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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Reconstructing the early spatial #spread of #pandemic respiratory #viruses in the #USA

 

Abstract

Understanding the geographic spread of emerging respiratory viruses is critical for pandemic preparedness, yet the early spatiotemporal dynamics of the 2009 H1N1 pandemic influenza and SARS-CoV-2 in the United States (US) remain unclear. While mobility and genomic data have revealed important aspects of pandemic spatial spread, several key questions remain: Did the two pandemics follow similar spatial transmission routes? How rapidly did they spread across the US? What role did stochastic processes play in early spatial transmission? To address these questions, we integrated high-resolution disease data with a robust, data-efficient inference framework combining air travel, commuting flows, and pathogen superspreading potentials to reconstruct their spatial spread across US metropolitan areas. The two pandemics exhibited distinct transmission pathways across locations; however, both pandemics established local circulation in most metropolitan areas within weeks, driven by several shared transmission hubs. Early spatial spread was more strongly associated with air travel than with commuting, though stochastic dynamics introduced substantial uncertainty in transmission routes, creating challenges for timely detection and control. Simulations indicate that broad wastewater surveillance coverage beyond top transmission hubs coupled with effective infection control may slow initial spatial expansion. Our findings highlight the rapid, stochastic spread of pandemic respiratory pathogens and the difficulties of early outbreak containment.

Competing Interest Statement

JS and Columbia University disclose partial ownership of SK Analytics. Other authors declare no competing interest.

Funding Statement

This study was supported by funding from National Natural Science Foundation of China 12371516 (RZ), National Science Foundation DMS-2229605 (SP), Centers for Disease Control and Prevention U01CK000592 (JS, SP) and 75D30122C14289 (JS), National Institute of Allergy and Infectious Diseases R01AI163023 (JS), Princeton Catalysis Initiative (BTG), Princeton Precision Health (BTG), and High Meadows Environmental Institute (BTG). The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the US National Institutes of Health, Centers for Disease Control and Prevention, or Department of Health and Human Services.

Source: 

Link: https://www.medrxiv.org/content/10.1101/2025.11.24.25340792v1

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Estimated #impact of 2022–2023 #influenza #vaccines on annual #hospital #burden in the #USA

 

Significance

Annual influenza epidemics in the United States cause hundreds of thousands of hospitalizations. Quantifying vaccine impact is vital, yet many analyses overlook vaccines’ dual benefits: directly protecting recipients and indirectly protecting their contacts. Using a mathematical model that accounts for both effects, we estimate that vaccination prevented about 70,000 hospitalizations during the 2022–2023 season, with another 19,000 potentially avoidable if coverage met the 70% national target. Despite uncertainty in vaccine effectiveness against infection, our findings suggest that vaccinating younger adults offers substantial indirect protection for older adults. Tailoring annual vaccination campaigns by age group and state could further strengthen their public health impact.

Abstract

During the COVID-19 pandemic early years, infection prevention measures suppressed transmission of seasonal influenza and other respiratory viruses. The early onset and moderate severity of the US 2022–2023 influenza season may have resulted from reduced use of nonpharmaceutical interventions or lower population immunity after 2 y of limited influenza virus circulation. We used a mathematical model of influenza virus transmission that incorporates vaccine-derived protection against both infection and severe disease to estimate the impact of influenza vaccines on healthcare burden. Assuming reported levels of past vaccine effectiveness (VE) against infection and hospitalization, we estimate that influenza vaccines prevented 69,886 (95% CI: 51,860 to 84,575) influenza-related hospitalizations nationwide during the 2022–2023 season, with 57% attributable to reduced susceptibility and onward transmission. Despite limited data on VE against infection, our analyses suggest substantial indirect protection, particularly from young adults to other age groups. This is supported by a significant negative correlation between young adult (aged 18 to 49 y) vaccination rates and observed hospital burden across US states. Among those aged ≧65 y, nearly half of averted hospitalizations resulted from vaccinating younger age groups. These findings highlight the need for better estimates of influenza VE against infection and the potential benefits of increasing young adult influenza vaccination rates to reduce both direct and indirect disease burden.

Source: Proceedings of the National Academy of Sciences of the United States of America, https://www.pnas.org/doi/abs/10.1073/pnas.2505175122?af=R

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Clustering #Countries on #Development Indicators Reveals Structure Relevant for #H5N1 #Mortality Analysis

 

Abstract

Infectious diseases are often observed to have different epidemiology in different countries, which arises due to various factors including those that are ecological, socioeconomic, and healthcare-related. Such variability can sometimes be best captured through looking at groups of countries that are similar within-group but variable between-group. In this study we use statistical learning methods to generate data-driven disease-centric groupings of countries rather than those developed for administrative or political reasons by e.g. the WHO, World Bank, and the United Nations. In particular, we apply hierarchical clustering to group countries based on shared disease-relevant characteristics for zoonotic H5N1 influenza. Using statistical methods such as classification and regression trees (CART)-based imputation and dynamic tree cutting, the analysis accounts for missing data and identifies epidemiologically (rather than politically or economically) meaningful clusters. Applying health metric relevant indicators, we cluster the countries of the world and using a Bayesian approach compute CFRs of zoonotic H5N1 influenza before comparing across clusters. We find that countries with stronger healthcare systems and lower poverty rates tend to have lower and more stable CFRs, whereas resource-limited settings face higher fatality risks.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

MKA was supported by the Schlumberger Foundation Faculty for the Future. TH was supported by the Wellcome Trust (227438/Z/23/Z) and the Medical Research Council (UKRI483).

Source: MedRxIV, https://www.medrxiv.org/content/10.1101/2025.11.08.25339808v1

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#Modeling and #Analysis of SIRR Model (#Ebola #Transmission Dynamics Model) with Delay Differential Equation

 

Abstract

Background

Ebola virus disease (EVD) is a severe and often fatal illness with high transmission potential and recurring outbreaks. Traditional compartmental models often neglect biologically important delays, such as the latent period before an infected individual becomes infectious, limiting their ability to capture real-world epidemic patterns. Including such delays can provide a more accurate understanding of outbreak persistence and control strategies.

Methods

In this study, we develop and analyze a novel deterministic SIRR model that captures the complex transmission dynamics of Ebola by explicitly combining nonlinear incidence rates with a delay differential equation framework. Unlike traditional models, this approach integrates a biologically motivated delay to represent the latent period before infectiousness, providing a more realistic depiction of disease spread. The basic reproduction number (R0) is derived using the next-generation matrix, and local stability for disease-free and endemic equilibria is established. Using center manifold theory, we investigate transcritical bifurcation at R0 = 1, while Hopf bifurcation analysis determines when delays trigger oscillatory epidemics. Sensitivity analysis identifies parameters most influencing R0, and numerical simulations are performed using the fourth-order Runge–Kutta method.

Results

The main novelty of this work lies in its detailed investigation of how delays influence outbreak persistence and can trigger oscillatory epidemics, patterns often observed in practice but rarely captured by classic models. For R0< 1, the disease-free equilibrium is locally asymptotically stable; for R0> 1, an endemic equilibrium emerges. Increasing delays destabilizes the system, amplifying peak infections, prolonging outbreaks, and producing sustained oscillations. Isolation of recovered individuals (c) significantly reduces R_0, while transmission rate (β), recruitment rate (Λ), and isolation transition rate (ρ) are identified as the most sensitive parameters.

Conclusions

Accounting for delayed recovery dynamics is crucial for accurately predicting outbreak patterns and designing effective interventions. This delay-based, nonlinear-incidence model offers a robust analytical and computational framework for guiding public health strategies, with direct implications for reducing transmission, shortening outbreak duration, and preventing epidemic resurgence.

Source: F1000 Research, https://f1000research.com/articles/14-857/v1

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Modelling a potential #zoonotic #spillover event of #H5N1 #influenza

 

Abstract

Highly Pathogenic Avian Influenza (HPAI) is a prominent candidate for a future human pandemic arising from a zoonotic spillover event. Its best-known subtype is H5N1, with South- or South-East Asia a likely location for an initial outbreak. Such an outbreak would be initiated through a primary event of bird-to-human infection, followed by sustained human-to-human transmission. Early interventions require the extraction, integration and interpretation of epidemiological information from the limited and noisy case data available at outbreak onset. We studied the implications of a potential zoonotic spillover of H5N1 influenza into humans. Our simulations used BharatSim, an agent-based model framework designed primarily for the population of India, but which can be tuned easily for others. We considered a synthetic population representing primary contacts in an outbreak site with infected birds. These primary contacts transfer infections to secondary (household) contacts, from where the infection spreads further. We simulate outbreak scenarios in farm as well as wet-market settings, accounting for the network structure of human contacts and the stochasticity of the infection process. We further simulated multiple interventions, including bird-culling, quarantines, and vaccinations. We show how limited, noisy data for primary and secondary infections can be used to estimate epidemiological transmission parameters, such as the basic reproductive ratio R_0 from other metrics like the secondary attack risk, in realistic social interaction settings. We describe the impact of early interventions (bird-culling, quarantines, and vaccination), taken together or separately, in slowing or terminating the outbreak. An individual-based model allows for the most granular description of the bird-human spillover and subsequent human-to-human transmission for the case of H5N1. Such models can be contextualised to individual communities across varied geographies, given representative contact networks. We show how such models allow for the systematic real-time exploration of policy measures that could constrain disease-spread, as well as guide a better understanding of disease epidemiology for an emerging infectious disease.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

The authors are grateful for ongoing support from the Mphasis F1 Foundation. BharatSim development was supported by the Bill and Melinda Gates Foundation, Grant No: R/BMG/PHY/GMN/20, as well as by the Mphasis F1 Foundation.

Source: MedRxIV, https://www.medrxiv.org/content/10.1101/2025.04.28.25326570v2

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