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Journal articlePeeling RW, Fongwen NT, Guzman MG, et al., 2025,
Specimen and data sharing to advance research and development on Zika virus
, The Lancet Microbe, Pages: 101057-101057, ISSN: 2666-5247 -
Journal articleHozé N, Pons-Salort M, Metcalf CJE, et al., 2025,
RSero: A user-friendly R package to reconstruct pathogen circulation history from seroprevalence studies.
, PLoS Comput Biol, Vol: 21Population-based serological surveys are a key tool in epidemiology to characterize the level of population immunity and reconstruct the past circulation of pathogens. A variety of serocatalytic models have been developed to estimate the force of infection (FOI) (i.e., the rate at which susceptible individuals become infected) from age-stratified seroprevalence data. However, few tool currently exists to easily implement, combine, and compare these models. Here, we introduce an R package, Rsero, that implements a series of serocatalytic models and estimates the FOI from age-stratified seroprevalence data using Bayesian methods. The package also contains a series of features to perform model comparison and visualise model fit. We introduce new serocatalytic models of successive outbreaks and extend existing models of seroreversion to any transmission model. The different features of the package are illustrated with simulated and real-life data. We show we can identify the correct epidemiological scenario and recover model parameters in different epidemiological settings. We also show how the package can support serosurvey study design in a variety of epidemic situations. This package provides a standard framework to epidemiologists and modellers to study the dynamics of past pathogen circulation from cross-sectional serological survey data.
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Journal articleScachetti GC, Forato J, Claro IM, et al., 2025,
Re-emergence of Oropouche virus between 2023 and 2024 in Brazil: an observational epidemiological study.
, Lancet Infect Dis, Vol: 25, Pages: 166-175BACKGROUND: Oropouche virus is an arthropod-borne virus that has caused outbreaks of Oropouche fever in central and South America since the 1950s. This study investigates virological factors contributing to the re-emergence of Oropouche fever in Brazil between 2023 and 2024. METHODS: In this observational epidemiological study, we combined multiple data sources for Oropouche virus infections in Brazil and conducted in-vitro and in-vivo characterisation. We collected serum samples obtained in Manaus City, Amazonas state, Brazil, from patients with acute febrile illnesses aged 18 years or older who tested negative for malaria and samples from people with previous Oropouche virus infection from Coari municipality, Amazonas state, Brazil. Basic clinical and demographic data were collected from the Brazilian Laboratory Environment Management System. We calculated the incidence of Oropouche fever cases with data from the Brazilian Ministry of Health and the 2022 Brazilian population census and conducted age-sex analyses. We used reverse transcription quantitative PCR to test for Oropouche virus RNA in samples and subsequently performed sequencing and phylogenetic analysis of viral isolates. We compared the phenotype of the 2023-24 epidemic isolate (AM0088) with the historical prototype strain BeAn19991 through assessment of titre, plaque number, and plaque size. We used a plaque reduction neutralisation test (PRNT50) to assess the susceptibility of the novel isolate and BeAn19991 isolate to antibody neutralisation, both in serum samples from people previously infected with Oropouche virus and in blood collected from mice that were inoculated with either of the strains. FINDINGS: 8639 (81·8%) of 10 557 laboratory-confirmed Oropouche fever cases from Jan 4, 2015, to Aug 10, 2024, occurred in 2024, which is 58·8 times the annual median of 147 cases (IQR 73-325). Oropouche virus infections were reported in all 27 federal units, with 8182 (77·5%) of
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Journal articleTamayo Cuartero C, Carnegie AC, Cucunuba ZM, et al., 2025,
From the 100 Day Mission to 100 lines of software development: how to improve early outbreak analytics.
, Lancet Digit Health, Vol: 7, Pages: e161-e166Since the COVID-19 pandemic, considerable advances have been made to improve epidemic preparedness by accelerating diagnostics, therapeutics, and vaccine development. However, we argue that it is crucial to make equivalent efforts in the field of outbreak analytics to help ensure reliable, evidence-based decision making. To explore the challenges and key priorities in the field of outbreak analytics, the Epiverse-TRACE initiative brought together a multidisciplinary group of experts, including field epidemiologists, data scientists, academics, and software engineers from public health institutions across multiple countries. During a 3-day workshop, 40 participants discussed what the first 100 lines of code written during an outbreak should look like. The main findings from this workshop are summarised in this Viewpoint. We provide an overview of the current outbreak analytic landscape by highlighting current key challenges that should be addressed to improve the response to future public health crises. Furthermore, we propose actionable solutions to these challenges that are achievable in the short term, and longer-term strategic recommendations. This Viewpoint constitutes a call to action for experts involved in epidemic response to develop modern and robust data analytic approaches at the heart of epidemic preparedness and response.
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Journal articleLackritz EM, Ng L-C, Marques ETA, et al., 2025,
Zika virus: advancing a priority research agenda for preparedness and response
, The Lancet Infectious Diseases, ISSN: 1473-3099 -
Journal articleSchmit N, Topazian H, Pianella M, et al., 2025,
Quantifying the potential value of entomological data collection for programmatic decision-making on malaria control in sub-Saharan African settings
, Malaria Journal, ISSN: 1475-2875 -
Journal articlePons-Salort M, Blake IM, Grassly NC, 2025,
Duration of immunity after inactivated poliovirus vaccine: how many booster doses are needed?
, Clin Microbiol Infect -
Journal articlePenn MJ, Scheidwasser N, Donnelly CA, et al., 2025,
Bayesian Inference of Phylogenetic Distances: Revisiting the Eigenvalue Approach.
, Bull Math Biol, Vol: 87Using genetic data to infer evolutionary distances between molecular sequence pairs based on a Markov substitution model is a common procedure in phylogenetics, in particular for selecting a good starting tree to improve upon. Many evolutionary patterns can be accurately modelled using substitution models that are available in closed form, including the popular general time reversible model (GTR) for DNA data. For more complex biological phenomena, such as variations in lineage-specific evolutionary rates over time (heterotachy), other approaches such as the GTR with rate variation (GTR + Γ ) are required, but do not admit analytical solutions and do not automatically allow for likelihood calculations crucial for Bayesian analysis. In this paper, we derive a hybrid approach between these two methods, incorporating Γ ( α , α ) -distributed rate variation and heterotachy into a hierarchical Bayesian GTR-style framework. Our approach is differentiable and amenable to both stochastic gradient descent for optimisation and Hamiltonian Markov chain Monte Carlo for Bayesian inference. We show the utility of our approach by studying hypotheses regarding the origins of the eukaryotic cell within the context of a universal tree of life and find evidence for a two-domain theory.
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Journal articleAnanth S, Adeoti AO, Ray A, et al., 2025,
Healthcare worker views on antimicrobial resistance in chronic respiratory disease
, Antimicrobial Resistance and Infection Control, ISSN: 2047-2994 -
Journal articleLaufer Halpin A, Mathers AJ, Walsh TR, et al., 2025,
A framework towards implementation of sequencing for antimicrobial-resistant and other health-care-associated pathogens.
, Lancet Infect DisAntimicrobial resistance continues to be a growing threat globally, specifically in health-care settings in which antimicrobial-resistant pathogens cause a substantial proportion of health-care-associated infections (HAIs). Next-generation sequencing (NGS) and the analysis of the data produced therein (ie, bioinformatics) represent an opportunity to enhance our capacity to address these threats. The 3rd Geneva Infection Prevention and Control Think Tank brought together experts to identify gaps, propose solutions, and set priorities for the use of NGS for HAIs and antimicrobial-resistant pathogens. The major deliverable recommendation from this meeting was a proposed framework for implementing the sequencing of HAI pathogens, specifically those harbouring antimicrobial-resistance mechanisms. The key components of the proposed framework relate to wet laboratory quality, sequence data quality, database and tool selection, bioinformatic analyses, data sharing, and NGS data integration, to support public health and actions for infection prevention and control. In this Personal View we detail and discuss the framework in the context of global implementation, specifically in low-income and middle-income countries.
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