#Overview of available modelling #evidence to inform the scale and potential spread of #Bundibugyo virus in the current #Ebola disease #outbreak (ECDC, June 17 '26, summary)

 

ASSESSMENT | 17 June 2026

Key findings 

    • So far in the current outbreak of Ebola disease caused by Bundibugyo virus, international modelling efforts have focused on estimating the outbreak size and near-term trajectories, as well as the risk of regional and international spread.  

    • Multiple modelling groups suggest that the true size of the outbreak is larger than reported. 

        - One model estimated that cumulative infections as of 13 June were between 3.0 and 10.2 times the reported number of cases (90% credible interval). 

    • Epistorm estimated the relative risk of importation to be highest for Rwanda, Tanzania and Kenya, which together account for approximately 54% of the relative risk. 

        - ECDC has estimated the risk of importation into the EU/EEA to be low. 

    • The United States Centers for Disease Control and Prevention published scenario modelling analysis results that estimated a 65% probability that the outbreak will exceed 20 000 cases within three months under a scenario where 20% of individuals with Bundibugyo virus infection were isolated and no other interventions were implemented. 

    • Current modelling estimates are highly uncertain due to data limitations. 

        - Multiple epidemic trajectories remain compatible with the available surveillance data, limiting confidence in estimates of outbreak size and future trends. 

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Suggested citation: European Centre for Disease Prevention and Control. Overview of available modelling evidence to inform the scale and potential spread of Bundibugyo virus in the current Ebola disease outbreak. ECDC: Stockholm; 2026.   ISBN 978-92-9498-899-7; doi: 10.2900/3614787; Catalogue number TQ-01-26-044-EN-N 

© European Centre for Disease Prevention and Control, Stockholm, 2026

(...)

Source: 

Link: https://www.ecdc.europa.eu/en/publications-data/overview-available-modelling-evidence-inform-scale-and-potential-spread

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Modeled #Scenario #Projections for the #Ebola Disease #Outbreak Caused by #Bundibugyo Virus, 2026 (MMWR)

 

Summary

    -- What is already known about this topic?

        ° An outbreak of Bundibugyo virus disease (BVD), a type of Ebola disease, is currently ongoing, centered in the Ituri province of the Democratic Republic of the Congo (DRC).

    -- What is added by this report?

        ° CDC used a transmission model to project outbreak growth over 3 months, by using different assumptions about the number of deaths as of May 24, 2026, and by varying the percentages of persons with BVD who are successfully identified and isolated to prevent ongoing transmission. Assuming 50 cumulative deaths as of May 24, 2026, if 70% of patients were to enter isolation, only approximately one in 20 simulations projected an outbreak exceeding 10,000 cases within 3 months.

    -- What are the implications for public health practice?

        ° Large-scale, rapid public health action is needed to control the current outbreak, already the largest known BVD outbreak, from becoming one of the largest Ebola epidemics in history.

Abstract

On May 15, 2026, the Ministries of Health in the Democratic Republic of the Congo and Uganda declared outbreaks of Bundibugyo virus disease (BVD), a type of Ebola disease. In response to reports of high numbers of suspected cases and deaths in these outbreaks, CDC simulated scenario projections to understand possible future morbidity and mortality. A branching process model with the capacity to model transmission-reducing nonpharmaceutical interventions was calibrated to three putative cumulative death counts and projected for four possible intervention scenarios ranging from poor (20%) to extremely high (95%) levels of isolation and treatment of symptomatic persons. The analysis suggested a plausible spillover event (i.e., the transmission of a virus from its natural animal reservoir to humans) in mid to late February 2026. With poor isolation levels of patients with BVD (20%) and no other interventions, the likelihood of an outbreak that exceeds 20,000 cases within 3 months is 65%. If, however a high proportion of patients were to enter isolation (70%), only a one in 20 chance is projected for an outbreak with ≥10,000 cases within 3 months. These results underscore the importance of strong public health interventions, because the current outbreak is already the largest known BVD outbreak and has the potential to quickly become one of the largest Ebola disease outbreaks ever recorded.

Source: 

Link: https://www.cdc.gov/mmwr/volumes/75/wr/mm7522e1.htm?s_cid=mm7522e1_e&ACSTrackingID=USCDC_921-DM155686&ACSTrackingLabel=Early%20Release%20%E2%80%93%20Vol.%2075%2C%20June%205%2C%202026&deliveryName=USCDC_921-DM155686

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Predicting #Influenza Virus #Host #Tropism and Zoonotic #Spillover #Risk from #Protein Sequences

 

Abstract

Novel infectious diseases, predominantly originating from non-human animals, pose a significant threat to global public health and economic stability. Avian influenza virus presents an especially significant challenge due to its high mortality rates and spillover capability into new host species. Recent H5N1 spillover events into poultry and cattle resulted in massive economic burden and increased human health risk. Traditional methods of disease surveillance rely on reactive case detection and pathogen characterization, providing insufficient lead time for effective intervention. Computational tools that allow efficient and proactive prediction of zoonotic potential are critical in mitigation of influenza outbreaks and identification of strains with human spillover risk. Existing models predicting influenza virus subtypes or host have been developed; however, the complexity of spillover events, including the non-binary nature of zoonotic potential, limits the capabilities of these models. In the approach reported here, rich protein language model embeddings were generated from ESM-2 for each protein in influenza virus strains and used to predict the protein host tropism probabilities across nine animal families. The protein host tropism model achieved weighted precision and recall scores of 0.95 and 0.95, respectively. We then constructed a zoonotic risk prediction model using the outputs from the protein host tropism prediction model to classify the strains into six classifications: avian, mammal, human, avian-to-human zoonotic, avian-to-mammal zoonotic, or mammal-to-human zoonotic. The average weighted precision and recall scores for this model were 0.90 and 0.90, respectively. This framework advances the prediction of influenza zoonotic risk by being agnostic to influenza subtype, incorporating non-human mammals and mammal zoonotic spillover classifications, and using the full influenza proteome to capture the complexity of spillover dynamics.

Competing Interest Statement

The authors have declared no competing interest.

Source: 

Link: https://www.biorxiv.org/content/10.64898/2026.05.21.726772v1

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Spatiotemporal #Dynamics of Highly Pathogenic Avian #Influenza #H5 Virus Introductions and Regional Spread in the Republic of #Korea

 

Abstract

Highly pathogenic avian influenza (HPAI) viruses from clade 2.3.4.4 have caused recurrent outbreaks in poultry since 2014. In the Republic of Korea, clade 2.3.4.4b viruses have driven five epidemic waves, yet the factors underlying HPAI introduction and farm-to-farm spread remain poorly understood. We compiled hemagglutinin gene sequences of clade 2.3.4.4b viruses from wild birds and poultry in the Republic of Korea (October 2016–March 2024) and reconstructed dispersal dynamics using Bayesian phylogeography. Dispersal patterns suggest that domestic duck farms in the western provinces likely form a key interface for spillover from wild birds into poultry. Mixed-effects generalized linear models showed that both wild-to-poultry and farm-to-farm transition rates were positively associated with the number of poultry farms in the destination province, while wild-to-poultry rates were further associated with higher avian influenza virus infection probability among wild birds. Wild-to-poultry transition rates were lower in 2020–2024 than in 2016–2018, which may reflect strengthened interventions. These findings suggest that poultry farm abundance and introduction pressure from wild birds jointly shape the spatial dynamics of HPAI introduction and spread. More broadly, these factors may provide operational indicators to guide risk-based surveillance and control strategies.

Competing Interest Statement

The authors have declared no competing interest.

Source: 

Link: https://www.biorxiv.org/content/10.64898/2026.05.21.726857v1

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Computational Structural Analysis Predicts #Host-Range Promiscuity and #Antiviral #Resistance in North #American #H5N1 Lineages

 

Abstract

Influenza A virus has been circulating in birds in Eurasia for more than 146 years, but human infection has been sporadic. H5N1 (clade 2.3.4.4b) has recently infected hundreds of species of wild and domestic birds and mammals in North America. Infections include 71 people in the United States. There have been 2 human fatalities (United States and Mexico). We have integrated time-series analysis, molecular phylogenetics, and structural biology to understand how H5N1 is circulating in North America and adapting to new hosts. Our time-series analysis reveals that the circulation of H5N1 follows a distinct seasonal pattern, with cases in the United States increasing November to April. We also document an increase in the number of cases reported since 2021. We show that H5N1 spreads in North America as 2 distinct lineages. These viral lineages have achieved a vast host range by efficiently binding the viral surface protein hemagglutinin to both mammalian and avian cell surface receptors. This novel host-range promiscuity is concomitant with the strengthening of the viral polymerase basic 2 protein binding for mammalian and avian immune proteins. Once bound, the immune proteins have diminished ability to fight the virus, thus allowing for efficient replication. Our analyses predict that while most antivirals remain effective, a fatal human isolate showed reduced binding to multiple drugs from different classes. The H5N1 virus is causing an animal pandemic through promiscuity of host range and strengthening ability to evade the innate immune systems of both mammalian and avian cells.

Source: 

Link: https://spj.science.org/doi/10.34133/csbj.0066

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Mechanistic #modelling of highly pathogenic avian #influenza: A scoping #review revealing critical gaps in cross-species #transmission models

 

Abstract

Background

Highly pathogenic avian influenza (HPAI) viruses, particularly subtypes such as H5N1 and H7N9, have caused widespread outbreaks in wild birds, poultry, livestock and occasionally humans, raising concerns about cross-species transmission and pandemic potential. Effective control and surveillance strategies require a thorough understanding of HPAI transmission dynamics, which can be supported by mathematical modelling.

Objective

This scoping review aimed to identify mechanistic models used to study HPAI transmission. Specifically, we sought to categorize model types, describe their application contexts (e.g., wild birds, poultry, livestock, and humans), and highlight modelling gaps relevant to understanding and mitigating the risks of HPAI spread.

Methods

Following PRISMA guidelines and the PRISMA extension for scoping reviews (PRISMA-ScR), we conducted systematic searches of PubMed and Web of Science to identify peer-reviewed studies employing deterministic and stochastic models to analyze HPAI transmission. Eligible articles published between January 2023 and June 2025 were screened and grouped by model structure, host populations, transmission pathways, and modelling objectives.

Results

After screening, 30 studies published after 2023 were included in this scoping review. Compartmental models were the most common (26 studies), with 16 deterministic and 10 stochastic approaches. These models were primarily used to describe transmission among wild birds, poultry, livestock, and humans and to evaluate interventions such as culling, vaccination, and movement restrictions. Agent-based models (2 studies) captured individual-level interactions and spatial heterogeneity, while network models (2 studies) represented contact structures and transmission pathways between farms or species.

Conclusions

Currently, mechanistic modelling of HPAI is dominated by compartmental approaches, including both deterministic and stochastic formulations, whereas agent-based and network models remain relatively underused. Although most studies focus on transmission in wild birds and poultry, and in some cases spillover infections to humans, few explicitly examine infection dynamics in livestock or in transmission between livestock and humans, despite the importance of livestock (e.g., cattle) as potential intermediaries in human infection. Key gaps persist in the integration of empirical data, representation of multi-host interactions, and evaluation of realistic intervention strategies. Addressing these limitations is essential to improve predictive accuracy and to strengthen the role of modelling in informing HPAI surveillance and control.

Source: 

Link: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0347929

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Characterizing #viral #clearance kinetics in acute #influenza

 

Abstract

Pharmacometric assessment of antiviral efficacy in acute influenza informs treatment decisions and pandemic preparedness. We characterized natural viral clearance in acute influenza to guide phase II trial design using simulations based upon observed data. Standardized duplicate oropharyngeal swabs were collected daily over 14 days from 80 untreated low-risk Thai adults, with viral densities measured using quantitative polymerase chain reaction. We evaluated three models to describe viral clearance: exponential, bi-exponential and growth-and-decay. The growth-and-decay model provided the best fit, but the exponential decay model was the most parsimonious. The median viral clearance half-life was 10.3 h (interquartile range (IQR): 6.8–15.4h), varying by influenza type: 9.6 h (IQR: 6.2–13.0 h) for influenza A and 14.0 h (IQR: 10.3–19.3 h) for influenza B. Simulated trials using parameters from the exponential decay model showed that 148 patients per arm provide over 90% power to detect treatments accelerating viral clearance by 40%. Variation in clearance rates strongly impacted the power; doubling this variation would require 232 patients per arm for an antiviral with a 60% effect size. A sampling strategy with four swabs per day reduces the required sample size to 81 per arm while maintaining over 80% power. We recommend this approach to assess and compare current anti-influenza drugs.

This article is part of the Theo Murphy meeting issue ‘Evaluating anti-infective drugs’.

Source: 

Link: https://royalsocietypublishing.org/rstb/article/381/1949/20240351/481559/Characterizing-viral-clearance-kinetics-in-acute

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Seasonal forcing and waning #immunity drive the sub-annual periodicity of the #COVID19 #epidemic

 

Abstract

Seasonal trends in infectious diseases are shaped by climatic and social factors, with many respiratory viruses peaking in winter. However, the seasonality of COVID-19 remains in dispute, with significant waves of cases across the United States occurring in both winter and summer. Using wavelet analysis of COVID-19 cases during the pandemic period, we find that the periodicity of epidemic COVID-19 varies markedly across the U.S. and correlates with winter temperatures, indicating seasonal forcing. However, seasonal forcing alone cannot explain the pattern of multiple waves per year that has been so characteristic of COVID-19. Using a modified SIRS model that allows specification of the tempo of waning immunity, we show that specific forms of non-durable immunity can sufficiently explain the sub-annual waves characteristic of the COVID-19 epidemic.

Source: 

Link: https://journals.plos.org/plospathogens/article?id=10.1371/journal.ppat.1014169

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Robustly Quantifying #Uncertainty in #International Avian #Influenza #H5N1 Infection #Fatality Ratios

 

Abstract

Knowing the mortality rates associated with infection by a pathogen is essential for effective preparedness and response. Here, harnessing the flexibility of a Bayesian approach, we produce an estimate of the Infection Fatality Ratio (IFR) for A(H5N1) conditional on explicit assumptions, and quantify the uncertainty thereof. We also apply the method to first-wave COVID-19 data up to March 2020, demonstrating the estimates that could be obtained were the model available then. Our analysis uses World Development Indicators (WDI) from the World Bank, the A(H5N1) WHO confirmed cases and deaths tracker by country (2003-2024), and COVID-19 cases and deaths data from John Hopkins University (January and February 2020). Since infectious disease dynamics are typically influenced by local socio-economic factors rather than political borders, individual countries are placed within clusters of countries sharing similar WDIs relevant to respiratory viral diseases, with clusters derived by performing Hierarchical Clustering. To estimate the IFR, we fit a Negative Binomial Bayesian Hierarchical Model for A(H5N1) and COVID-19 separately. We explicitly modelled key unobserved parameters with informative priors from expert opinion and literature. By modelling underreporting, our analysis suggests lower fatality (15.3%) compared to WHO's Case Fatality Ratio estimate (54%) on lab-confirmed cases. However, credible intervals are wide ([0.5%, 64.2%] 95% CrI). Therefore, good preparedness for a potential A(H5N1) pandemic implies adopting scenario planning under our central estimate, as well as for IFRs as high as 70%. Our approach also returns a COVID-19 IFR estimate of 2.8% with [2.5%, 3.1%] 95% CrI which is consistent with literature.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

MKA is supported by the Schlumberger Foundation Faculty for the Future. TH is supported by the Wellcome Trust (Ref: 227438/Z/23/Z) and Medical Research Council (Ref: UKRI483). LG, MN, TF are employed by UKHSA. The research leading to these results received UK Government grant-in-aid funding to UKHSA. The views expressed in this publication are those of the authors and not necessarily those of UKHSA or Department for Health and Social Care. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Source: 

Link: https://www.medrxiv.org/content/10.64898/2026.04.22.26351373v1

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Using an evolutionary epidemiological #model of #pandemics to estimate the #infection #fatality ratio for #humans infected with avian #influenza viruses

 

Abstract

The risk of highly pathogenic avian influenza virus infection to humans is challenging to estimate as many human avian influenza virus (AIV) infections are undetected because infections may be asymptomatic, symptomatic but not tested, and difficult to identify through contact tracing, as human-to-human transmission is rare. We derive equations that consider the evolutionary mechanisms that give rise to pandemics and are parameterized to be consistent with records of past pandemics. We estimate that thousands of human AIV infections occur worldwide in an average year and estimate the infection fatality ratio as 32 deaths per 10,000 infections (95% confidence interval: [9.6, 75]). This estimate is comparable to SARS-CoV-2 during the recent pandemic and higher than seasonal human influenza. We estimate that preventing animal-to-human influenza spillovers would delay pandemic emergence by several years. Preventing human infections with AIV is necessary given the high risk of severe outcomes to individuals and to reduce the risk of pandemics occurring in the future.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

AH was supported by a Natural Sciences and Engineering Research Council of Canada Discovery Grant (RGPIN 023-05905) and a Catalyst Grant: Avian Influenza OneHealth Research, Enhanced tracking of the circulation of and risk from highly pathogenic avian influenza viruses at the human-wildlife interface from the Canadian Institutes of Health Research. JM, ML, and AH were support by an Atlantic Canada Research in the Mathematical Sciences Collaborative Research Group award.

Source: 

Link: https://www.medrxiv.org/content/10.64898/2026.01.21.26344526v2

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#Online monitoring and early #detection of #influenza #outbreaks using exponentially weighted spatial lasso: a case study in #China during 2014–2020

 

Abstract

Influenza poses a persistent public health threat in China, with substantial impacts on health and the economy, especially during seasonal epidemics and emerging outbreaks. Seasonality, local clustering, and serial correlation inherent in influenza data introduce spatio-temporal complexities that traditional statistical process control (SPC) methods cannot adequately capture. This study introduces a novel nonparametric framework for real-time influenza monitoring across 300+ Chinese cities from 2014 to 2020. Reference periods are selected to establish baseline incidence patterns and fit a nonparametric spatio-temporal model to estimate mean and covariance structures. These estimates enable the setting of dynamic outbreak thresholds. Next, exponentially weighted spatial LASSO (EWSL) charting statistics are computed for the monitoring period, prioritizing recent observations and detecting subtle mean shifts in small, clustered regions - well-suited to influenza's progression dynamics. Charting statistics exceeding control limits trigger timely outbreak warnings. Results demonstrate that our method consistently outperforms alternative methods, and existing literature corroborates that its early signals correspond to actual outbreaks - including those for H7N9 strains, influenza A and B viruses, and the initial spread of COVID-19. These findings highlight the potential of our approach as an effective epidemic monitoring tool, addressing complex spatio-temporal patterns and supporting timely, data-driven public health interventions.

Source: 

Link: https://www.tandfonline.com/doi/full/10.1080/02664763.2025.2534915

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Predicting highly pathogenic avian #influenza #H5N1 #outbreak #risk using extreme #weather and bird #migration data in machine learning models

 

Abstract

Background. 

Climate change is intensifying extreme weather events (EWEs) with potentially profound consequences for zoonotic disease dynamics, yet the mechanisms linking EWEs to highly pathogenic avian influenza (HPAI) H5N1 outbreaks remain poorly characterized. The ongoing H5N1 panzootic, responsible for infection in over 500 avian and mammalian species, as well as nearly 1000 human cases and 477 deaths worldwide, provides a critical opportunity to evaluate how climate conditions shape spillover risk at landscape scales. 

Methods. 

We compiled a county-month dataset of confirmed H5N1 detections across the contiguous United States from 2022 to 2024 and integrated it with satellite-derived climate metrics, storm event data, and wild bird activity data. We trained and validated a gradient boosting machine classifier to predict outbreak risk and characterize predictor relationships. 

Results. 

Our model achieved strong discriminative performance (AUC-ROC = 0.856; AUC-PR = 0.237, representing a 7-fold improvement over chance) and high recall (0.726), supporting its utility as an early warning tool. Human population and temperature-related variables were the most influential predictors: cold temperature shocks and prolonged low temperatures were consistently associated with elevated outbreak risk, likely through enhanced environmental viral persistence, wild bird habitat compression, and allostatic stress-driven immunosuppression in reservoir hosts. Among storm variables, high wind coverage elevated risk, potentially via aerosol dispersal of contaminated particulates, while tornado activity showed an inverse relationship, consistent with documented avoidant behavior in migratory birds. Wild bird reservoir density showed a strong positive monotonic relationship with outbreak risk. 

Conclusions. 

Our analyses demonstrate that routinely available environmental and infection data can be used to predict HPAI outbreak risk at fine spatiotemporal scales. These findings demonstrate the divergent roles of short- versus long-term environmental exposures in HPAI spillover dynamics, as well as the potential for machine learning-based surveillance tools to inform targeted biosecurity interventions and early warning systems.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This research was supported by a subaward agreement between prime award recipient Boston University (PI: Gregory Wellenius) and the subaward recipient Regents of the University of Colorado (PI: Elise Grover) under the National Institute of Environmental Health Sciences of the National Institutes of Health, Award Number U24ES035309 -01. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Source: 

Link: https://www.medrxiv.org/content/10.64898/2026.03.30.26349797v1

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#AI - guided multi-omics #analysis identifies NPC1-modulated susceptibility to #SARS-CoV-2 #infection under #PM2.5 exposure

 

Abstract

Exposure to airborne fine particulate matter (PM2.5) has been linked to increased risk of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, yet the underlying mechanisms remain unclear. Here, by leveraging a fine-tuned foundation model of single-cell transcriptomics, we uncover shared transcriptional signatures between PM2.5 exposure and SARS-CoV-2 infection. We further validate this association using population-level epidemiological analyses and perform genome-wide association studies (GWAS) to identify genetic variants that modulate infection risk under PM2.5 exposure. In addition, we identify NPC1 as a key modulator involved in SARS-CoV-2 infection efficiency under virus-laden PM2.5 exposure through integrative functional genomic analyses and in vitro experiments. Our findings suggest that PM2.5 facilitates viral entry through an NPC1-modulated endo-lysosomal pathway, providing a mechanistic explanation for observed pollution-related susceptibility. By integrating artificial intelligence (AI)-guided transcriptomics, epidemiology, GWAS, functional genomics, and in vitro verification, our study elucidates how environmental and genetic factors jointly influence SARS-CoV-2 susceptibility. This work highlights how AI-assisted multi-omics integration systematically decodes the health impacts of environmental exposures from molecular to population levels and informs air quality policy and infectious disease preparedness.

Source: 

Link: https://www.nature.com/articles/s41467-026-71196-3

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Behavioural determinants of #testing behaviour during a hypothetical avian #influenza #outbreak: an interview study

 

Abstract

Background: 

Avian Influenza (AI) is a potential pandemic threat, specifically when human-to-human transmission occurs. For outbreak management testing is essential. Current knowledge on testing behaviour is mostly derived from other infectious diseases such as COVID-19. It is necessary to identify determinants of testing behaviour for AI in an early phase. Therefore, this interview study aims to identify a wide range of behavioural determinants of testing during a hypothetical human-to-human transmissible AI outbreak. 

Methods: 

Semi-structured in-depth interviews, based on the Theoretical Domains Framework, were carried out between May 2024 and February 2025. Participants were included through purposive and convenience sampling. During the interviews an animation was shown illustrating a hypothetical AI outbreak. Verbatim transcripts were thematically analysed. 

Results: 

We included seventeen participants (median age 44, range 20-81; 71% women) with diverse backgrounds in terms of age, gender, educational level and country of birth. We found that having the freedom to decide to test would make testing more acceptable, whereas a decreased sense of autonomy would discourage testing. Most themes included individual rather than population-level benefits as drivers of testing behaviour. These included protecting loved ones, one's own health and gaining psychological reassurance. External conditions like being unable to go to work or an event would generally encourage testing behaviour. Lower trust in governmental authorities could hamper testing behaviour. Previous experiences from the COVID-19 pandemic shaped the participants' answers about AI testing behaviour. 

Conclusion: 

Key considerations include balancing people's need for autonomy with the external measures imposed by employers or the government, rebuilding trust in institutions and acknowledging how prior experiences with testing may shape testing behaviour in future AI outbreaks. Further research is needed to determine how these findings can be translated into effective communication and how trust in authorities can be build.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This study was supported by ZonMw (projectnumber 10710032310014) an organisation that stimulates innovations to improve healthcare in the Netherlands. The funder had no involvement in study design, data collection, analysis, interpretation of the findings or in manuscript preparation.

Source: 

Link: https://www.medrxiv.org/content/10.64898/2026.03.17.26348610v1

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An #outbreak of highly pathogenic avian #influenza #H5N1 could impact the dairy #cattle sector and the broader #economy in the #USA

 

Abstract

The outbreak of Highly Pathogenic Avian Influenza H5N1 in U.S. dairy cattle poses substantial risks to public health, economic sustainability of farming, and global food systems. Using a Computable General Equilibrium model, we simulate its short- to medium-term impacts on Gross Domestic Product and other macro-economic outcomes for the US and its main trading partners. We simulate impacts under the current situation and realistic and reasonable worst-case scenarios. We estimate domestic economic losses ranging between 0.06% and 0.9% of US GDP, with losses to the dairy sector ranging between 3.4% and 20.6%. Trading partners increase dairy production to compensate for the loss. Current government subsidies are about 1.2% (95% HDI: 1% to 1.4%) of output losses, and likely insufficient to incentivise farmers to step up surveillance and biosecurity for mitigating the possible emergence of H5N1 strains with pandemic potential into human populations.

Source: 

Link: https://www.nature.com/articles/s43247-025-03153-9

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Using an evolutionary #epidemiological #model of #pandemics to estimate the #infection #fatality ratio for #humans infected with avian #influenza viruses

 

Abstract

The risk of highly pathogenic avian influenza infection to humans is challenging to estimate because many human avian influenza virus (AIV) infections are undetected as they may be asymptomatic, symptomatic but not tested, and as contact tracing is difficult because human-to-human spread is rare. We derive equations that consider the evolutionary mechanisms that give rise to pandemics and are parameterized to be consistent with records of past pandemics. We estimate that thousands of human AIV infections occur worldwide in an average year and estimate the infection fatality ratio as 32 deaths per 10,000 infections (95% confidence interval: [9.6, 75]). We estimate that preventing 20% of animal-to-human influenza spillovers annually would delay pandemic emergence by an average of 9.4 years. There is a high level of uncertainty in our estimates due to the few records of past pandemics, but even so this infection fatality ratio is comparable to SARS-CoV-2 during the recent pandemic and is higher than seasonal human influenza. Preventing human infections with AIV is necessary given the high risk of severe outcomes to individuals and to reduce the risk of pandemics occurring in the future.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

AH was supported by a Natural Sciences and Engineering Research Council of Canada Discovery Grant (RGPIN 023-05905) and a Catalyst Grant: Avian Influenza OneHealth Research, Enhanced tracking of the circulation of and risk from highly pathogenic avian influenza viruses at the human-wildlife interface from the Canadian Institutes of Health Research. JM, ML, and AH were support by an Atlantic Canada Research in the Mathematical Sciences Collaborative Research Group award.

Source: 

Link: https://www.medrxiv.org/content/10.64898/2026.01.21.26344526v1

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Quantifying #H5N1 #outbreak #potential and #control effectiveness in high-risk agricultural populations

 

Abstract

Avian influenza is a global public health threat. Since 2021, the ongoing H5N1 panzootic has brought a major shift in H5Nx epidemiology, including unprecedented spread, wide host range and lack of seasonality. Infections in marine mammals, wildlife and livestock have heightened concern for human-to-human transmission and pandemic potential. Contact tracing and self-isolation are used as public health measures in the UK to manage contacts of confirmed human cases of avian influenza. In this study, we aimed to estimate potential outbreak sizes and evaluate the effectiveness of contact tracing and self-isolation in managing community outbreaks of H5N1 following spillover from birds to people. We characterised contact patterns from an underrepresented agricultural population at high risk of avian influenza exposure through contact with birds (Avian Contact Study). Informed by these realistic social contact data, we modelled outbreak sizes using a stochastic branching process model. Most simulations resulted in small-scale outbreaks, ranging from 0 to 10 cases. When the basic reproduction number was 1.1, contact tracing and self-isolation reduced the average outbreak size from 41 cases (95% Confidence Interval (CI): 37–46 cases) to 7 cases (95% CI: 6–8 cases), preventing, on average, 8 out of every 10 infections. However, controls became less effective in reducing the outbreak size when a higher proportion of cases were asymptomatic. Overall, our findings suggest that contact tracing and self-isolation can be effective at preventing zoonotic infections. Increasing awareness, encouraging self-isolation, and detecting asymptomatic cases through routine surveillance are important components of zoonotic infection containment strategies.

Source: 

Link: https://journals.plos.org/globalpublichealth/article?id=10.1371/journal.pgph.0005463

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The #epidemiology of #pathogens with #pandemic potential: A review of key #parameters and clustering analysis

 

Highlights

• Epidemiological parameters differ by pathogen and by setting.

• Unsupervised machine learning classifies pathogens into distinct epidemiological archetypes.

• Pathogens can be allocated into defined groups outlining plausible parameter ranges across epidemiologically similar pathogens.

Abstract

Introduction

In the light of the COVID-19 pandemic many countries are trying to widen their pandemic planning from its traditional focus on influenza. However, it is impossible to draw up detailed plans for every pathogen with epidemic potential. We set out to try to simplify this process by reviewing the epidemiology of a range of pathogens with pandemic potential and seeing whether they fall into groups with shared epidemiological traits.

Methods

We reviewed the epidemiological characteristics of 19 different pathogens with pandemic potential (those on the WHO priority list of pathogens, different strains of influenza and Mpox). We extracted data on key parameters (reproduction number serial interval, proportion of presymptomatic transmission, case fatality risk and transmission route) and applied an unsupervised learning algorithm. This combined Monte Carlo sampling with ensemble clustering to classify pathogens into distinct epidemiological archetypes based on their shared characteristics.

Results

From 154 articles we extracted 302 epidemiological parameter estimates. The clustering algorithms categorise these pathogens into six archetypes (1) highly transmissible Coronaviruses, (2) moderately transmissible Coronaviruses, (3) high-severity contact and zoonotic pathogens, (4) Influenza viruses (5) MERS-CoV-like and (6) MPV-like.

Conclusion

Unsupervised learning on epidemiological data can be used to define distinct pathogen archetypes. This method offers a valuable framework to allocate emerging and novel pathogens into defined groups to evaluate common approaches for their control.

Source: 

Link: https://www.sciencedirect.com/science/article/pii/S1755436525000702?via%3Dihub

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#Ecology and #environment predict spatially stratified #risk of #H5 highly pathogenic avian #influenza clade 2.3.4.4b in wild #birds across #Europe

 

Abstract

Highly pathogenic avian influenza (HPAI) represents a threat to animal and human health, with the ongoing H5N1 outbreak within the H5 2.3.4.4b clade being one of the largest on record. However, it remains unclear what factors have contributed to its intercontinental spread. We use Bayesian additive regression trees, a machine learning method designed for probabilistic modelling of complex nonlinear phenomena, to construct species distribution models (SDMs) for HPAI clade 2.3.4.4b presence. We identify factors driving geospatial patterns of infection and project risk distributions across Europe. Our models are time-stratified to capture both seasonal changes in risk and shifts in epidemiology associated with the succession of H5N6/H5N8 by H5N1 within the clade. While previous studies aimed to model HPAI presence from physical geography, we explicitly consider wild bird ecology by including estimates of bird species richness, abundance of specific taxa, and “abundance indices” describing total abundance of birds with high-risk behavioural traits. Our projections of HPAI clade 2.3.4.4b indicate a shift in persistent, year-round risk towards cold, low-lying regions of northwest Europe associated with H5N1. Methodologically, we demonstrate that while most variation in risk can be explained by climate and physical geography, adding host ecology is a valuable refinement to SDMs of HPAI.

Source: 

Link: https://www.nature.com/articles/s41598-025-30651-9

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The #epidemiology of #chikungunya virus in #Brazil and the potential #impact of #vaccines: a mathematical modelling study

 

Summary

Background

The first chikungunya virus (CHIKV) vaccine is now licensed in Brazil, the country that reports the most cases of CHIKV globally; however, the optimal use of the vaccine remains unclear owing to a poor understanding of CHIKV epidemiology and population immunity. We aimed to combine the distribution of cases and deaths reported since 2014 with seroprevalence studies to inform mathematical models that estimate the underlying rates of infection by state and year, and the underlying patterns of disease and death by age and sex.

Methods

We quantified the annual CHIKV infection and disease burden between 2014 and 2024 in each of the 27 federative units of Brazil using a mathematical model in a Bayesian framework that integrated serological surveys (n=12) and confirmed CHIKV disease cases (n=488 234) and CHIKV deaths (n=1719) reported between January, 2014, and September, 2024. Using this base, we estimated the potential impact of a vaccine over the period 2025–29 had the population been vaccinated before the 2025 season, evaluating different roll-out strategies.

Findings

We found that 18·3% (95% credible interval 16·5–20·3) of the Brazilian population has been infected since 2014, with the highest risk concentrated in the northeast and southeast. Overall, 1·13% (1·07–1·19) of infections were detected by surveillance systems, with an increasing probability of symptoms with age and greater risk of symptoms in females. Vaccinating 40% of the population older than 12 years (73 million doses), and assuming a vaccine efficacy of 70% against infection and 95% against disease, would avert up to 1·6 million (0·5–3) cases and 198 (61–359) deaths over the next 5 years.

Interpretation

Despite widespread circulation, most of Brazil remains susceptible to infection. CHIKV vaccination has the potential to substantially reduce disease burden.

Funding

CEPI.

Source: 

Link: https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(25)00605-X/fulltext?rss=yes

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