#Geospatial #evidence of introduction of highly pathogenic avian #influenza into dairy #farms in the #USA

 

Abstract

The emergence of Highly Pathogenic Avian Influenza (HPAI) A/H5N1 clade 2.3.4.4b has led to unprecedented intercontinental spread spread across avian and mammalian species. First confirmed outbreaks in dairy farms happened in the United States in March 2024. This study evaluated the modes of introduction and spread across 42 confirmed dairy farm outbreaks spanning nine states between 10 March and 5 May 2024, through geospatial mapping, case-control ecological exposure analysis, and space-time cluster testing. Non-waterfowl abundance was significantly higher in outbreak counties than in high-cattle-density non-outbreak counties (p<0.00001, r=−0.542), identifying it as a distinguishing ecological feature of early dairy outbreak locations. Knox space-time cluster analysis confirmed non-random transmission dynamics across 14 of 30 threshold combinations, consistent with a single introduction in Texas followed by regional and trade-mediated amplification, with livestock trade, poultry litter, and contaminated milking equipment as likely contributing factors. Proactive surveillance targeting counties with high non-waterfowl abundance and cattle density is essential to reduce the risk of further spillover events.

Competing Interest Statement

The authors have declared no competing interest.

Source: 

Link: https://doi.org/10.1101/2024.05.02.24306785

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#Epidemiology of #MERS-CoV in #Saudi Arabia, 2012–2025: a nationwide surveillance analysis

 

Abstract

Background

Middle East respiratory syndrome (MERS), caused by MERS coronavirus (MERS-CoV), is a severe zoonotic respiratory illness first identified in Saudi Arabia in 2012. Dromedary camels are the recognised reservoir, with human transmission occurring through animal exposure and, less commonly, person-to-person spread, particularly in healthcare settings.

Objective

To characterise all laboratory-confirmed MERS-CoV cases reported in Saudi Arabia from September 2012 to August 2025, examine trends across four periods defined by suspected-case definitions, and identify factors independently associated with mortality.

Methods

We analysed laboratory-confirmed cases in the national surveillance database by period and probable infection source. Case-fatality ratios (CFRs), 95% confidence intervals (CIs), and unadjusted odds ratios were calculated. Multivariable logistic regression assessed independent mortality associations using a prespecified model including period, age, sex, and infection source, and a fuller model additionally considering nationality, comorbidities, and pregnancy. Seasonality of primary cases was assessed using an annual harmonic term.

Results

Among 2,219 cases, 1,545 (70%) were male, 1,566 (71%) Saudi nationals, and 608 (27%) aged ≥65 years. Overall, 1,211 (55%) were primary cases, including 172 (14%) reporting camel contact; 737 (33%) were healthcare-associated and 271 (12%) household- or community-associated. There were 867 deaths (CFR, 39%; 95% CI, 37%–41%). CFR declined from 65% in the earliest period to 32% in the most recent, although changing testing eligibility and ascertainment limit temporal comparisons. Older adults had the highest crude CFR. In the prespecified model, healthcare-associated infection among patients was associated with higher odds of death (aOR, 1.74; 95% CI, 1.15–2.65), while odds were lower among healthcare workers (aOR, 0.09; 95% CI, 0.05–0.17) and household contacts (aOR, 0.29; 95% CI, 0.18–0.48). No consistent seasonality was observed.

Conclusions

MERS-CoV epidemiology in Saudi Arabia reflects persistent zoonotic spillover, episodic healthcare-associated transmission, and substantial mortality, particularly among older adults. Sustained One Health surveillance, rapid detection, and rigorous infection prevention and control remain essential.

Source: 

Link: https://doi.org/10.1093/jtm/taag094

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Evaluating the effects of #population #mobility on #transmission during the #COVID19 #pandemic

 

Abstract

Changes to community mobility during the COVID-19 pandemic, together with unprecedented availability of surveillance and mobility data, provide a unique opportunity to quantify the effect of mobility on respiratory infection transmission. We present a renewal model that incorporates epidemiological mechanisms influencing SARS-CoV-2 transmission along with a non-mechanistic scaling process. We compare models with and without mobility using epidemic data from 122 countries and quantify the influence of mobility through reductions in non-mechanistic scaling. In several countries, this non-mechanistic process closely tracks mobility trends and varies substantially less when mobility is incorporated. This is especially evident in Western Europe and North America, but not in several countries of West Africa and the Arabian Peninsula. Google and Facebook data produce similar findings, and parks is frequently assigned the lowest weight in the multi-location Google analysis. Mobility trends are strongly aligned with short-term transmission dynamics in many high-income settings, although between-country heterogeneity remains.

Source: 

Link: https://doi.org/10.1038/s41467-026-78247-9

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Modelling #assessment of the different #paths of between-farm #transmission of avian #influenza HPAI in Northern #Italy in 2021--2022

 

Abstract

We propose a stochastic epidemic model to describe the spread of avian influenza in a network of poultry farms. We consider three main paths of infection transmission between poultry farms: from nearby farms, mainly through airborne diffusion; from farms belonging to the same company, through shared veterinarians, forage providers and similar; from undetected small farms or wildlife. From the parameter estimation, based on a modified Expectation-Maximization algorithm, we infer that approximately 63% of the farms were infected from nearby ones, with an infection force declining with distance; of these, more than one third belonged to the same company of the estimated infector. About 20% were infected from premises belonging to the same company but farther away than the distance threshold of 2 km; the remaining ones from wildlife or unidentified sources. These estimates have been validated by a comparison with genetic data, available for a subset of the farms: the genetic distance between two farms identified, with high probability, as an infector-infectee pair is much lower than between random pairs. These results may help in the implementation of tailored prevention and control measures for future outbreaks.

Competing Interest Statement

The authors have declared no competing interest.

Funder Information Declared

Fondazione Caritro (Cassa di Risparmio di Trento e Rovereto), Post-Doc 2024 project ``Modelli matematici di malattie infettive piu` ospiti e popolazioni eterogenee: applicazioni all'influenza aviaria''

Source: 

Link: https://doi.org/10.64898/2026.09.19.752832

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Field #Investigation of #Bundibugyo Virus Disease (BDBV) #Outbreak in Ituri Province, #DRC: ... An Outbreak Investigation Review

 

Abstract

Background: 

The 2026 outbreak of Bundibugyo Ebola virus disease (BDBV) in eastern Democratic Republic of the Congo (DRC), centered in Ituri Province, represents the largest documented outbreak caused by Bundibugyo ebolavirus since its discovery in Uganda in 2007. The outbreak evolved within a complex humanitarian setting characterized by armed conflict, population displacement, mining-related migration, weak health systems, extensive population mobility, and an infodemic environment marked by misinformation and reduced public trust. We conducted a field investigation to assess epidemiological, operational, laboratory, infection prevention and control (IPC), community engagement, risk communication, and infodemic management challenges and identify priority interventions to strengthen outbreak control. 

Methods: 

A rapid field assessment was conducted between 12–15 June 2026 in Bunia, Rwampara Health Zone, and the Ituri Provincial Public Health Laboratory. Data were collected through direct observation, review of surveillance and laboratory reports, health facility assessments, stakeholder interviews, and analysis of outbreak response indicators. Epidemiological trends, surveillance performance, laboratory capacity, clinical care, IPC activities, logistics, risk communication, community engagement, and infodemic management approaches were evaluated. 

Results: 

As of 12 July 2026, the outbreak had resulted in 1926 laboratory-confirmed cases and 702 deaths, corresponding to an overall case fatality rate (CFR) of 36.4% across affected provinces. Ituri Province remained the epicenter, accounting for 90.8% of confirmed cases (1705/1877) and 85.5% of reported deaths (577/675). During the preceding 24 h, 53 new confirmed cases and 30 deaths were reported, including 20 community deaths (66.7%), highlighting persistent delays in detection, referral, and access to care. Surveillance systems identified 766 alerts, of which 678 (88.5%) were investigated, resulting in 235 suspected cases. Contact tracing remained a major challenge, with only 64.4% (4171/6475) of registered contacts successfully followed, below the recommended ≥95% target. Laboratory activities included testing of 137 specimens, with 29 positive results and an overall positivity rate of 21.2%. Decentralized molecular diagnostic platforms improved access to testing; however, data inconsistencies, delayed investigations, and gaps in outcome classification affected response monitoring. Major operational challenges included limited treatment capacity, high occupancy of Ebola treatment centres, shortages of trained personnel and IPC supplies, insecurity affecting response teams, and insufficient preparedness in newly affected areas. Community resistance, attacks on burial teams, detention of frontline responders, misinformation, and rumors contributed to delayed care-seeking, reduced acceptance of public health measures, and incomplete cooperation with contact tracing. Risk communication and community engagement efforts were constrained by limited outreach capacity, language barriers, low trust, and inadequate systems for rumor detection and infodemic response. 

Conclusions: 

The ongoing BDBV outbreak in eastern DRC demonstrates the difficulty of controlling Ebola transmission in conflict-affected and socially complex settings. Sustained transmission, community deaths, geographic expansion, and operational constraints highlight the urgent need to strengthen surveillance, contact tracing, laboratory systems, IPC capacity, clinical care, and integrated risk communication and infodemic management strategies. Building trust through community-centered approaches, proactive misinformation management, and engagement of trusted local actors will be essential to accelerate outbreak containment and strengthen preparedness across the Great Lakes region.

Source: 

Link: https://doi.org/10.3390/idr18050100

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#Estimation of the #transmission #dynamics of #H5N1 HPAI #outbreak in a dairy #herd using a modeling approach

 

Abstract

The emergence of Highly Pathogenic Avian Influenza (HPAI 2.3.4.4b) in dairy herds in 2024 across 19 states in the United States of America has raised concerns regarding the potential national and global zoonotic impact. All recent modeling efforts implemented homogenous cattle-to-cattle (both intra and inter-herd) transmission models which did not capture the real-world heterogeneity in mixing of animals, individual variations in susceptibility and infectiousness and clinical incidences across pens and lactation groups. The aim of this study was to develop a heterogenous transmission model to estimate the epidemiological parameters for intra-herd HPAI transmission on Californian dairies. We developed a validated stochastic agent-based model to estimate the epidemiological parameters for intra-herd HPAI transmission on California dairies. The hierarchical agent-based model also parameterized stochastic cattle movements within-herd to simulate real dairy management practices. A cow-level SEIR transmission approach was assumed during the outbreak. A novel Bayesian Optimizer with Gaussian Process (BO-GP) was fitted to the agent-based model for validation which converged within 25-40 iterations (out of 100 per farm) with minimal loss over two distinct error metrics, namely, Poisson loss function and temporal distance metric. Our optimized simulations estimated an average R0 was around 10.7-10.8 across all farms within the first 15 days of observed outbreak on four dairy farms with a mean effective transmission rate of 4.5% per contact between susceptible and infectious cows within each pen. Our model demonstrated that the movement of cows between pens ensured localized clusters of outbreaks within the sub-herds (pen population) that prolonged the overall outbreak within farms. We estimated the total duration of infection between 14.5 and 28 days, which is higher than the estimates from the homogenous models. With an integrated hierarchical agent-based model combined with Bayesian approximation, we produced actionable insights on the epidemiology of intra-farm spread of HPAI within cow herds, thereby guiding both future model development and applied disease control strategy.

Competing Interest Statement

The authors have declared no competing interest.

Funder Information Declared

Animal and Plant Health Inspection Service (USDA-APHIS), AP25VSD&B000C007

Source: 

Link: https://doi.org/10.64898/2026.09.22.753543

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Estimated #Transmissibility and #CFR of #Bundibugyo Virus, #Uganda, 2007

 

Abstract

Because the epidemiology of Bundibugyo virus remains unclear, we reanalyzed the first recognized outbreak (Uganda, 2007). Adjusting for case under-ascertainment and the effect of control measures, we estimated the effective reproduction number (1.55, falling to <1 after intervention) and case-fatality rate (31%, declining to 25%). The underascertainment rate was 15%.

Source: 

Link: https://doi.org/10.3201/eid3210.261175

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The First 100 Days of Five #Ebola #Outbreaks — #DRC, #Uganda, and West #Africa, 2007–2026 (MMWR, Sept. 15 '26)

 

Summary

    ° What is already known about this topic?

        § The Democratic Republic of the Congo (DRC) is experiencing its largest and deadliest Ebola disease outbreak, which is also the largest Ebola outbreak caused by Bundibugyo virus and the second largest Ebola outbreak worldwide.

    ° What is added by this report?

        § The 2026 Ebola outbreak resulted in 5,458 confirmed cases and 2,606 deaths in DRC in the first 100 days after initial detection, indicating rapid growth. No previous Ebola outbreak caused >800 cases during the first 100 days.

    ° What are the implications for public health practice?

        § This Ebola outbreak appears to be expanding faster than any previously documented Ebola outbreak, with seven times as many cases 100 days after initial detection than the 2014 Ebola outbreak in West Africa, the largest outbreak worldwide. Urgent implementation of public health interventions to identify cases, trace contacts, and limit transmission are needed to bring the outbreak under control.

Abstract

The first 100 days after identification of an outbreak are important to understanding transmission dynamics, impact of early public health interventions, and trajectory of potential future cases and deaths. A large Ebola disease outbreak in the Democratic Republic of the Congo (DRC), which has become the country’s largest and deadliest, is ongoing. To better understand the current outbreak, this report compared metrics from the first 100 days of this outbreak with those of four past Ebola outbreaks. The historic outbreaks of Ebola disease selected for this comparison include the two largest (the 2014 outbreak in West Africa and a 2018 outbreak in DRC), and the only two previous outbreaks caused by Bundibugyo virus (the 2007 outbreak in Uganda and the 2012 outbreak in DRC). This activity was reviewed by CDC, deemed not research, and conducted consistent with applicable federal law and CDC policy.*

Source: 

Link: http://dx.doi.org/10.15585/mmwr.mm7537e1

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#Habitat-Mediated #Spillover #Risk of #Andes #Hantavirus in Oligoryzomys longicaudatus: A Mechanistic Eco-Epidemiological Model

 

Abstract

Andes hantavirus (ANDV) is a rodent-borne orthohantavirus associated with hantavirus cardiopulmonary syndrome in southern South America. Its maintenance and spillover risk depend on the ecology of its principal reservoir, Oligoryzomys longicaudatus, and on environmental changes that alter habitat availability, host abundance, and human–rodent interfaces. We developed a mechanistic eco-epidemiological model that couples effective habitat cover to an SIR framework for ANDV transmission in O. longicaudatus. Habitat degradation and compensatory restoration modify rodent carrying capacity, natality, and the force of infection, which depends on infected host load relative to instantaneous ecological capacity. We derived the habitat equilibrium, the basic reproduction number ℛ0, and the time-dependent effective reproduction number ℛ𝑒(𝑡) and evaluated infection-burden and threshold indicators across degradation-restoration scenarios. The  analysis shows that ℛ0 is independent of equilibrium habitat cover because susceptible abundance scales with carrying capacity at the disease-free  equilibrium. In contrast, ℛ𝑒(𝑡), cumulative incidence, and infected load depend on transient habitat-mediated crowding. Restoration increases reservoir abundance and absolute infection burden, whereas degradation can reduce abundance while increasing crowding-driven transmission pressure and prolonging supercritical windows. These results identify ecological conditions under which habitat change may intensify reservoir infection pressure and guide One Health surveillance at human–rodent interfaces.

Source: 

Link: https://www.mdpi.com/1999-4915/18/9/1024

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Characteristics and #Superspreading #Potential of #Andes Virus Person-to-Person #Transmission

 

Abstract

By using historical contact tracing data, we estimated that 23.4% of case-patients caused 80% of Andes virus (ANDV) person-to-person transmission. We demonstrated a low but nonnegligible probability of observing a large-scale ANDV infection outbreak in a rodent-free setting consisting of close contacts, despite the historically self-limited person-to-person transmission of ANDV.

Source: 

Link: https://doi.org/10.3201/eid3210.260908

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Modeling the #impact of respiratory disease #outbreaks on the #US #agricultural #workforce

 

Abstract

Background: 

Emerging respiratory disease outbreaks pose a major threat to food production systems. Agricultural workers face health disparities that amplify their susceptibility to respiratory pathogens, yet the consequences for worker health and food production remain poorly understood. 

Methods: 

We developed a household-structured susceptible-infectious-recovered (SIR) transmission model to compare disease dynamics between agricultural workers and the general U.S. population across six regions. We simulated outbreaks across a range of epidemiological scenarios and assessed productivity losses in California for three labor-intensive crops (oranges, iceberg lettuce, strawberries) with different harvest seasonalities. 

Results: 

We show that, for a baseline reproduction number of R0 = 2.0, the peak disease prevalence among agricultural workers is 1.41-1.69 times higher than that of the general population across regions, and final outbreak sizes are 1.29-1.42 times higher. Household structure accounts for 54%-68% of the disease disparity. Peak productivity losses range from 0.50%-0.63% across crops, translating to millions in lost potential revenue. At higher transmissibility and severity (R0 = 3 and assuming all infections are symptomatic), losses are 2.4 times higher. 

Conclusions: 

Household crowding may lead to disproportionate respiratory disease burden among agricultural workers that is exacerbated by vaccination and obesity disparities, highlighting the need for targeted outbreak preparedness and mitigation strategies in the agricultural sector to maintain food system resilience and support public health in these communities.

Competing Interest Statement

The authors have declared no competing interest.

Source: 

Link: https://www.medrxiv.org/content/10.64898/2026.03.31.26349871v2

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Operational #epidemiology of the early phase of the 2026 #Bundibugyo virus disease #outbreak in the #DRC and #Uganda

 

Abstract

The 2026 Bundibugyo virus disease outbreak in the Democratic Republic of the Congo (DRC) and Uganda occurred in a fragile cross-border setting marked by delayed recognition, diagnostic uncertainty, insecurity, population mobility, community mistrust, and healthcare-worker exposure. We conducted a retrospective operational epidemiology study using official situation reports, public health declarations, ministry statements, WHO and Africa CDC updates, cross-border communiqués, and selected partner statements. This analysis reflects publicly available data from the early outbreak period, with the primary DRC data lock on 27 May 2026 and Uganda and regional triangulation through 29 May 2026. We reconstructed the early timeline, described epidemiological and geographic progression, quantified response indicators, and applied a transparent health-zone prioritization framework. The earliest documented symptom onset was 24 April 2026. The Institut National de Recherche Biomédicale (INRB) detected non-Zaire ebolavirus in eight of 13 samples on 14 May, giving a 20-day symptom-onset-to-first-laboratory-detection interval; species-level Bundibugyo virus confirmation and official declaration followed on 15 May. By the DRC data lock on 27 May, 125 confirmed cases and 17 confirmed deaths had been reported, giving a confirmed case fatality ratio of 13.6%. DRC also reported 906 suspected cases and 223 suspected deaths, giving a suspected fatality proportion of 24.6%. Confirmed cases expanded from three to 13 health zones across three provinces within 12 days. Ituri province accounted for 110 confirmed cases (88.0%); Bunia, Rwampara, and Mongbwalu health zones together contributed 90 cases (72.0%). Uganda reported nine confirmed cases and one death by 29 May. Operational pressure included 2,635 listed contacts in DRC, 436 contacts under follow-up in Uganda, and 126 of 774 collected samples (16.3%) pending testing in DRC. Healthcare workers represented 16 of 125 DRC confirmed cases (12.8%) and at least three of nine Uganda cases (33.3%). Applying the health-zone prioritization framework, Rwampara, Mongbwalu, and Bunia were classified as higher priority health zones. Excluding confirmed burden did not change any health-zone tier; data classification confidence was high for 10 zones and moderate for three. Within this early operational snapshot, outbreak visibility reflected both transmission and operational factors, particularly detection delay, laboratory backlog, cross-border movement, healthcare-linked exposure, and community trust or security incidents. The framework provides a transparent basis for rapid decision support during an outbreak’s early phase by directing surveillance, laboratory, case-management, infection prevention and control, contact-tracing, and community-engagement resources toward health zones where multiple operational risks converge.

Source: 

Link:https://journals.plos.org/globalpublichealth/article?id=10.1371/journal.pgph.0006680

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#Influenza #H3N2 #epidemiology in #England during the 2025 to 2026 season: a mathematical modelling study

 

Abstract

Background

England experienced an unusually early and rapid increase in influenza A/H3N2 subclade K infections in 2025/26. Antigenic change and a fast selective sweep raised concerns over a potentially severe season. Building on analysis conducted as the subclade emerged, we aim to compare epidemic dynamics of the 2025/26 season to previous years and to model plausible epidemiological scenarios.

Methods

We compared peak epidemic growth rates and reproduction numbers across influenza seasons from 2011/12 to 2025/26 using routine surveillance data in England. Weekly epidemic growth rates were estimated using a Gaussian random walk model, and time-varying reproduction numbers using EpiEstim. We also developed an age-stratified transmission model and interactive web tool to explore scenarios varying immune escape, transmissibility, and seed date, using 2022/23 as a baseline season.

Results

Peak A/H3N2 growth rates and time-varying reproduction numbers for the 2025/26 season are of similar magnitude but earlier than previous severe seasons. Scenario analyses suggest early trends are compatible with moderate levels of immune escape, a 10% higher R0, or an earlier seed date, though it is not possible to distinguish the relative importance of these mechanisms from these data alone.

Conclusions

The 2025/26 influenza season is characterised by early but not unusually rapid growth. Earlier growth does not systematically lead to especially large epidemics due to earlier susceptible depletion combined with a dampening effect from school holidays. Laboratory evidence for antibody escape does not directly translate to large reductions in population immunity, supporting the need for complementary real-time epidemiological analyses and modelling.

Source: 

Link: https://www.nature.com/articles/s44528-026-00016-3

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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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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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Case - #Fatality #Risk of #Norovirus, #England, 2022–2025

 

Abstract

Norovirus incidence increased in England during 2022–2025, when GII.17 replaced GII.4 as the dominant genotype. By using nationally linked norovirus testing and fatality data, we found age and care setting, but not genotype, were associated with case-fatality risk. Increased incidence might reflect changes in transmissibility or population immunity.

Source: 

Link: https://wwwnc.cdc.gov/eid/article/32/8/26-0091_article

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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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A systematic #review of #Nipah virus disease epidemiological #parameters, #outbreaks, and mathematical #models

 

Summary

Our systematic review, based on PRISMA guidelines (PROSPERO CRD42023393345), characterised the epidemiology, outbreaks, and mathematical models of Nipah virus, an important public health threat in south and southeast Asia. We searched PubMed and Web of Science from database inception to March 14, 2025, and extracted 243 parameters, 89 risk factors, 39 models, and 23 distinct outbreaks from 119 papers. IgG seroprevalence estimates in the general population ranged from 0% to 12·5%. Nipah virus causes severe disease, with pooled case–fatality ratio estimates ranging widely from 9·1% (95% CI 0·2–41·3) in Singapore to 81·9% (95% CI 71·9–88·9) in Bangladesh. The infection timeline and clinical course of Nipah virus remain poorly characterised; we estimated a median incubation period of 8·77 days (165, 95% CI 7·53–10·02) from eight estimates in seven articles with sufficient information. Transmission parameter estimates were scarce, and all but one of five central estimates of the basic reproduction number were less than one. Nipah virus mathematical models (39) were rarely fitted to data (eight). All extracted information is accessible via our R package, epireview.

Source: 

Link: https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(26)00239-2/abstract?rss=yes

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Inter-population #connectivity of southern elephant #seals and the likely intra-species #transmission #pathways of high pathogenicity avian #influenza

 

Abstract

High Pathogenicity Avian Influenza (HPAI) H5N1 clade 2.3.4.4b has spread beyond birds to affect seals across the Southern Ocean and sub-Antarctic region, with southern elephant seals (Mirounga leonina) particularly devastated. The virus, likely introduced via spillover from infected migratory birds, has killed tens of thousands of adult seals and pups throughout most of their range, though Macquarie Island remains unaffected so far. We used twenty years of elephant seal movement data from the southern Indian and Pacific oceans to assess whether seal-to-seal transmission could spread HPAI H5N1 between breeding colonies, despite the vast distances separating them (Marion Island, Iles Crozet, Iles Kerguelen, and Macquarie Island). There was substantial overlap in seals' at-sea distributions during their winter post-moult trips, when seals travel for weeks at average speeds of 3.5 km/h. Two transmission pathways were examined: (1) terrestrial "stepping stone" routes, where infected seals could pass the virus between colonies during short intervals to remain infectious were feasible from Marion Island to Kerguelen but not from Kerguelen to Macquarie Island; and (2) at-sea encounters between seals, which occurred frequently enough to enable transmission. The findings suggest that once established at Macquarie Island, the virus could potentially spread further to New Zealand's sub-Antarctic islands and mainland New Zealand. While seal-to-seal transmission appears possible, we conclude this is unlikely. Nonetheless, understanding at-sea contact rates enhances knowledge of H5N1 epidemiology and demonstrates the value of combining long-term population monitoring with movement data to understand wildlife disease dynamics.

Competing Interest Statement

The authors have declared no competing interest.

Funder Information Declared

Integrated Marine Observing System, https://ror.org/010x3gp67

CNRS

Source: 

Link: https://www.biorxiv.org/content/10.64898/2026.07.07.737127v1

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Systemic #atopy and upper - #airway disease define susceptibility to incident #asthma after #COVID19 in #Korea

 

Abstract

Incident asthma is an important respiratory sequela after COVID-19, but it is unclear which allergic phenotypes amplify risk. Using a linked nationwide Korean database of 3,987,182 individuals with confirmed severe acute respiratory syndrome coronavirus 2 infection, we compare claims-based incident asthma in those with pre-existing systemic atopy and/or upper-airway disease (allergic rhinitis, chronic rhinosinusitis, atopic dermatitis or food allergy) versus those without after 1:1 propensity score matching. During follow-up to 31 December 2022, participants with pre-existing disease have higher asthma incidence than matched controls (3.55 vs 2.13 per 1,000 person-years), with a hazard ratio of 1.66 (95% confidence interval 1.58–1.75). Asthma risk is elevated for each condition and increases with greater disease burden. These findings show that pre-existing allergic and upper-airway phenotypes stratify post-COVID incident asthma risk on a national scale, supporting targeted surveillance in high-risk subgroups.

Source: 

Link: https://www.nature.com/articles/s41467-026-74860-w

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