Epidemics最新文献_第4页

Bayesian spatio-temporal modelling for infectious disease outbreak detection 传染病爆发检测的贝叶斯时空模型

IF 2.4 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2026-03-01 Epub Date: 2025-12-17 DOI: 10.1016/j.epidem.2025.100879

Matthew Adeoye , Xavier Didelot , Simon E.F. Spencer

The Bayesian analysis of infectious disease surveillance data from multiple locations typically involves building and fitting a spatio-temporal model of how the disease spreads in the structured population. Here we present new generally applicable methodology to perform this task. We introduce a parsimonious representation of seasonality and a biologically informed specification of the outbreak component to avoid parameter identifiability issues. We develop a computationally efficient Bayesian inference methodology for the proposed models, including techniques to detect outbreaks by computing marginal posterior probabilities at each spatial location and time point. We show that it is possible to efficiently integrate out the discrete parameters associated with outbreak states, enabling the use of dynamic Hamiltonian Monte Carlo (HMC) as a complementary alternative to a hybrid Markov chain Monte Carlo (MCMC) algorithm. Furthermore, we introduce a robust Bayesian model comparison framework based on importance sampling to approximate model evidence in high-dimensional space. The performance of our methodology is validated through systematic simulation studies, where simulated outbreaks were successfully detected, and our model comparison strategy demonstrates strong reliability. We also apply our new methodology to monthly incidence data on invasive meningococcal disease from 28 European countries. The results highlight outbreaks across multiple countries and months, with model comparison analysis showing that the new specification outperforms previous approaches. The accompanying software is freely available as a R package at https://github.com/Matthewadeoye/DetectOutbreaks.

对来自多个地点的传染病监测数据的贝叶斯分析通常涉及建立和拟合疾病如何在结构化人群中传播的时空模型。在这里，我们提出了一种新的普遍适用的方法来执行这项任务。我们引入了季节性的简约表示和爆发成分的生物学信息规范，以避免参数可识别性问题。我们为提出的模型开发了一种计算效率高的贝叶斯推理方法，包括通过计算每个空间位置和时间点的边际后验概率来检测爆发的技术。我们表明，可以有效地积分出与爆发状态相关的离散参数，从而可以使用动态哈密顿蒙特卡罗（HMC）作为混合马尔可夫链蒙特卡罗（MCMC）算法的补充替代方案。此外，我们引入了一个基于重要抽样的鲁棒贝叶斯模型比较框架来近似高维空间中的模型证据。我们的方法的性能通过系统的模拟研究得到验证，其中模拟的爆发被成功检测到，我们的模型比较策略显示出很强的可靠性。我们还将我们的新方法应用于28个欧洲国家侵袭性脑膜炎球菌病的每月发病率数据。结果突出了多个国家和多个月的疫情，模型比较分析表明，新规范优于以前的方法。随附的软件可以在https://github.com/Matthewadeoye/DetectOutbreaks上作为R包免费获得。

{"title":"Bayesian spatio-temporal modelling for infectious disease outbreak detection","authors":"Matthew Adeoye , Xavier Didelot , Simon E.F. Spencer","doi":"10.1016/j.epidem.2025.100879","DOIUrl":"10.1016/j.epidem.2025.100879","url":null,"abstract":"<div><div>The Bayesian analysis of infectious disease surveillance data from multiple locations typically involves building and fitting a spatio-temporal model of how the disease spreads in the structured population. Here we present new generally applicable methodology to perform this task. We introduce a parsimonious representation of seasonality and a biologically informed specification of the outbreak component to avoid parameter identifiability issues. We develop a computationally efficient Bayesian inference methodology for the proposed models, including techniques to detect outbreaks by computing marginal posterior probabilities at each spatial location and time point. We show that it is possible to efficiently integrate out the discrete parameters associated with outbreak states, enabling the use of dynamic Hamiltonian Monte Carlo (HMC) as a complementary alternative to a hybrid Markov chain Monte Carlo (MCMC) algorithm. Furthermore, we introduce a robust Bayesian model comparison framework based on importance sampling to approximate model evidence in high-dimensional space. The performance of our methodology is validated through systematic simulation studies, where simulated outbreaks were successfully detected, and our model comparison strategy demonstrates strong reliability. We also apply our new methodology to monthly incidence data on invasive meningococcal disease from 28 European countries. The results highlight outbreaks across multiple countries and months, with model comparison analysis showing that the new specification outperforms previous approaches. The accompanying software is freely available as a R package at <span><span>https://github.com/Matthewadeoye/DetectOutbreaks</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100879"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Augmenting community-driven vector surveillance with automated image classification: Lessons from the Artificial Intelligence Mosquito Alert (AIMA) system 利用自动图像分类增强社区驱动的病媒监测：来自人工智能蚊子警报（AIMA）系统的经验教训

IF 2.4 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-12-01 Epub Date: 2025-10-19 DOI: 10.1016/j.epidem.2025.100863

Monika Falk , Joan Garriga , Roger Eritja , Isis Sanpera-Calbet , Enric Pou , Alex Richter-Boix , John R.B. Palmer , Frederic Bartumeus

The Mosquito Alert (MA) platform leverages artificial intelligence to enhance community-driven mosquito surveillance by automatically identifying mosquito species from geolocated images submitted via a mobile app. This empowers the public to report both native and invasive mosquitoes of public health relevance, contributing to early detection and monitoring efforts. The Artificial Intelligence Mosquito Alert (AIMA) system integrates machine learning image classification within an automated backend pipeline to enable real-time triaging of submissions: critical reports are flagged for expert review, routine cases are classified automatically, and contributors receive immediate feedback fostering participant engagement. By automating routine identifications, the system reduces the burden on experts, allowing them to focus on complex or ambiguous cases that require taxonomic expertise. This study focuses on two AIMA operational periods in 2023 and 2024. We evaluate model updates and performance across these years, highlighting both progress achieved and remaining limitations under real-world citizen science conditions. The most reliably classified species across both models were Aedes albopictus and Culex sp., whereas Aedes aegypti remained difficult to identify. Despite its limitations, AIMA remains central to enabling scalable, responsive, and intelligent mosquito vector surveillance, substantially reducing the time experts must devote to routine identifications. Functioning as an Early Warning System (EWS), MA produces real-time distribution maps of invasive species and rapidly delivers actionable information to public health authorities, facilitating timely responses and intervention.

蚊子警报（MA）平台利用人工智能，通过移动应用程序提交的地理定位图像自动识别蚊子种类，加强社区驱动的蚊子监测。这使公众能够报告与公共卫生相关的本地和入侵蚊子，有助于早期发现和监测工作。人工智能蚊子警报（AIMA）系统将机器学习图像分类集成到自动化后端管道中，以实现提交的实时分类：关键报告被标记供专家审查，常规案例被自动分类，贡献者收到即时反馈，促进参与者参与。通过自动化常规识别，该系统减轻了专家的负担，使他们能够专注于需要分类学专业知识的复杂或模棱两可的案例。本研究的重点是2023年和2024年两个AIMA运营时期。我们评估了这些年来模型的更新和性能，强调了在现实公民科学条件下取得的进展和仍然存在的局限性。两种模式中最可靠的分类物种是白纹伊蚊和库蚊，而埃及伊蚊仍然难以识别。尽管存在局限性，AIMA仍然是实现可扩展、反应灵敏和智能的蚊虫媒介监测的核心，大大减少了专家必须投入常规识别的时间。作为早期预警系统（EWS）， MA生成入侵物种的实时分布图，并迅速向公共卫生当局提供可操作的信息，促进及时的反应和干预。

{"title":"Augmenting community-driven vector surveillance with automated image classification: Lessons from the Artificial Intelligence Mosquito Alert (AIMA) system","authors":"Monika Falk , Joan Garriga , Roger Eritja , Isis Sanpera-Calbet , Enric Pou , Alex Richter-Boix , John R.B. Palmer , Frederic Bartumeus","doi":"10.1016/j.epidem.2025.100863","DOIUrl":"10.1016/j.epidem.2025.100863","url":null,"abstract":"<div><div>The Mosquito Alert (MA) platform leverages artificial intelligence to enhance community-driven mosquito surveillance by automatically identifying mosquito species from geolocated images submitted via a mobile app. This empowers the public to report both native and invasive mosquitoes of public health relevance, contributing to early detection and monitoring efforts. The Artificial Intelligence Mosquito Alert (AIMA) system integrates machine learning image classification within an automated backend pipeline to enable real-time triaging of submissions: critical reports are flagged for expert review, routine cases are classified automatically, and contributors receive immediate feedback fostering participant engagement. By automating routine identifications, the system reduces the burden on experts, allowing them to focus on complex or ambiguous cases that require taxonomic expertise. This study focuses on two AIMA operational periods in 2023 and 2024. We evaluate model updates and performance across these years, highlighting both progress achieved and remaining limitations under real-world citizen science conditions. The most reliably classified species across both models were <em>Aedes albopictus</em> and <em>Culex</em> sp., whereas <em>Aedes aegypti</em> remained difficult to identify. Despite its limitations, AIMA remains central to enabling scalable, responsive, and intelligent mosquito vector surveillance, substantially reducing the time experts must devote to routine identifications. Functioning as an Early Warning System (EWS), MA produces real-time distribution maps of invasive species and rapidly delivers actionable information to public health authorities, facilitating timely responses and intervention.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100863"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145363569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A deep learning approach for enhancing pandemic prediction: A retrospective evaluation of transformer neural networks and multi-source data fusion for infectious disease forecasting 增强流行病预测的深度学习方法：用于传染病预测的变压器神经网络和多源数据融合的回顾性评估。

IF 2.4 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-12-01 Epub Date: 2025-11-01 DOI: 10.1016/j.epidem.2025.100865

Jiande Wu , Shakhawat Tanim , MinJae Woo , Tanvir Ahammed , Amanda Marie Bleichrodt , Lior Rennert

This paper introduces a deep learning model for county-level Covid-19 forecasting, presenting it as a retrospective case study. We utilize a transformer neural network with multi-source data fusion, incorporating historical case data, death data, and social media sentiment to capture complex temporal and spatial dynamics. Additionally, we develop multi-level and multi-scale attention mechanisms for adaptive time-frequency analysis. In a retrospective evaluation across three Omicron variant waves (December 2021 through February 2023), the model demonstrated strong performance in predicting county-level Covid-19 cases and deaths, with median county agreement accuracy ranging from 74.0 % to 82.6 % for one-week case forecasts and 68.7–79.6 % for 5-week case forecasts. While these historical results are promising, prospective validation is needed to assess the model's utility under live, evolving data conditions. Median county agreement accuracy for deaths ranged from 83.2 % to 86.3 % for one-week forecasts and 84.3–87.2 % for five-week forecasts. Incorporating social media data yielded mild to moderate improvement in forecasting accuracy. Overall, the proposed model yielded substantial improvements compared to a baseline persistence model utilizing the last observation carried forward. By integrating real-time data and capturing complex pandemic dynamics, this approach surpasses traditional methods. The results demonstrate the model's strong performance in a retrospective setting, highlighting the utility of multi-source data fusion and attention mechanisms for fine-grained epidemiological forecasting. This work serves as a case study on the application of advanced deep learning techniques to local-level pandemic data, offering a methodological framework for future research.

本文介绍了一种县级Covid-19预测的深度学习模型，并以回顾性案例研究的形式进行了介绍。我们利用具有多源数据融合的变压器神经网络，结合历史案例数据、死亡数据和社交媒体情绪来捕捉复杂的时空动态。此外，我们还开发了多层次和多尺度的自适应时频分析注意机制。在对三个Omicron变异波（2021年12月至2023年2月）的回顾性评估中，该模型在预测县级Covid-19病例和死亡方面表现出色，一周病例预测的中位县一致性准确率为74.0 %至82.6 %，五周病例预测的中位县一致性准确率为68.7- 79.6% %。虽然这些历史结果很有希望，但需要进行前瞻性验证，以评估模型在实时、不断变化的数据条件下的效用。对于一周预测，死亡的中位数县协议准确率为83.2 %至86.3 %，对于五周预测为84.3-87.2 %。结合社交媒体数据在预测准确性方面产生了轻微到中度的改善。总的来说，与利用最后一次观测的基线持久性模型相比，所提出的模型产生了实质性的改进。通过整合实时数据和捕捉复杂的大流行动态，这种方法超越了传统方法。结果表明，该模型在回顾性设置中具有强大的性能，突出了多源数据融合和注意机制在细粒度流行病学预测中的实用性。这项工作是将先进的深度学习技术应用于地方一级大流行数据的案例研究，为未来的研究提供了方法框架。

{"title":"A deep learning approach for enhancing pandemic prediction: A retrospective evaluation of transformer neural networks and multi-source data fusion for infectious disease forecasting","authors":"Jiande Wu , Shakhawat Tanim , MinJae Woo , Tanvir Ahammed , Amanda Marie Bleichrodt , Lior Rennert","doi":"10.1016/j.epidem.2025.100865","DOIUrl":"10.1016/j.epidem.2025.100865","url":null,"abstract":"<div><div>This paper introduces a deep learning model for county-level Covid-19 forecasting, presenting it as a retrospective case study. We utilize a transformer neural network with multi-source data fusion, incorporating historical case data, death data, and social media sentiment to capture complex temporal and spatial dynamics. Additionally, we develop multi-level and multi-scale attention mechanisms for adaptive time-frequency analysis. In a retrospective evaluation across three Omicron variant waves (December 2021 through February 2023), the model demonstrated strong performance in predicting county-level Covid-19 cases and deaths, with median county agreement accuracy ranging from 74.0 % to 82.6 % for one-week case forecasts and 68.7–79.6 % for 5-week case forecasts. While these historical results are promising, prospective validation is needed to assess the model's utility under live, evolving data conditions. Median county agreement accuracy for deaths ranged from 83.2 % to 86.3 % for one-week forecasts and 84.3–87.2 % for five-week forecasts. Incorporating social media data yielded mild to moderate improvement in forecasting accuracy. Overall, the proposed model yielded substantial improvements compared to a baseline persistence model utilizing the last observation carried forward. By integrating real-time data and capturing complex pandemic dynamics, this approach surpasses traditional methods. The results demonstrate the model's strong performance in a retrospective setting, highlighting the utility of multi-source data fusion and attention mechanisms for fine-grained epidemiological forecasting. This work serves as a case study on the application of advanced deep learning techniques to local-level pandemic data, offering a methodological framework for future research.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100865"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145460189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling the spatio-temporal spread of cholera in France in 1892 模拟1892年霍乱在法国的时空传播。

IF 2.4 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-12-01 Epub Date: 2025-11-26 DOI: 10.1016/j.epidem.2025.100872

Charlotte Perlant , François-Xavier Weill , Juliette Paireau , Mirabelle Scipioni , Paolo Bosetti , Simon Cauchemez

From an historical perspective, it is important to understand how past epidemics spread; but such a task is complicated by limited data availability. Here, using unique digitized historical data, we characterized the patterns and drivers of spread of the last major French cholera epidemic in 1892. We found that epidemic dynamics are well captured by a standard gravity model, highlighting the key contribution of human mobility to cholera spread. Our findings also underscore the crucial role of major commercial ports that acted both as points of introduction from external sources (multiple introductions were estimated) and as local transmission hubs (transmission rates increased by a factor of 10 around ports). We also estimated a 2.5-fold increase in transmission rates in mid-August, compensated by a reduction in the duration of infectivity of municipalities, highlighting both seasonality in transmission and the effectiveness of control measures implemented in 1892. Applying modern analytical techniques to historical outbreaks enhances our understanding of past pandemics.

从历史的角度来看，重要的是要了解过去的流行病是如何传播的；但由于可用数据有限，这一任务变得复杂。在这里，我们使用独特的数字化历史数据，描述了1892年法国最后一次主要霍乱疫情的传播模式和驱动因素。我们发现，一个标准的重力模型很好地捕捉了流行病的动态，突出了人类流动性对霍乱传播的关键贡献。我们的研究结果还强调了主要商业港口的关键作用，这些港口既可以作为外部来源的引入点（估计有多个引入点），也可以作为本地传输中心（港口周围的传输速率增加了10倍）。我们还估计，8月中旬的传播率增加了2.5倍，但各城市感染持续时间的缩短弥补了这一点，突出了传播的季节性和1892年实施的控制措施的有效性。将现代分析技术应用于历史上的疫情，增强了我们对过去大流行的理解。

{"title":"Modeling the spatio-temporal spread of cholera in France in 1892","authors":"Charlotte Perlant , François-Xavier Weill , Juliette Paireau , Mirabelle Scipioni , Paolo Bosetti , Simon Cauchemez","doi":"10.1016/j.epidem.2025.100872","DOIUrl":"10.1016/j.epidem.2025.100872","url":null,"abstract":"<div><div>From an historical perspective, it is important to understand how past epidemics spread; but such a task is complicated by limited data availability. Here, using unique digitized historical data, we characterized the patterns and drivers of spread of the last major French cholera epidemic in 1892. We found that epidemic dynamics are well captured by a standard gravity model, highlighting the key contribution of human mobility to cholera spread. Our findings also underscore the crucial role of major commercial ports that acted both as points of introduction from external sources (multiple introductions were estimated) and as local transmission hubs (transmission rates increased by a factor of 10 around ports). We also estimated a 2.5-fold increase in transmission rates in mid-August, compensated by a reduction in the duration of infectivity of municipalities, highlighting both seasonality in transmission and the effectiveness of control measures implemented in 1892. Applying modern analytical techniques to historical outbreaks enhances our understanding of past pandemics.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100872"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145649825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Supporting LGBTQ+ epidemiologists in the UK during research-related travel and international collaboration 支持英国LGBTQ+流行病学家进行与研究相关的旅行和国际合作。

IF 2.4 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-12-01 Epub Date: 2025-11-13 DOI: 10.1016/j.epidem.2025.100871

Joseph T. Hicks , Bethan Cracknell Daniels , Rosie Maddren , Jack Doyle , Travis Mager , Christina J. Atchison , Lucy Okell

Conferences, fieldwork, international positions, and collaborations with international partners are beneficial to any epidemiologist, strengthening relationships with fellow scientists, policymakers, health professionals, and those affected by the studied disease. However, international working can pose unique challenges for minority groups. In the UK, LGBTQ+ scientists have a degree of legal protection against discrimination, and universities often have LGBTQ+ staff-student networks that provide support. By contrast, international work can present barriers that non-LGTBQ+ colleagues may not be aware of, such as stress when travelling to countries with anti-LGBTQ+ laws, policies, or sentiments. Homophobic, biphobic, and transphobic beliefs, policies, and actions fluctuate over time, but persist or are on the rise in many locations across the world, including high-income countries. Without institutional support, work-related travel can present a cognitive burden, threatening both physical safety and mental well-being of LGBTQ+ researchers. At Imperial College London, we have worked to address these challenges by developing resources and training for LGBTQ+ staff, students, and allies. We developed an initiative including the creation of online written resources, integration of these materials into travel safety protocols, and a partnership with a LGBTQ+ mental health organization to offer in-person training. We present our experience developing these resources, describe feedback of training participants, and discuss strategies for institutions to develop their own support resources, fostering greater equity in the research experience for individuals of all identities.

会议、实地考察、国际职位以及与国际伙伴的合作对任何流行病学家都是有益的，可以加强与科学家同行、政策制定者、卫生专业人员以及受所研究疾病影响者的关系。然而，国际工作可能给少数群体带来独特的挑战。在英国，LGBTQ+ 科学家有一定程度的免受歧视的法律保护，大学通常有LGBTQ+ 师生网络提供支持。相比之下，国际工作可能会带来非lgbtq + 同事可能没有意识到的障碍，例如前往有反lgbtq + 法律、政策或情绪的国家旅行时的压力。憎恶同性恋、双性恋和跨性别者的信仰、政策和行动随着时间的推移而波动，但在包括高收入国家在内的世界许多地方持续存在或呈上升趋势。如果没有机构支持，与工作相关的旅行可能会带来认知负担，威胁LGBTQ+ 研究人员的身体安全和心理健康。在伦敦帝国理工学院，我们通过为LGBTQ+ 员工、学生和盟友开发资源和培训，努力应对这些挑战。我们制定了一项倡议，包括创建在线书面资源，将这些材料整合到旅行安全协议中，并与LGBTQ+ 心理健康组织合作，提供面对面的培训。我们介绍了我们开发这些资源的经验，描述了培训参与者的反馈，并讨论了机构开发自己的支持资源的策略，以促进所有身份的个人在研究经验方面的更大公平。

{"title":"Supporting LGBTQ+ epidemiologists in the UK during research-related travel and international collaboration","authors":"Joseph T. Hicks , Bethan Cracknell Daniels , Rosie Maddren , Jack Doyle , Travis Mager , Christina J. Atchison , Lucy Okell","doi":"10.1016/j.epidem.2025.100871","DOIUrl":"10.1016/j.epidem.2025.100871","url":null,"abstract":"<div><div>Conferences, fieldwork, international positions, and collaborations with international partners are beneficial to any epidemiologist, strengthening relationships with fellow scientists, policymakers, health professionals, and those affected by the studied disease. However, international working can pose unique challenges for minority groups. In the UK, LGBTQ+ scientists have a degree of legal protection against discrimination, and universities often have LGBTQ+ staff-student networks that provide support. By contrast, international work can present barriers that non-LGTBQ+ colleagues may not be aware of, such as stress when travelling to countries with anti-LGBTQ+ laws, policies, or sentiments. Homophobic, biphobic, and transphobic beliefs, policies, and actions fluctuate over time, but persist or are on the rise in many locations across the world, including high-income countries. Without institutional support, work-related travel can present a cognitive burden, threatening both physical safety and mental well-being of LGBTQ+ researchers. At Imperial College London, we have worked to address these challenges by developing resources and training for LGBTQ+ staff, students, and allies. We developed an initiative including the creation of online written resources, integration of these materials into travel safety protocols, and a partnership with a LGBTQ+ mental health organization to offer in-person training. We present our experience developing these resources, describe feedback of training participants, and discuss strategies for institutions to develop their own support resources, fostering greater equity in the research experience for individuals of all identities.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100871"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145558112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Environmental drivers of Ixodes ricinus tick population dynamics: Mechanistic modelling using longitudinal field surveys and climate data 蓖麻蜱种群动态的环境驱动因素：利用纵向野外调查和气候数据的机制建模

IF 2.4 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-12-01 Epub Date: 2025-09-08 DOI: 10.1016/j.epidem.2025.100854

Younjung Kim , Benoît Jaulhac , Juan F. Vesga , Laurence Zilliox , Nathalie Boulanger , W.John Edmunds , Raphaëlle Métras

Ixodes ricinus is the primary vector for Lyme disease and tick-borne encephalitis across Europe. However, the environmental drivers of the tick's complex life cycle have not been quantified with real-world data, making it challenging to incorporate tick demography into tick-borne disease transmission models. To address this gap, we fitted a mechanistic model to a detailed 10-year longitudinal dataset from four sites in Northern France, where I. ricinus is abundant and Lyme disease and tick-borne encephalitis have been reported for decades. By incorporating key demographic processes and the influence of environmental conditions on these processes, our model estimated oviposition, hatching, and moulting rates across a range of temperature or saturation deficit, as well as questing and vertebrate host contact rates. In the studied tick population, moulting peaked at 14.2 °C (95 %HDI: 12.5–16.1 °C), substantially lower than commonly suggested by laboratory-based studies, whereas oviposition and hatching peaked at 24.4 °C (95 %HDI: 10.9–27.2 °C) and 24.7 °C (95 %HDI: 17.8–27.2 °C), respectively. Furthermore, the parameter scaling the empirical baseline vertebrate host contact rates was found to vary significantly between the four study sites, with one site presenting up to 2.90 (95 %HDI: 2.15–3.86) times higher contact rates than the other three sites. Additionally, for ticks overwintering through diapause, moulting in spring more accurately matched the predominantly unimodal questing patterns observed, compared to moulting in summer. Finally, model projections under pessimistic climate change scenarios indicated decreasing tick abundance trends over the next two decades, while no significant decrease was predicted under moderate scenarios. This study provides a foundation for models of I. ricinus-borne pathogen transmission and can be adapted to other Ixodidae populations of public health significance.

在整个欧洲，蓖麻伊蚊是莱姆病和蜱传脑炎的主要媒介。然而，蜱虫复杂生命周期的环境驱动因素尚未用现实世界的数据量化，这使得将蜱虫人口统计学纳入蜱媒疾病传播模型具有挑战性。为了解决这一差距，我们将一个机制模型拟合到法国北部四个地点的详细的10年纵向数据集，在那里蓖麻I. ricinus丰富，莱姆病和蜱传脑炎已经报道了几十年。通过结合关键的人口统计过程和环境条件对这些过程的影响，我们的模型估计了在温度或饱和缺陷范围内的产卵、孵化和换毛率，以及探索和脊椎动物宿主接触率。人口研究的蜱虫,蜕皮 达到峰值14.2°C(-16.1 95 %人类发展指数:12.5 °C),大大低于一般建议的实验室研究,而产卵和孵化 达到峰值24.4°C(-27.2 95 %人类发展指数:10.9 °C)和24.7 °C(95 %人类发展指数:17.8 - -27.2 °C),分别。此外，四个研究地点的经验基线脊椎动物宿主接触率参数差异显著，其中一个地点的接触率比其他三个地点高2.90倍（95 %HDI: 2.15-3.86）。此外，对于通过滞育越冬的蜱虫来说，与夏季的换羽相比，春季的换羽更准确地匹配了观察到的主要单峰寻巢模式。在悲观气候变化情景下，蜱虫丰度呈下降趋势，而在温和气候变化情景下，蜱虫丰度没有显著下降。本研究为建立蓖麻伊蚊病原传播模型奠定了基础，并可适用于其他具有公共卫生意义的伊蚊种群。

{"title":"Environmental drivers of Ixodes ricinus tick population dynamics: Mechanistic modelling using longitudinal field surveys and climate data","authors":"Younjung Kim , Benoît Jaulhac , Juan F. Vesga , Laurence Zilliox , Nathalie Boulanger , W.John Edmunds , Raphaëlle Métras","doi":"10.1016/j.epidem.2025.100854","DOIUrl":"10.1016/j.epidem.2025.100854","url":null,"abstract":"<div><div><em>Ixodes ricinus</em> is the primary vector for Lyme disease and tick-borne encephalitis across Europe. However, the environmental drivers of the tick's complex life cycle have not been quantified with real-world data, making it challenging to incorporate tick demography into tick-borne disease transmission models. To address this gap, we fitted a mechanistic model to a detailed 10-year longitudinal dataset from four sites in Northern France, where <em>I. ricinus</em> is abundant and Lyme disease and tick-borne encephalitis have been reported for decades. By incorporating key demographic processes and the influence of environmental conditions on these processes, our model estimated oviposition, hatching, and moulting rates across a range of temperature or saturation deficit, as well as questing and vertebrate host contact rates. In the studied tick population, moulting peaked at 14.2 °C (95 %HDI: 12.5–16.1 °C), substantially lower than commonly suggested by laboratory-based studies, whereas oviposition and hatching peaked at 24.4 °C (95 %HDI: 10.9–27.2 °C) and 24.7 °C (95 %HDI: 17.8–27.2 °C), respectively. Furthermore, the parameter scaling the empirical baseline vertebrate host contact rates was found to vary significantly between the four study sites, with one site presenting up to 2.90 (95 %HDI: 2.15–3.86) times higher contact rates than the other three sites. Additionally, for ticks overwintering through diapause, moulting in spring more accurately matched the predominantly unimodal questing patterns observed, compared to moulting in summer. Finally, model projections under pessimistic climate change scenarios indicated decreasing tick abundance trends over the next two decades, while no significant decrease was predicted under moderate scenarios. This study provides a foundation for models of <em>I. ricinus</em>-borne pathogen transmission and can be adapted to other <em>Ixodidae</em> populations of public health significance.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100854"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rtglm: Unifying estimation of the time-varying reproduction number, Rt, under the Generalised Linear and Additive Models 广义线性和加性模型下时变再现数Rt的统一估计。

IF 2.4 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-12-01 Epub Date: 2025-09-16 DOI: 10.1016/j.epidem.2025.100857

Pierre Nouvellet

Most current methods to estimate the time-varying reproduction number (R_t), such as EpiEstim, rely on branching processes and the renewal equation. They also require subjective choices to set the level of temporal and spatial heterogeneity assumed. We propose a novel framework to estimate R_t based on Generalized Linear and Additive Models (GLM/GAM). By integrating the renewal equation model within GLM/GAM, the proposed framework, “Rtglm”, allows smooth estimation of R_t variations over time and space without relying on arbitrary scaling parameters. The performance of Rtglm was evaluated using historical datasets and simulated outbreaks. It demonstrated improved overall performance and accuracy compared to EpiEstim, as measured by the CRPS scores and Mean Square Errors respectively. However, when case incidence was low and R_t estimation relied on a smoothing term, Rtglm was marginally overconfident in its estimates. The method offers substantial improvement for the real-time estimation of spatio-temporal trends in R_t, with improved performance and lower reliance on arbitrarily set parameters. The open-source and user-friendly R package developed will also simplify user experience. Finally, the framework bridges gaps between epidemic monitoring methodologies and sets the stage for future extensions to enhance statistical inference and integrate additional epidemiological complexities, including the evaluation of intervention strategies.

目前大多数估计时变繁殖数（Rt）的方法，如EpiEstim，依赖于分支过程和更新方程。它们还需要主观选择来设定假定的时间和空间异质性水平。我们提出了一种基于广义线性和可加模型（GLM/GAM）估计Rt的新框架。通过在GLM/GAM中集成更新方程模型，所提出的框架“Rtglm”允许平滑估计Rt随时间和空间的变化，而不依赖于任意缩放参数。Rtglm的性能使用历史数据集和模拟爆发进行评估。与EpiEstim相比，它的总体性能和准确性都有所提高，分别通过CRPS分数和均方误差来衡量。然而，当病例发生率较低且Rt估计依赖于平滑项时，Rtglm对其估计略微过于自信。该方法为实时估计Rt时空趋势提供了实质性的改进，提高了性能，降低了对任意设置参数的依赖。开发的开源和用户友好的R包也将简化用户体验。最后，该框架弥合了流行病监测方法之间的差距，并为今后的扩展奠定了基础，以加强统计推断和整合更多的流行病学复杂性，包括对干预战略的评价。

{"title":"Rtglm: Unifying estimation of the time-varying reproduction number, Rt, under the Generalised Linear and Additive Models","authors":"Pierre Nouvellet","doi":"10.1016/j.epidem.2025.100857","DOIUrl":"10.1016/j.epidem.2025.100857","url":null,"abstract":"<div><div>Most current methods to estimate the time-varying reproduction number (<em>R<sub>t</sub></em>), such as <em>EpiEstim</em>, rely on branching processes and the renewal equation. They also require subjective choices to set the level of temporal and spatial heterogeneity assumed. We propose a novel framework to estimate <em>R<sub>t</sub></em> based on Generalized Linear and Additive Models (GLM/GAM). By integrating the renewal equation model within GLM/GAM, the proposed framework, “<em>Rtglm</em>”, allows smooth estimation of <em>R<sub>t</sub></em> variations over time and space without relying on arbitrary scaling parameters. The performance of <em>Rtglm</em> was evaluated using historical datasets and simulated outbreaks. It demonstrated improved overall performance and accuracy compared to <em>EpiEstim</em>, as measured by the CRPS scores and Mean Square Errors respectively. However, when case incidence was low and <em>R<sub>t</sub></em> estimation relied on a smoothing term, <em>Rtglm</em> was marginally overconfident in its estimates. The method offers substantial improvement for the real-time estimation of spatio-temporal trends in <em>R<sub>t</sub></em>, with improved performance and lower reliance on arbitrarily set parameters. The open-source and user-friendly R package developed will also simplify user experience. Finally, the framework bridges gaps between epidemic monitoring methodologies and sets the stage for future extensions to enhance statistical inference and integrate additional epidemiological complexities, including the evaluation of intervention strategies.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100857"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145151631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An optimized geo-hierarchical ensemble model to forecast hospitalizations from respiratory viruses in the United States 一个优化的地理层次集成模型预测美国呼吸道病毒住院率。

IF 2.4 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-12-01 Epub Date: 2025-11-13 DOI: 10.1016/j.epidem.2025.100869

Shaochong Xu , Hongru Du , Ensheng Dong , Xianglong Wang , Liyue Zhang , Lauren M. Gardner

Accurate forecasting of infectious diseases is crucial for timely public health response. Ensemble frameworks have shown promising outcomes in short-term forecasting of COVID-19, among other respiratory viruses, however, there is a need to further improve these frameworks. Here, we propose the generalized Optimized Geo-Hierarchical Ensemble Model (OGEM), a novel forecasting machine learning framework to forecast state-level hospitalizations of influenza, COVID-19, and RSV in the U.S. independently. This framework is multi-resolution: it integrates state, regionally-trained, and nationally-trained models through an ensemble layer and applies various optimization methods to parameterize the model weights and enhance overall predictive accuracy. This proposed framework builds on existing forecasting literature by 1) employing an ensemble of three spatially hierarchical models with state-level forecasts as the output; 2) incorporating four distinct weight optimization methods to generate the ensemble; 3) utilizing clustering methods to dynamically identify multi-state regions as a function of short-term and long-term hospitalization trends for the regionally-trained model; and 4) providing a generalized framework to forecast the expected near-term hospitalizations from Influenza, RSV and COVID-19. Results demonstrate OGEM is a robust framework with relatively high performance. Extensive experimentation using historical data highlights the predictive power of our framework compared to existing ensemble approaches. Its robust performance underscores the framework's effectiveness and potential for improving and broadening infectious disease forecasting.

传染病的准确预测对于及时作出公共卫生反应至关重要。集成框架在COVID-19和其他呼吸道病毒的短期预测中显示出有希望的结果，但是，需要进一步改进这些框架。在这里，我们提出了广义优化地理层次集成模型（OGEM），这是一种新的预测机器学习框架，可以独立预测美国流感、COVID-19和RSV的州一级住院情况。该框架是多分辨率的：它通过集成层集成了状态、区域和国家训练的模型，并应用各种优化方法来参数化模型权重，提高整体预测精度。该框架建立在现有预测文献的基础上：1)采用三个空间层次模型的集合，以国家级预测作为输出；2)结合四种不同的权重优化方法生成集成；3)利用聚类方法动态识别多状态区域，作为区域训练模型短期和长期住院趋势的函数；4)提供一个通用框架来预测流感、RSV和COVID-19的近期预期住院情况。结果表明，OGEM是一个具有较高性能的鲁棒框架。与现有的集成方法相比，使用历史数据的大量实验突出了我们框架的预测能力。它的强劲表现突出了该框架在改进和扩大传染病预测方面的有效性和潜力。

{"title":"An optimized geo-hierarchical ensemble model to forecast hospitalizations from respiratory viruses in the United States","authors":"Shaochong Xu , Hongru Du , Ensheng Dong , Xianglong Wang , Liyue Zhang , Lauren M. Gardner","doi":"10.1016/j.epidem.2025.100869","DOIUrl":"10.1016/j.epidem.2025.100869","url":null,"abstract":"<div><div>Accurate forecasting of infectious diseases is crucial for timely public health response. Ensemble frameworks have shown promising outcomes in short-term forecasting of COVID-19, among other respiratory viruses, however, there is a need to further improve these frameworks. Here, we propose the generalized Optimized Geo-Hierarchical Ensemble Model (OGEM), a novel forecasting machine learning framework to forecast state-level hospitalizations of influenza, COVID-19, and RSV in the U.S. independently. This framework is multi-resolution: it integrates state, regionally-trained, and nationally-trained models through an ensemble layer and applies various optimization methods to parameterize the model weights and enhance overall predictive accuracy. This proposed framework builds on existing forecasting literature by 1) employing an ensemble of three spatially hierarchical models with state-level forecasts as the output; 2) incorporating four distinct weight optimization methods to generate the ensemble; 3) utilizing clustering methods to dynamically identify multi-state regions as a function of short-term and long-term hospitalization trends for the regionally-trained model; and 4) providing a generalized framework to forecast the expected near-term hospitalizations from Influenza, RSV and COVID-19. Results demonstrate OGEM is a robust framework with relatively high performance. Extensive experimentation using historical data highlights the predictive power of our framework compared to existing ensemble approaches. Its robust performance underscores the framework's effectiveness and potential for improving and broadening infectious disease forecasting.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100869"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145573965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A pilot study to correlate wastewater and clinical surveillance for hepatitis A in New York state 一项在纽约州将废水和临床监测与甲型肝炎相关联的初步研究。

IF 2.4 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-12-01 Epub Date: 2025-11-11 DOI: 10.1016/j.epidem.2025.100867

Maxwell D. Weidmann , Patrick W. Bryant , Lynsey Schoultz , Aishwarya Jadhav , Lindsey Rickerman , Dustin T. Hill , Daryl M. Lamson , David A. Larsen , Kirsten St. George

The COVID-19 pandemic prompted a rapid expansion of wastewater-based surveillance in New York State (NYS). Pilot studies were initiated in 2023 to assess the use of this system for the surveillance of hepatitis A virus (HAV) and other pathogens of public health interest. A known cause of outbreaks in the US associated with contaminated food products and transmission between injection drug users, HAV is present in feces for weeks before the onset of symptoms. However, the use of wastewater surveillance as an early warning system has not been assessed outside of the outbreak setting. We compare clinical HAV surveillance with quantitative testing of wastewater samples for HAV RNA from four counties in NYS between September 2022 and November 2023, a period of relatively low HAV incidence. There was a significantly higher mean concentration of HAV RNA in wastewater from sewersheds in districts with reported HAV cases, relative to those without (267 vs. 21 gene copies per microliter, p < 0.05). For 91 % of HAV cases, HAV RNA was detected in the wastewater from the same county between HAV exposure onset and diagnosis, and new HAV RNA detection in wastewater occurred, on average, 41 days before case diagnosis. Our findings demonstrate that wastewater surveillance may provide early warning of case clusters at the county level in low-incidence settings and may allow for detection of otherwise missed asymptomatic or mild illness. Expansion of testing to include all sewersheds in each county may further improve the sensitivity for identifying locations for targeted HAV intervention.

2019冠状病毒病大流行促使纽约州迅速扩大了基于废水的监测。2023年启动了试点研究，以评估该系统在监测甲型肝炎病毒（HAV）和其他与公共卫生有关的病原体方面的使用情况。甲肝病毒是美国爆发甲肝疫情的已知原因，与受污染的食品和注射吸毒者之间的传播有关，甲肝病毒在出现症状前数周就存在于粪便中。然而，废水监测作为早期预警系统的使用尚未在疫情环境之外进行评估。我们比较了2022年9月至2023年11月期间纽约州四个县的临床甲肝病毒监测与甲肝病毒RNA定量检测废水样本，这是甲肝病毒发病率相对较低的时期。在报告有甲型肝炎病例的地区，下水道废水中甲型肝炎RNA的平均浓度明显高于没有报告甲型肝炎病例的地区（267对21基因拷贝/微升）

{"title":"A pilot study to correlate wastewater and clinical surveillance for hepatitis A in New York state","authors":"Maxwell D. Weidmann , Patrick W. Bryant , Lynsey Schoultz , Aishwarya Jadhav , Lindsey Rickerman , Dustin T. Hill , Daryl M. Lamson , David A. Larsen , Kirsten St. George","doi":"10.1016/j.epidem.2025.100867","DOIUrl":"10.1016/j.epidem.2025.100867","url":null,"abstract":"<div><div>The COVID-19 pandemic prompted a rapid expansion of wastewater-based surveillance in New York State (NYS). Pilot studies were initiated in 2023 to assess the use of this system for the surveillance of hepatitis A virus (HAV) and other pathogens of public health interest. A known cause of outbreaks in the US associated with contaminated food products and transmission between injection drug users, HAV is present in feces for weeks before the onset of symptoms. However, the use of wastewater surveillance as an early warning system has not been assessed outside of the outbreak setting. We compare clinical HAV surveillance with quantitative testing of wastewater samples for HAV RNA from four counties in NYS between September 2022 and November 2023, a period of relatively low HAV incidence. There was a significantly higher mean concentration of HAV RNA in wastewater from sewersheds in districts with reported HAV cases, relative to those without (267 vs. 21 gene copies per microliter, p < 0.05). For 91 % of HAV cases, HAV RNA was detected in the wastewater from the same county between HAV exposure onset and diagnosis, and new HAV RNA detection in wastewater occurred, on average, 41 days before case diagnosis. Our findings demonstrate that wastewater surveillance may provide early warning of case clusters at the county level in low-incidence settings and may allow for detection of otherwise missed asymptomatic or mild illness. Expansion of testing to include all sewersheds in each county may further improve the sensitivity for identifying locations for targeted HAV intervention.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100867"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145551482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The transmission dynamics of Norovirus in England: A genotype-specific modelling study 诺如病毒在英格兰的传播动力学：一项基因型特异性建模研究。

IF 2.4 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-12-01 Epub Date: 2025-11-27 DOI: 10.1016/j.epidem.2025.100875

Juan F. Vesga , Amy Douglas , Cristina Celma , Edward S. Knock , Marc Baguelin , W. John Edmunds

Background

Norovirus is the leading cause of acute gastroenteritis cases in England and worldwide, with diverse co-circulating genotypes. Vaccine candidates targeting multiple genotypes are advancing. However, most transmission models still focus on single-strain dynamics, limiting their ability to assess the role of co-circulating strains on population burden.

Methods

We developed an age structured multistrain transmission model that integrates norovirus genotype diversity, waning immunity, and cross-protection within genogroups. We calibrate to case and genotyping surveillance time-series data with community-wide age structured incidence estimates and cross-sectional seroprevalence among English children to capture the transmission dynamics of the main co-circulating norovirus strains in England. Using a calibrated model, we examine the case of an emerging GII.4 variant under different scenarios of transmissibility.

Results

We found that on average the current GII.4 strain has an R₀ of 4.8 (CrI 4.5 – 5.01). We estimate the average number of lifetime norovirus episodes per person to be 5.2 (CrI 95 % 4.5 – 6.3) in the absence of new pandemic strains, with 66 % of children in England experiencing at least one symptomatic episode by the age of four. Our sensitivity analysis and model selection suggests that cross-protection within genogroups (between strains of the same genogroup), is very limited at conferring protection. Importantly, our modelling suggests that a potential emerging variant would cause a larger first epidemic season and return to baseline levels with an increase in relative contribution of GII.4. If such variant was more transmissible, the size of the initial peak could almost double the current average epidemic peak.

Conclusions

This approach addresses key limitations of single-strain frameworks and offers a more comprehensive understanding of norovirus dynamics, improving the capacity to assess the potential population-level effects of upcoming multivalent vaccine strategies.

背景：诺如病毒是英国和世界范围内急性胃肠炎病例的主要原因，具有多种共循环基因型。针对多种基因型的候选疫苗正在推进。然而，大多数传播模型仍然侧重于单株动力学，限制了它们评估共传播菌株对种群负担的作用的能力。方法：我们建立了一个年龄结构的多株传播模型，该模型整合了诺如病毒基因型多样性、免疫减弱和基因群内的交叉保护。我们校准病例和基因分型监测时间序列数据，包括全社区年龄结构发病率估计和英国儿童的横断面血清患病率，以捕捉英国主要共循环诺如病毒株的传播动态。使用校准模型，我们在不同的传播情况下研究了新出现的GII.4变体的情况。结果：目前的GII.4菌株的平均R0为4.8 （CrI为4.5 ~ 5.01）。我们估计，在没有新的大流行毒株的情况下，每人一生中诺如病毒发作的平均次数为5.2次（CrI 95 % 4.5 - 6.3），英格兰有66 %的儿童在4岁之前至少经历一次症状发作。我们的敏感性分析和模型选择表明，基因组内（同一基因组的菌株之间）的交叉保护在提供保护方面非常有限。重要的是，我们的模型表明，一个潜在的新变种将导致更大的第一个流行季节，并随着全球免疫球蛋白的相对贡献的增加而回到基线水平。如果这种变异具有更强的传染性，初始峰值的规模可能几乎是当前平均流行峰值的两倍。结论：该方法解决了单株框架的主要局限性，并提供了对诺如病毒动力学更全面的了解，提高了评估即将到来的多价疫苗策略的潜在人群水平效应的能力。

{"title":"The transmission dynamics of Norovirus in England: A genotype-specific modelling study","authors":"Juan F. Vesga , Amy Douglas , Cristina Celma , Edward S. Knock , Marc Baguelin , W. John Edmunds","doi":"10.1016/j.epidem.2025.100875","DOIUrl":"10.1016/j.epidem.2025.100875","url":null,"abstract":"<div><h3>Background</h3><div>Norovirus is the leading cause of acute gastroenteritis cases in England and worldwide, with diverse co-circulating genotypes. Vaccine candidates targeting multiple genotypes are advancing. However, most transmission models still focus on single-strain dynamics, limiting their ability to assess the role of co-circulating strains on population burden.</div></div><div><h3>Methods</h3><div>We developed an age structured multistrain transmission model that integrates norovirus genotype diversity, waning immunity, and cross-protection within genogroups. We calibrate to case and genotyping surveillance time-series data with community-wide age structured incidence estimates and cross-sectional seroprevalence among English children to capture the transmission dynamics of the main co-circulating norovirus strains in England. Using a calibrated model, we examine the case of an emerging GII.4 variant under different scenarios of transmissibility.</div></div><div><h3>Results</h3><div>We found that on average the current GII.4 strain has an R<sub>0</sub> of 4.8 (CrI 4.5 – 5.01). We estimate the average number of lifetime norovirus episodes per person to be 5.2 (CrI 95 % 4.5 – 6.3) in the absence of new pandemic strains, with 66 % of children in England experiencing at least one symptomatic episode by the age of four. Our sensitivity analysis and model selection suggests that cross-protection within genogroups (between strains of the same genogroup), is very limited at conferring protection. Importantly, our modelling suggests that a potential emerging variant would cause a larger first epidemic season and return to baseline levels with an increase in relative contribution of GII.4. If such variant was more transmissible, the size of the initial peak could almost double the current average epidemic peak.</div></div><div><h3>Conclusions</h3><div>This approach addresses key limitations of single-strain frameworks and offers a more comprehensive understanding of norovirus dynamics, improving the capacity to assess the potential population-level effects of upcoming multivalent vaccine strategies.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100875"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145649822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0