IF 2.4 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2026-01-12 DOI: 10.1016/j.epidem.2026.100888

Jessica E. Biddle , Stacey House , Jennie H. Kwon , Rachel M. Presti , Stephanie A. Fritz , Tara Curley , Son H. McLaren , Melissa S. Stockwell , Jonathan Schmitz , H. Keipp Talbot , Carlos G. Grijalva , Elie A. Saade , Zainab Albar , Vel Murugan , Rick A. Cruz , Emily T. Martin , Ivana A. Vaughn , Karen J. Wernli , Brianna M. Wickersham , Richard K. Zimmerman , Olivia L. Williams

Household studies play a critical role in estimating influenza transmission parameters, which are essential for real-time modeling of epidemic and pandemic dynamics to inform influenza control strategies. We compared two approaches for estimating household influenza transmission parameters from multisite studies conducted in the United States during the 2023–2024 influenza season: interviewing index cases about illnesses among household contacts (n = 1537 contacts) and prospective enrollment of index cases and their household contacts with systematic, daily symptom assessment and testing (n = 857 contacts). We compared the detection of symptomatic illness, influenza-like illness (ILI; fever and either cough or sore throat), influenza virus infection, and estimated serial illness onset intervals among household contacts across studies. Symptomatic illness episodes among household contacts were identified in 40 % of contacts by index case interview compared to 59 % of contacts from individual daily follow-up. Reports of ILI were more comparable between platforms (20 % vs. 26 % respectively). Index case interviews identified 12 % of household contacts with positive influenza tests while systematic, daily testing in the individual daily follow-up platform identified influenza infection among 44 % of household contacts. Both platforms yielded a median serial interval of 4 days. While index case interviews offer rapid, resource-efficient data collection and can inform epidemiological outcomes such as age-related risks and serial intervals, they substantially underestimate laboratory-confirmed influenza cases compared to systematic daily follow-up. These findings highlight the importance of study design in accurately capturing transmission dynamics and underscore the need for systematic laboratory testing to inform public health responses.

家庭研究在估计流感传播参数方面发挥着关键作用，这对于实时建模流行病和大流行动态至关重要，从而为流感控制策略提供信息。我们比较了2023-2024年流感季节在美国进行的多地点研究中估计家庭流感传播参数的两种方法：对家庭接触者中疾病的指示病例进行访谈（ = 1537名接触者），并对指示病例及其家庭接触者进行系统的每日症状评估和检测（ = 857名接触者）。我们比较了各研究中家庭接触者的症状性疾病、流感样疾病（ILI、发热、咳嗽或喉咙痛）、流感病毒感染的检出率，以及估计的系列疾病发病间隔。40% %的接触者通过指数病例访谈被确定为家庭接触者的症状性疾病发作，而59% %的接触者通过个人日常随访被确定为家庭接触者。不同平台间的ILI报告更具可比性（分别为20% %和26% %）。指数病例访谈确定了12% %的流感检测呈阳性的家庭接触者，而在个人每日随访平台中系统的每日检测确定了44% %的家庭接触者感染流感。两种平台的平均连续间隔均为4天。虽然索引病例访谈提供了快速、资源高效的数据收集，并可告知流行病学结果，如与年龄相关的风险和连续间隔，但与系统的日常随访相比，它们大大低估了实验室确诊的流感病例。这些发现强调了研究设计在准确捕捉传播动态方面的重要性，并强调需要进行系统的实验室检测，以便为公共卫生反应提供信息。

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引用次数: 0

Social contact patterns derived from an epidemiological survey and GPS-based co-location data – A systematic comparison using parallel data collections during the COVID-19 pandemic in Germany 来自流行病学调查和基于gps的同址数据的社会接触模式——在德国COVID-19大流行期间使用平行数据收集进行系统比较

IF 2.4 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2026-01-08 DOI: 10.1016/j.epidem.2026.100886

Huynh Thi Phuong , Janik Suer , Vitaly Belik , Alejandra Rincón Hidalgo , Andrzej K. Jarynowski , Richard Pastor , Steven Schulz , Ashish Thampi , Chao Xu , Marlli Zambrano , Rafael Mikolajczyk , André Karch , Veronika K. Jaeger , on behalf of OptimAgent Consortium

The parametrisation of contact behaviour is crucial for infectious disease transmission models. Contact information derived from self-reported surveys and from co-location in space and time (GPS-based) may reflect different dimensions of contact behaviour, which might be associated with distinct epidemiological risks depending on the contagion of interest. This study explores whether and how contacts measured using these distinct approaches exhibit similar or complementary contact patterns. We compare the mean number of contacts and the mean excess number of contacts (i.e. the ratio of mean squared contacts to mean contacts) from the COVIMOD survey and NETCHECK GPS co-location data between April 2020 and December 2021. While mean contacts measure contact intensity, mean excess contacts reflect dispersion, which is important for understanding superspreading behaviour. Mean contacts were considerably higher in co-location data (11.04; 95 %CI: 10.90–11.19) than in survey data (3.38; 95 % CI: 3.30–3.47); however, both data sources correlated well with each other. Mean excess contacts were similar during periods of strict non-pharmaceutical interventions (NPIs) but diverged when NPIs were lifted, with co-location data values rising more markedly. Setting-specific contact patterns also differed, potentially due to methodological differences in setting classification and data capture. Furthermore, regional variation was more pronounced in co-location data, with densely populated city-states showing higher contact numbers. Comparative insights from the two data sources demonstrate that GPS-based and survey-based contact data capture complementary and distinct aspects of human interaction. Combining both sources could provide a more comprehensive picture of human interactions relevant to infectious disease modelling.

接触行为的参数化对传染病传播模型至关重要。从自我报告的调查和从空间和时间上的共同定位（基于gps）获得的接触信息可能反映接触行为的不同方面，这可能与不同的流行病学风险有关，取决于感兴趣的传染。本研究探讨了使用这些不同的方法测量接触是否以及如何表现出相似或互补的接触模式。我们比较了2020年4月至2021年12月期间COVIMOD调查和NETCHECK GPS共定位数据中的平均接触数和平均过量接触数（即均方接触数与平均接触数的比率）。平均接触量测量的是接触强度，而平均过量接触量反映的是色散，这对理解超扩散行为很重要。同址数据的平均接触率（11.04;95 %CI: 10.90-11.19）明显高于调查数据（3.38;95 %CI: 3.30-3.47）；然而，这两个数据源之间的相关性很好。在严格的非药物干预（npi）期间，平均过量接触相似，但在npi解除时出现分歧，共址数据值上升更为显著。特定环境的接触模式也有所不同，这可能是由于在环境分类和数据捕获方面的方法差异。此外，同一地点数据的区域差异更为明显，人口稠密的城邦显示出更高的联系号码。来自两种数据来源的对比分析表明，基于gps的接触数据和基于调查的接触数据捕捉到了人类互动的互补和不同方面。将这两种来源结合起来，可以更全面地了解与传染病建模有关的人类相互作用。

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引用次数: 0

Evaluating mobility restrictions through spatiotemporal effective reproduction number analysis in a multi-patch model with complex mobility data 基于复杂迁移数据的多斑块模型时空有效繁殖数分析评估迁移限制。

IF 2.4 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-12-20 DOI: 10.1016/j.epidem.2025.100884

Byul Nim Kim , Minchan Choi , Hyosun Lee, Sunmi Lee

Understanding the spatial and temporal dynamics of infectious disease transmission is critical for effective epidemic preparedness and response. COVID-19 transmission is influenced by mobility patterns, regional connectivity, and evolving public health interventions, making it challenging to quantify region-specific transmission risks. Our study integrates intervention-driven analysis, real-world data, and high-resolution modeling to establish a robust computational framework for assessing interregional transmission dynamics. We employ a multi-patch model to estimate the time-dependent regional effective reproduction number and systematically quantify interregional infection contributions. By integrating high-resolution mobility and COVID-19 incidence data from South Korea, we identify key transmission hubs and assess the impact of mobility-driven transmission across different epidemic phases. Our results highlight Seoul and Gyeonggi as dominant sources of interregional spread, with their influence varying across phases of the pandemic. By distinguishing locally transmitted infections from mobility-induced cases, we introduce a data-driven approach to evaluate the effectiveness of movement restrictions and targeted interventions. Findings from the Pre-Delta phase demonstrate that mobility controls in transmission hubs significantly reduced the spread of infections. Our results underscore that densely connected regions disproportionately drive nationwide transmission, emphasizing the need for adaptive, phase-dependent intervention strategies rather than uniform nationwide policies. This study advances computational epidemiology by providing a scalable framework for integrating real-world mobility data with epidemic modeling to inform targeted, data-driven public health responses.

了解传染病传播的时空动态对有效防范和应对流行病至关重要。COVID-19的传播受到流动模式、区域连通性和不断发展的公共卫生干预措施的影响，因此很难量化特定区域的传播风险。我们的研究整合了干预驱动分析、真实世界数据和高分辨率模型，建立了评估区域间传播动态的强大计算框架。我们采用多斑块模型来估计随时间变化的区域有效繁殖数，并系统地量化区域间感染的贡献。通过整合来自韩国的高分辨率流动性和COVID-19发病率数据，我们确定了关键的传播中心，并评估了流动性驱动的传播在不同流行阶段的影响。我们的研究结果强调，首尔和京畿是区域间传播的主要来源，它们的影响在大流行的各个阶段有所不同。通过区分本地传播感染和活动引起的病例，我们引入了一种数据驱动的方法来评估活动限制和有针对性干预措施的有效性。前三角洲阶段的调查结果表明，传播中心的流动性控制大大减少了感染的传播。我们的研究结果强调，密集连接的地区不成比例地推动了全国范围的传播，强调需要适应性的、阶段性的干预策略，而不是统一的全国政策。这项研究通过提供一个可扩展的框架，将现实世界的流动性数据与流行病建模相结合，从而为有针对性的、数据驱动的公共卫生响应提供信息，从而推动了计算流行病学的发展。

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引用次数: 0

Machine learning-based short-term forecasting of COVID-19 hospital admissions using routine hospital patient data 基于机器学习的基于常规医院患者数据的COVID-19住院率短期预测

IF 2.4 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-12-18 DOI: 10.1016/j.epidem.2025.100877

Martin S. Wohlfender , Judith A. Bouman , Olga Endrich , Alban Ramette , Alexander B. Leichtle , Guido Beldi , Christian L. Althaus , Julien Riou

During the COVID-19 pandemic, the field of infectious disease modeling advanced rapidly, with forecasting tools developed to track trends in transmission dynamics and anticipate potential shortages of critical resources such as hospital capacity. In this study, we compared short-term forecasting approaches for COVID-19 hospital admissions that generate forecasts one to five weeks ahead, using retrospective electronic health records. We extracted different features (e.g., daily emergency department visits) from an individual-level patient dataset covering six hospitals located in the region of Bern, Switzerland, from February 2020 to June 2023. We then applied five methods – last-observation carried forward (baseline), linear regression, XGBoost and two types of neural networks – to time series using a leave-future-out training scheme with multiple cutting points and optimized hyperparameters. Performance was evaluated using the root mean square error between forecasts and observations. Generally, we found that XGBoost outperformed the other methods in predicting future hospital admissions. Our results also show that adding features such as the number of hospital admissions with fever and augmenting hospital data with measurements of viral concentration in wastewater improves forecast accuracy. This study offers a thorough and systematic comparison of methods applicable to routine hospital data for real-time epidemic forecasting. With the increasing availability and volume of electronic health records, improved forecasting methods will contribute to more precise and timely information during epidemic waves of COVID-19 and other respiratory viruses, thereby strengthening evidence-based public health decision-making.

在2019冠状病毒病大流行期间，传染病建模领域发展迅速，开发了预测工具来跟踪传播动态趋势并预测医院能力等关键资源的潜在短缺。在这项研究中，我们比较了COVID-19住院率的短期预测方法，这些方法使用回顾性电子健康记录提前一到五周进行预测。从2020年2月至2023年6月，我们从覆盖瑞士伯尔尼地区六家医院的个人患者数据集中提取了不同的特征（例如，每日急诊室就诊次数）。然后，我们应用了五种方法-最后一次观测（基线），线性回归，XGBoost和两种类型的神经网络-使用具有多个切割点和优化超参数的留未来训练方案来处理时间序列。使用预测和观测之间的均方根误差来评估性能。一般来说，我们发现XGBoost在预测未来住院率方面优于其他方法。我们的研究结果还表明，增加发烧住院人数等特征，以及通过测量废水中的病毒浓度来增加医院数据，可以提高预测的准确性。本研究对适用于医院常规数据的流行病实时预测方法进行了全面、系统的比较。随着电子健康记录的可用性和数量的增加，改进的预测方法将有助于在2019冠状病毒病和其他呼吸道病毒流行期间提供更准确和及时的信息，从而加强基于证据的公共卫生决策。

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引用次数: 0

Whose knowledge counts? Equity, epistemic justice, and reforming infectious disease research culture 谁的知识更重要？公平、认识公正与改革传染病研究文化。

IF 2.4 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-12-17 DOI: 10.1016/j.epidem.2025.100883

Hanna-Tina Fischer , Augustina Koduah

Infectious disease epidemiology is shaped by engrained research cultures that privilege biomedical and quantitative knowledge systems, systematically marginalizing qualitative, contextual, and locally informed approaches. These hierarchies reflect deeper inequities in who leads, who participates, and whose knowledge counts—disparities often patterned along geography, gender, language, and disciplinary background. This perspectives paper examines how funding priorities, academic training, and publishing norms sustain epistemic and structural exclusion, particularly for researchers based in the Global South. Drawing on Ghana’s COVID-19 response, we show how reliance on externally developed epidemiological models mirrored broader marginalization in research authorship, agenda-setting, and decision-making. We argue that equity-focused reforms in funding, training, and publishing—grounded in epistemic and distributive justice—are necessary to transform infectious disease research culture. A more just and inclusive research culture is not only an ethical imperative but essential to the effectiveness and legitimacy of epidemic responses.

传染病流行病学受到根深蒂固的研究文化的影响，这种文化推崇生物医学和定量知识系统，系统性地边缘化定性、情境性和当地知情方法。这些等级制度反映了谁领导、谁参与、谁的知识有价值等方面更深层的不平等——这种不平等通常是按地理、性别、语言和学科背景划分的。这篇远景论文考察了资助优先级、学术培训和出版规范如何维持认知和结构性排斥，特别是对全球南方的研究人员而言。以加纳应对COVID-19为例，我们展示了对外部开发的流行病学模型的依赖如何反映了在研究作者、议程设置和决策方面更广泛的边缘化。我们认为，以公平为重点的资助、培训和出版改革——以认识和分配公正为基础——对于改变传染病研究文化是必要的。更加公正和包容的研究文化不仅是一种道德要求，而且对流行病应对的有效性和合法性至关重要。

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引用次数: 0

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 : 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。

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引用次数: 0

UnMuted: Defining SARS-CoV-2 lineages according to temporally consistent mutation clusters in wastewater samples 根据废水样本中暂时一致的突变簇来定义SARS-CoV-2谱系

IF 2.4 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-12-16 DOI: 10.1016/j.epidem.2025.100876

Devan Becker

SARS-CoV-2 lineages are defined according to placement in a phylogenetic tree, but approximated by a list of mutations based on sequences collected from clinical sampling. Wastewater lineage abundance is generally found under the assumption that the mutation frequency is approximately equal to the sum of the abundances of the lineages to which it belongs. By leveraging numerous samples collected over time, I am able to estimate the temporal trends of the abundance of lineages as well as the definitions of those lineages. This is accomplished by assuming that collections of mutations that appear together over time can be used to define lineages.

Three main models are considered: One that does not imposes a temporal structure, one that includes an explicit temporal component but allows for missing lineages, and one with an explicit temporal component that attempts to estimate all lineages. It is found that the temporal trend of estimated lineage definitions approximately corresponds to the trend of lineage definitions determined by clinical samples, despite having no information from clinical samples.

SARS-CoV-2谱系是根据系统发育树中的位置来定义的，但通过基于从临床抽样中收集的序列的突变列表来近似定义。废水谱系丰度通常是在假设突变频率近似等于其所属谱系丰度之和的情况下发现的。通过利用随时间收集的大量样本，我能够估计谱系丰度的时间趋势，以及这些谱系的定义。这是通过假设随时间一起出现的突变集合可以用来定义谱系来实现的。考虑了三种主要的模型：一种不强加时间结构，一种包括显式时间成分但允许缺失的血统，一种具有显式时间成分，试图估计所有血统。研究发现，尽管没有来自临床样本的信息，但估计的谱系定义的时间趋势大致对应于临床样本确定的谱系定义的趋势。

引用次数: 0

Seroresponse to repeated infections with Salmonella enterica Typhi and Paratyphi A 对反复感染伤寒和甲型副伤寒沙门氏菌的血清反应

IF 2.4 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-12-15 DOI: 10.1016/j.epidem.2025.100874

Peter F.M. Teunis , Jessica C. Seidman , Dipesh Tamrakar , Farah Naz Qamar , Samir K. Saha , Denise O. Garrett , Jason R. Andrews , Richelle C. Charles , Kristen Aiemjoy

Enteric fever, a systematic bacterial infection caused by Salmonella Typhi and Paratyphi, continues to impose a significant public health burden in low and middle-income countries, yet our understanding of the serum antibody dynamics following infection remains incomplete. Although previous work has characterized the longitudinal seroresponses following acute typhoid infection, gaps persist in deciphering how repeated exposures influence antibody decay and protection. In our longitudinal cohort study of blood culture-confirmed enteric fever cases enrolled in Bangladesh, Nepal, and Pakistan, we identified several instances of suspected re-infection defined by an initial decline followed by a subsequent rise in antibody levels. The presence of re-infection events interferes with the estimation of antibody decay dynamics and influences the interpretation of seroepidemiological data at the population level. To study the seroresponses to subsequent infections we employed a synthetic within-host model that accounts for elevated baseline antibody levels at time of infection. Compared to the first seroresponse, second or later responses appear to have similar decay rates. As peak levels depend on the time between infections, a new model-derived metric is proposed that does not depend on time since the most recent infection: the minimum baseline antibody level at infection resulting in a small jump (protective) seroconversion. After infection the time to reach the minimum baseline level increases about tenfold. Finally, we show how ignoring variation in subsequent seroresponses into seroincidence estimates leads to bias in population-level infection rates. These findings underscore the importance of accounting for re-infection in seroepidemiological studies and provide refined metrics for interpreting antibody responses, with critical implications for assessing disease burden and guiding public health strategies in endemic regions.

肠热是由伤寒沙门氏菌和副伤寒沙门氏菌引起的系统性细菌感染，在低收入和中等收入国家继续造成重大的公共卫生负担，但我们对感染后血清抗体动态的了解仍然不完整。虽然以前的工作已经描述了急性伤寒感染后的纵向血清反应，但在破译反复暴露如何影响抗体衰变和保护方面仍然存在差距。在我们对孟加拉国、尼泊尔和巴基斯坦经血液培养确诊的肠热病例的纵向队列研究中，我们发现了几例疑似再感染的病例，其特征是抗体水平最初下降，随后上升。再感染事件的存在干扰了抗体衰减动力学的估计，并影响了人群水平上血清流行病学数据的解释。为了研究对后续感染的血清反应，我们采用了宿主内合成模型，该模型考虑了感染时基线抗体水平的升高。与第一次血清反应相比，第二次或以后的反应似乎有相似的衰减率。由于峰值水平取决于两次感染之间的时间，因此提出了一种新的模型衍生指标，该指标不依赖于自最近感染以来的时间：感染时导致小跳跃（保护性）血清转换的最低基线抗体水平。感染后达到最低基线水平的时间增加了大约10倍。最后，我们展示了在血清发病率估计中忽略后续血清反应的变化如何导致人群水平感染率的偏差。这些发现强调了在血清流行病学研究中考虑再感染的重要性，并为解释抗体反应提供了精确的指标，对评估疾病负担和指导流行地区的公共卫生战略具有重要意义。

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引用次数: 0

The epidemiology of pathogens with pandemic potential: A review of key parameters and clustering analysis 具有大流行潜力的病原体流行病学：关键参数和聚类分析综述

IF 2.4 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-12-13 DOI: 10.1016/j.epidem.2025.100882

Jack Ward , Oswaldo Gressani , Sol Kim , Niel Hens , W. John Edmunds

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.

鉴于2019冠状病毒病大流行，许多国家正试图扩大其大流行规划，而不是传统上以流感为重点。然而，不可能为每一种具有流行潜力的病原体制定详细的计划。我们开始试图通过审查具有大流行潜力的一系列病原体的流行病学并观察它们是否属于具有共同流行病学特征的群体来简化这一过程。方法对19种不同的具有大流行潜力的病原体（WHO重点关注的病原体、不同的流感毒株和Mpox毒株）进行流行病学分析。我们提取了关键参数（繁殖数序列间隔、症状前传播比例、病死率风险和传播途径）的数据，并应用了无监督学习算法。该方法结合了蒙特卡罗采样和集合聚类，根据病原体的共同特征将其分类为不同的流行病学原型。结果从154篇文献中提取了302篇流行病学参数估计。聚类算法将这些病原体分为六种原型(1)高传染性冠状病毒，(2)中度传染性冠状病毒，(3)高严重性接触和人畜共患病原体，(4)流感病毒(5)mers - cov样和(6)mpv样。结论对流行病学资料的无监督学习可用于确定不同的病原体原型。这种方法提供了一个有价值的框架，将新出现的和新的病原体分配到确定的群体中，以评估控制它们的常用方法。

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引用次数: 0

Fast and trustworthy nowcasting of dengue fever: A case study using attention-based probabilistic neural networks in São Paulo, Brazil 快速可靠的登革热临近预报：在巴西圣保罗使用基于注意力的概率神经网络的案例研究

IF 2.4 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-12-12 DOI: 10.1016/j.epidem.2025.100880

Silas Koemen , Nuno R. Faria , Leonardo S. Bastos , Oliver Ratmann , André Victor Ribeiro Amaral , on behalf of the Machine Learning & Global Health Network

Nowcasting methods are crucial in infectious disease surveillance, as reporting delays often lead to underestimation of recent incidence and can impair timely public health decision-making. Accurate real-time estimates of case counts are essential for resource allocation, policy responses, and communication with the public. In this paper, we propose a novel probabilistic neural network (PNN) architecture, named NowcastPNN, to estimate occurred-but-not-yet-reported cases of infectious diseases, demonstrated here using dengue fever incidence in São Paulo, Brazil. The proposed model combines statistical modelling of the true number of cases, assuming a Negative Binomial (NB) distribution, with recent advances in machine learning and deep learning, such as the attention mechanism. Uncertainty intervals are obtained by sampling from the predicted NB distribution and using Monte Carlo (MC) Dropout. Using proper scoring rules for the prediction intervals, NowcastPNN achieves nearly a 30% reduction in losses compared to the second-best model among other state-of-the-art approaches. While our model requires a large training dataset (equivalent to two to four years of incidence counts) to outperform benchmarks, it is computationally cheap and outperforms alternative methods even with significantly fewer observations as input. These features make the NowcastPNN model a promising tool for nowcasting in epidemiological surveillance of arboviral threats and other domains involving right-truncated data.

临近预报方法在传染病监测中至关重要，因为报告的延迟往往导致对最近发病率的低估，并可能影响及时的公共卫生决策。准确实时估计病例数对于资源分配、政策应对和与公众沟通至关重要。在本文中，我们提出了一种新的概率神经网络（PNN）架构，名为NowcastPNN，用于估计已发生但尚未报告的传染病病例，本文以巴西圣保罗的登革热发病率为例。提出的模型结合了真实案例数量的统计建模，假设负二项（NB）分布，以及机器学习和深度学习的最新进展，如注意机制。通过对预测的NB分布进行抽样并使用蒙特卡罗（MC） Dropout方法获得不确定性区间。使用适当的预测区间评分规则，NowcastPNN与其他最先进的方法中第二好的模型相比，损失减少了近30%。虽然我们的模型需要一个大的训练数据集（相当于2到4年的发生率计数）来优于基准，但它在计算上很便宜，即使输入的观测值少得多，也优于其他方法。这些特征使得NowcastPNN模型在虫媒病毒威胁和其他涉及右截尾数据的领域的流行病学监测中成为一个有前途的工具。

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