Kristian G. Andersen

In April-July 2026, Andes virus (ANDV) caused an outbreak aboard the MV Hondius cruise ship resulting in 12 confirmed cases and three deaths. Before symptom onset, the index case had travelled through hantavirus-endemic regions of Chile and Argentina, leaving the geographic origin of infection unresolved. We conducted phylogenetic and phylogeographic analyses using newly generated ANDV genomes from human cases and rodent reservoirs to investigate the likely origin of the outbreak. Through phylogenetic analyses, we show that the outbreak genome falls within a geographically structured lineage spanning southern Chile and Argentine Andean Patagonia. Using phylogeographic reconstructions across all three segments, integrated with the index case’s travel itinerary, we identified the Toltén-Lanco area of southern Chile as the most likely geographic origin of the outbreak genome, with two candidate locations accounting for >98% of the posterior support. However, the index case visited this area 62-66 days before symptom onset, exceeding the conventionally reported ANDV incubation period of 7-42 days. Thus, while genomic evidence supports a southern Chilean origin of the outbreak genome, the timing and circumstances of transmission to the index case remain unresolved.

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.

Background: Observation of COVID-19 rebound after nirmatrelvir plus ritonavir (NPR) has driven important questions surrounding one of the only direct-acting antiviral treatments for COVID-19. Objective: The objective of this study was to examine the epidemiology of COVID-19 rebound among COVID-19–positive outpatients in the United States who independently decided whether or not to take NPR. Methods: This prospective, decentralized observational cohort study was conducted from August 2022 through December 2023 and included frequent proctored COVID-19 rapid antigen tests and self-report symptom surveys for 15 days. The primary outcome was the incidence of viral and symptom rebound. Secondary outcomes included time to initial viral and symptom clearance, rebound probability among patients who cleared by day 15, and symptom frequency. Results: Of 917 consenting participants, 669 (73%) were eligible for inclusion in the analysis (n=443, 66% in the NPR group; n=226, 34% in the control group). The mean age was 46.1 (SD 12.9) years, 62.6% (n=419) of participants were female, and 49.2% (n=329) had at least one preexisting condition. Overall, 15-day cumulative incidence was higher in the NPR group than the control group for both viral (70/443, 15.8% vs 12/226, 5.3%) and symptom (73/443, 16.5% vs 19/226, 8.4%) rebound. Time to initial viral and symptom clearance was similar between groups, and among those who experienced clearance by day 15, the probability of viral rebound (NPR: 19.1%, 95% CI 15.1%-24.0% vs control: 7%, 95% CI 4.0%-12.6%; P <.001) and symptom rebound (NPR: 47.7%, 95% CI 36.1%-60.8% vs control: 16.9%, 95% CI 10.9%-25.7%; P <.001) was higher in the NPR group than the control group. Conclusions: This study demonstrates that while COVID-19 rebound occurs in both NPR-treated and untreated outpatients, the incidence is higher in the NPR group.

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.

Widespread interest in the development of population-wide pathogen and antimicrobial resistance (AMR) monitoring has revealed wastewater’s microbial footprint as a marker of public health. Near-source wastewater remains a difficult sample type for microbiome analyses but represents a closer link to human health than the downstream products of its treatment. Few studies integrate methods for non-targeted monitoring applications, and critically, current methods cannot connect AMR genes to species, nor resolve full genomes. We address these challenges by developing a pipeline that enables untargeted metagenomics, metatranscriptomics, and novel long-read metagenomics (LRG). We achieve untargeted pathogen detection, limited by highly abundant resident species, while retaining microbial information with near-source sampling. Furthermore, LRG identifies antibiotic resistance gene-containing microbes and enables assembly of culture-independent genomes with previously unreported AMR genes. We establish an integrated approach to broadly monitor pathogens in wastewater, while demonstrating the importance of LRG to illuminate microbial AMR at the species level.

Case-based infectious disease surveillance is fundamental to public health, but is resource-intensive, logistically complex, and prone to sampling bias. Wastewater testing and sequencing have increasingly been used for population-scale monitoring of pathogen dynamics, including in low-resource settings. Broader adoption of wastewater genomic surveillance, however, is limited by a lack of flexibility across sequencing platforms and approaches, and adaptability to additional pathogens. Here, we describe “Freyja 2”, an integrated bioinformatics tool enabling robust real-time inference of pathogen lineage prevalence and growth dynamics from wastewater and other complex samples. In Freyja 2, we develop new methods for estimating lineage prevalence and growth rates, and demonstrate robustness across common sequencing platforms and to low genomic coverage. By incorporating global pathogen data streams, we extend Freyja 2 to support multi-pathogen surveillance. We demonstrate tracking of multiple recent or ongoing public health emergencies, including COVID-19, mpox, and H5N1 influenza, revealing unreported diversity and lineage co-circulation.

Background: Lassa fever is a fulminant viral illness associated with high in-hospital mortality. This disease constitutes a serious public health concern in West Africa, in particular Nigeria and the Mano River Union region (Guinea, Liberia, and Sierra Leone). In Sierra Leone, continuous monitoring of critically ill patients is hindered by a lack of equipment and personnel. Methods: We used wearable biosensor devices to remotely monitor hospitalized individuals with acute Lassa fever in order to describe vital sign trends that may be associated with clinical outcome and to evaluate the feasibility of this approach in a resource-limited setting. Results: The case fatality rate among participants (n = 8) was 62.5%, with a median time from hospital presentation to death of 2 days. Our results show that non-survivors (n = 5) spent a greater proportion of their monitoring period in the age-specific tachycardia range (45.8%) compared to survivors (1.7%), and had lower mean heart rate variability (10 ms) compared to those that survived (59 ms). Due to inconsistent sensor adhesion, as well as Bluetooth and cellular connectivity issues, over 80% of collected vital sign data was discarded for poor quality. Conclusions: Real-time monitoring of vital signs using wearable biosensors may have the potential to identify individuals at increased risk for poor outcomes in Lassa fever by detecting age-specific tachycardia and reductions in heart rate variability. Whether this represents an improvement upon traditional bedside vital sign collection in resource-limited settings is not clear, as a substantial proportion of monitoring data was of poor quality. Technical improvements in sensor connectivity and adhesion are needed to enable widespread use of this device, for both clinical and research purposes.

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.