animals in southern Nigeria in late 2013. We find that hMPXV-1 emerged in humans in August 2014 in the southern Rivers State and circulated undetected for three years. Rivers State was the main source of viral spread during the human epidemic. Our study sheds light on the recent establishment of MPXV in the human population and highlights the risk of persistent zoonotic emergence of MPXV in the complex border regions of Cameroon and Nigeria.

Transmission reconstruction––the inference of who infects whom in disease outbreaks––offers critical insights into how pathogens spread and provides opportunities for targeted control measures. We developed JUNIPER (Joint Underlying Network Inference for Phylogenetic and Epidemiological Reconstructions), a highly-scalable pathogen outbreak reconstruction tool that incorporates intrahost variation, incomplete sampling, and algorithmic parallelization. Central to JUNIPER is a statistical model for within-host variant frequencies observed by next generation sequencing, which we validated on a dataset of over 160,000 deep-sequenced SARS-CoV-2 genomes. Combining this within-host variation model with population-level evolutionary and transmission models, we developed a method for inferring phylogenies and transmission trees simultaneously. We benchmarked JUNIPER on computer-generated and real outbreaks in which transmission links were known or epidemiologically confirmed. We demonstrated JUNIPER’s real-world utility on two large-scale datasets: over 1,500 bovine H5N1 cases and over 13,000 human COVID-19 cases. Based on these analyses, we quantified the elevated H5N1 transmission rates in California and identified high-confidence transmission events, and demonstrated the efficacy of vaccination for reducing SARS-CoV-2 transmission. By overcoming computational and methodological limitations in existing outbreak reconstruction tools, JUNIPER provides a robust framework for studying pathogen spread at scale.

Pathogen genomic analysis is central to tracking, understanding, and containing outbreaks, but complexity and high costs of state-of-the-art (SOTA) phylogenetic tools limit global access and impact. We introduce Delphy, an exact reformulation of Bayesian phylogenetics designed to transform its speed, scalability and accessibility while retaining SOTA accuracy. Delphy's central data structure, an Explicit Mutation Annotated Tree, exploits the high sequence similarity in large-scale epidemic datasets for efficient tree exploration and convergence. By reproducing key analyses from recent major epidemics (Ebola, Zika, SARS-CoV-2, mpox, and H5N1), we demonstrate SOTA accuracy with up to 1,000x speedups. Assessing Delphy's scalability, we show that a simulated dataset of 100,000 sequences can be analyzed in under a day—the largest such computation to date. We distribute Delphy as a client-side web application, enabling users worldwide to turn raw data into interactive results within minutes, without the data ever leaving the user's machine. Delphy automatically identifies key viral lineages and mutations, as well as their emergence and prevalence through time, all with quantified uncertainties derived from a solid theoretical foundation. Delphy shows the power of Bayesian phylogenetics as a fast, accessible frontline tool for tackling future outbreaks.

Transmission reconstruction--the inference of who infects whom in disease outbreaks--offers critical insights into how pathogens spread and provides opportunities for targeted control measures. We developed JUNIPER (Joint Underlying Network Inference for Phylogenetic and Epidemiological Reconstructions), a highly-scalable pathogen outbreak reconstruction tool that incorporates intrahost variation, incomplete sampling, and algorithmic parallelization. Central to JUNIPER is a statistical model for within-host variant frequencies observed by next generation sequencing, which we validated on a dataset of over 160,000 deep-sequenced SARS-CoV-2 genomes. Combining this within-host variation model with population-level evolutionary and transmission models, we developed a method for inferring phylogenies and transmission trees simultaneously. We benchmarked JUNIPER on computer-generated and real outbreaks in which transmission links were known or epidemiologically confirmed. We demonstrated JUNIPER's real-world utility on two large-scale datasets: over 1,500 bovine H5N1 cases and over 13,000 human COVID-19 cases. Based on these analyses, we quantified the elevated H5N1 transmission rates in California and identified high-confidence transmission events, and demonstrated the efficacy of vaccination for reducing SARS-CoV-2 transmission. By overcoming computational and methodological limitations in existing outbreak reconstruction tools, JUNIPER provides a robust framework for studying pathogen spread at scale.

Highly Pathogenic Avian Influenza strain H5N1 has caused a multi-state outbreak among US dairy cattle, spreading across 15 states and infecting hundreds of herds since its onset. We rapidly developed and optimized PCR-based detection assays and sequencing protocols to support H5N1 molecular surveillance. Using 214 retail milk from 20 states for methods development, we found that H5N1 concentrations by digital PCR strongly correlated with qPCR cycle threshold (Ct) values, with dPCR exhibiting greater sensitivity. We also found that metagenomic sequencing after hybrid selection was best for higher concentration samples while amplicon sequencing performs best for lower concentrations. By establishing these methods, we were able to support the creation of a statewide surveillance program to test bulk milk samples monthly from all cattle dairy farms within Massachusetts, which remain negative to date. The methods, workflow, and recommendations described here provide a framework for others aiming to conduct H5N1 surveillance efforts.