#Ring and #community #vaccination for #Bundibugyo virus #outbreak response: a stochastic network modelling study

 

Summary

Background

Vaccination with rVSV-ZEBOV is highly effective against Ebola virus, but protection against Bundibugyo virus (BDBV) is unproven. We evaluated the relative population impact and dose efficiency of a partially cross-protective hypothetical vaccine under operationally realistic constraints during a BDBV outbreak.

Methods

We developed a stochastic transmission model on a clustered household–community contact network with empirically realistic local structure, calibrated to 2026 DR Congo BDBV outbreak data. Time-varying effective reproduction numbers were estimated using a Bayesian renewal model. We evaluated case detection, isolation, contact tracing, reactive ring vaccination (Ring 1: direct contacts of the index case; Ring 2: contacts of contacts), and community vaccination (20–80% coverage). Base-case vaccine effectiveness was 45% and included post-exposure protection against disease and mortality. Primary outcomes were mortality and incidence reductions, total doses, and dose efficiency (doses per death averted) over 90 days, evaluated in a probabilistic sensitivity analysis with 10 000 matched stochastic replicates per strategy.

Findings

Compared with base operations alone (30% detection, 30% tracing), enhanced operations alone (70% detection, 80% tracing) reduced expected mortality by 81·6% (95% uncertainty interval 73·1–87·7). Reactive Ring 2 vaccination under base operations reduced mortality by 24·6% (18·0–29·6), requiring 35·1 doses per death averted. Added to enhanced operations, Ring 2 vaccination reduced mortality by 83·6% overall (76·4–89·0), an incremental benefit of 10·5% (6·2–15·6) beyond enhanced operations alone. Community vaccination at 20%, 40%, 60%, and 80% coverage reduced mortality by 44·7% (34·8–52·5), 67·4% (56·2–74·3), 79·8% (70·4–85·3), and 86·6% (79·2–90·4), respectively, requiring 53·8–111·4 doses per death averted.

Interpretation

Strengthened case finding, contact tracing, and isolation averted most deaths even without vaccination. Once these operations were strong, reactive ring vaccination added a modest further benefit, whereas rapid community vaccination produced the largest reductions in simulated scenarios but required substantially more doses. A partially protective BDBV vaccine's population-level value will depend principally on rapid, broad delivery.

Funding

Canadian Institutes of Health Research.

Translation

For the French translation of the abstract see Supplementary Materials section.

Source: 

Link: https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(26)00464-0/fulltext

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Evaluating the #impact of #antiviral post-exposure #prophylaxis for health-care workers during #orthoebolavirus outbreaks: a modelling study

 

Summary

Background

Orthoebolavirus outbreaks place health-care workers (HCWs) at substantial risk, and HCW illness or death can weaken response capacity. The 2026 Bundibugyo virus outbreak in DR Congo highlights the need for deployable countermeasures when species-specific vaccines are unavailable. With candidate antivirals under evaluation, we aimed to estimate the impact of HCW-targeted antiviral post-exposure prophylaxis (PEP) across different readiness, disruption, and allocation scenarios.

Methods

We adapted a previously published stochastic branching-process model of orthoebolavirus transmission, representing health care, community, and funeral transmission; time-varying non-pharmaceutical interventions; and HCW-targeted PEP. The model was calibrated to two historical outbreaks using sequential approximate Bayesian computation: the 2013–16 west Africa epidemic, to define a high-burden, reasonable worst-case scenario archetype (west Africa-like archetype); and the 2018–20 North Kivu and Ituri outbreak in eastern DR Congo, to define an archetype with longer transmission under conflict-related response disruption (DR Congo-like archetype). The primary outcome was HCW deaths averted. For both archetypes, we simulated three antiviral deployment readiness scenarios (scenario 1: 100% coverage on day 0; scenario 2: scaled up to 80% coverage over 180 days; and scenario 3: scaled up to 50% coverage over 1 year) and compared their impact on HCW deaths with a scenario of no antiviral. For the DR Congo-like archetype only, we simulated four disruption scenarios: no antiviral PEP, ideal delivery (100% coverage and no dosing delay), delayed dosing with coverage preserved, and delayed dosing with delayed coverage. As a secondary outcome, we assessed number of PEP doses required per HCW death averted under different allocation scenarios.

Findings

At baseline (no antiviral PEP), cumulative HCW deaths reached a median of 553 (IQR 208–983) in the west Africa-like archetype by week 60, compared with 61 (19–125) in the DR Congo-like archetype by week 80. Assuming 80% efficacy and 80% coverage with antiviral PEP in the same timeframe in a central analysis, cumulative HCW deaths fell to 200 (68–349; equivalent reduction of 64% [63–66] relative to baseline) in the west Africa-like archetype and 22 (10–44; equivalent reduction of 64% [60–68]) in the DR Congo-like archetype. Under different scenarios of deployment readiness at 80% antiviral efficacy, the median reduction in HCW deaths compared with no PEP was 80% (95% CrI 79–81) in the west Africa-like archetype and 80% (76–84) in the DR Congo-like archetype for scenario 1; 60% (57–62) and 52% (41–58), respectively, for scenario 2; and 19% (16–22) and 22% (7–29), respectively, for scenario 3. In the DR Congo-like operational disruption analyses, an ideal scenario (PEP delivered at 100% coverage without a delay after exposure) averted 83% (79–87) of HCW deaths compared with no antiviral; maintaining 100% coverage but introducing delayed dosing (1–5 days post-exposure) reduced this finding to 50% (40–55) compared with no antiviral. Delayed coverage and dosing resulted in only 35% (25–47) of the ideal scenario impact. At 80% antiviral efficacy with same-day dosing, targeted allocation of recognised high-risk exposures (such as personal protective equipment breaches or direct body-fluid contact) required 44 doses (95% Crl 43–44) per HCW death averted versus 109 doses (85–161) with broad allocation.

Interpretation

HCW-targeted antiviral PEP could substantially reduce HCW deaths during orthoebolavirus outbreaks if efficacious antivirals can be delivered rapidly and high operational coverage is maintained. Comparisons of antiviral use cases and alternative response investments are needed to determine how resources can best support outbreak response.

Funding

Gilead Sciences, UK National Institute for Health and Care Research, Oxford Martin School, Miller Institute, EU Global Health EDCTP3, and Coalition for Epidemic Preparedness Innovations.

Translations

For the French and Swahili translations of the abstract see Supplementary Materials section.

Source: 

Link: https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(26)00437-8/fulltext?rss=yes

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Predicting the #Risk of Avian #Influenza #Zoonosis using Viral #Genome Sequencing Data

 

Abstract

Avian Influenza viruses (AIVs) infect a broad host range despite having a natural reservoir in wild aquatic birds. Whilst most strains stay within their host species, some break the species barrier through genetic adaptations. We are most concerned about zoonotic cases, where a human becomes infected. Despite these events being rare, they are associated with high mortality and introduce the risk of onward human-to-human transmission of AIV. As a novel pathogen within the human population, this could have pandemic potential. Using genetic composition features for 8 AIV proteins drawn from viral sequence data, we employ machine-learning algorithms to classify AIV cases as zoonotic or not. These genetic features encode host 'signatures' which can indicate zoonosis and include frequency measures such as dipeptide composition and amino acid physiochemical properties. We consistently find XGBoost to outperform all other algorithms. We optimise parameters for ten classification models: one for each of the 8 proteins and two combined models. Following this, we show that a multi-model approach gives the best performing prediction for AIV zoonosis. We have identified all 8 proteins as having a role in predicting zoonotic transmission. Of particular importance is the PB2 and HA proteins, with specific amino acid physiochemical properties such as charge, secondary structure and hydrophobicity amongst the most indicative features in our combined models. Our alignment-free computational study can identify AIV cases still within avian hosts which are genetically closest to zoonotic AIV cases, thereby identifying the cases most likely to cross the species barrier. In a resource limited environment, our model could be used to quickly identify high priority cases for further investigation.

Competing Interest Statement

The authors have declared no competing interest.

Source: 

Link: https://www.biorxiv.org/content/10.64898/2026.08.21.746166v1

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Estimating the #infection #fatality #ratio of zoonotic avian #influenza viruses with #pandemic potential using an evolutionary epidemiological model

 

Abstract

The risk of zoonotic avian influenza (AIV) infection to humans is challenging to estimate as many human avian influenza virus 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 infections with AIVs possessing pandemic potential occur worldwide in an average year. Combining these estimates with H5N1 fatality data, we estimate a historical average infection fatality ratio of 32 (95% uncertainty interval: 9.6-75) deaths per 10,000 infections. 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 AIVs 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.

Source: 

Link: https://www.medrxiv.org/content/10.64898/2026.01.21.26344526v3

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HPAI #H5N1 #risk in #Australia: a model for the prediction of #poultry #outbreaks

 

Abstract

The panzootic highly pathogenic avian influenza (HPAI) H5N1 virus has now been detected on the Australian mainland, with incursions from the sub-Antarctic region posing an increasing threat to domestic wildlife and poultry populations. Our study aimed to predict the risk of HPAI H5N1 poultry outbreaks across Australia at the local government area (LGA) level using a range of influential risk factors. We first used a Maximum Entropy (MaxEnt) model to estimate the environmental suitability for HPAI H5N1 occurrence across Australia. The resulting suitability layer was then integrated with five additional predictor layers, including abundance data for two Southern Ocean wild birds, one of which has introduced HPAI H5N1 into Australia; abundance data for 28 native Australian wild birds; native bird flyways across Australia; Australian chicken density; and poultry farm density. The six layers were aggregated and averaged to generate an HPAI H5N1 risk map for poultry outbreaks across Australian LGAs. Although most incursions have occurred in Western Australia (WA) and South Australia (SA), we identified New South Wales (NSW) and Victoria (VIC) as having the highest predicted risk of HPAI H5N1 poultry outbreaks. Additional high-risk areas were identified in WA, SA, and Tasmania (TAS). In contrast, the Northern Territory (NT) and large parts of Queensland (QLD), WA, and SA were predicted to be at low risk. These findings provide a spatially explicit framework to support targeted surveillance, preparedness, and biosecurity measures aimed at mitigating the impact of future HPAI H5N1 outbreaks in Australian poultry.

Competing Interest Statement

CR MacIntyre is funded by NHMRC and Medical Research Futures Fund and is Founding Director of EPIWATCH Global Pty Ltd.

Funder Information Declared

NHMRC, CRM funded by NHMRC Investigator Grant 2016907

Source: 

Link: https://www.biorxiv.org/content/10.64898/2026.07.27.740638v1

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Identifying the viral and #epidemiological factors behind the apparent global #extinction of #influenza B/Yamagata

 

Abstract

Until 2020, two lineages of the influenza B virus had co-circulated globally. Measures to control the COVID-19 pandemic led to a near-absence of influenza infections. While B/Victoria reemerged in late 2021, there have been no reports of B/Yamagata since the pandemic. To investigate which epidemiological and immunological factors were primarily responsible for the extinction of B/Yamagata, we developed a global model for the two influenza B lineages. To mimic the transmission impacts of the pandemic, we implemented a transient reduction in contacts and identified parameter values that recapitulated viral coexistence dynamic before the pandemic and the qualitative post-pandemic outcomes of B/Victoria (reemergence in late 2021) and B/Yamagata (extinction). Our results suggest that, rather than immunological or evolutionary mechanisms, the extinction of B/Yamagata was mainly driven by its lower basic reproduction number making the virus particularly vulnerable during the early phase of the pandemic. Stochastic simulations of our best-fitting model suggest that B/Victoria was also close to extinction during this period. We investigate the model to assess the feasibility of B/Victoria eradication through vaccination and the potential for a sustained re-emergence of B/Yamagata in the 2026-27 flu season, thus highlighting important considerations for biosafety.

Competing Interest Statement

The authors have declared no competing interest.

Source: 

Link: https://www.medrxiv.org/content/10.64898/2026.07.22.26358639v1

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Pan-continental #spillover #risk: integrated spatiotemporal, transmissibility and #surveillance analysis of avian #influenza #H5N1 in #Africa

 

Abstract

Background: 

The HPAI H5N1 panzootic represents a critical threat to human health in Africa, where traditional poultry systems and dense human-animal interfaces facilitate frequent zoonotic spillover. While sporadic human cases raise pandemic concerns, continent-wide integration of spatial dynamics, transmissibility indicators, and surveillance performance has been lacking. This study quantifies avian influenza transmission over two decades across Africa, identifies geographical hotspots, and evaluates the responsiveness of current surveillance systems.

Methods: 

We analysed 8,037 avian influenza outbreak events and 369 laboratory-confirmed human cases, predominantly caused by HPAI H5N1 (2004–2025), using harmonised data from FAO (EMPRES-i+), WHO, and WOAH. A Bayesian Besag-York-Mollié (BYM) spatiotemporal model estimated residual transmission risks and Incidence Rate Ratios (IRR) by subtype. The basic reproduction number (R₀) was derived via an exponential growth model applied to human outbreak phases across infectious durations of 7–30 days. Surveillance responsiveness was assessed by quantifying notification delays between clinical observation and official reporting.

Results: 

Risk of infection in animals: HPAI H5N1 was the dominant strain, representing 87.8% of animal cases, with Egypt acting as the primary epidemiological epicentre (66% of total records). The spatiotemporal model revealed that H5N1 is associated with a significantly higher risk of animal infection (IRR = 8.37; 95% CI: 6.65–10.53). Although 71% of outbreaks were reported within 5 days of detection, significant delays (≥15 days) occurred in 12% of cases, with notable regional disparities. Risk of infection in human: H5N1 was associated with a 67-fold increase in the incidence of human cases compared to other subtypes (IRR = 66.78; 95% CI: 25.29–176.37). Sensitivity analyses yielded R0 estimates ranging from 1.05 (95% CI: 0.91–1.31) to 1.23 (95% CI: 0.60–2.33), indicating localised epidemic potential.

Conclusion: 

Our findings highlight a persistent and geographically heterogeneous H5N1 reservoir in Africa with high zoonotic affinity. Although sustained human-to-human transmission remains limited, the identification of dual poultry-human hotspots and localised R0 peaks underscores the urgent need for geographically targeted One Health interventions. Strengthening real-time reporting systems and improving biosecurity in high-risk poultry value chains are critical to mitigating future pandemic threats on the continent.

Source: 

Link: https://www.frontiersin.org/journals/epidemiology/articles/10.3389/fepid.2026.1813211/full

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#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. 

(...)

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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