Epidemics最新文献_第2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 : 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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引用次数: 0

Insights from a Ventilation-Aware Pandemic and Outbreak Risk model (VAPOR) 来自通风感知的流行病和爆发风险模型的见解（蒸气）

IF 2.4 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-12-09 DOI: 10.1016/j.epidem.2025.100878

Natalie J. Wilson , Callandra Moore , Clara Eunyoung Lee , Ashleigh R. Tuite , David N. Fisman

Transmission of airborne pathogens in indoor spaces is strongly modulated by heterogeneity in ventilation. Understanding the role indoor air plays in pandemic risk is limited in part due to differing modeling approaches used in engineering and epidemiology. Here we present the VAPOR (Ventilation-Aware Pandemic and Outbreak Risk) model, a hybrid transmission framework that integrates Reed-Frost close-contact dynamics with Wells-Riley aerosol-mediated risk. Using a meta-population structure to simulate multi-patch environments (e.g., separate workplaces or schools), we explore how ventilation disparities shape epidemic potential. A fixed minority of individuals are modeled as “aerosolizers,” consistent with overdispersed real-world transmission patterns (e.g., SARS-CoV-2). Simulations reveal that both improving ventilation in high-risk patches and raising baseline ventilation across environments independently reduces risk. Parameter sweeps across air changes per hour (ACH, 2–12) demonstrate non-linear benefits with early saturation. These findings emphasize the need for targeted ventilation strategies and show how small-world effects amplify heterogeneity-driven transmission. VAPOR offers a framework for linking ventilation equity to epidemic control.

室内空间中空气传播的病原体受到通风不均一性的强烈调节。对室内空气在大流行风险中所起作用的理解有限，部分原因是工程和流行病学中使用的不同建模方法。在这里，我们提出了蒸汽（通风感知大流行和爆发风险）模型，这是一个混合传播框架，将Reed-Frost密切接触动力学与Wells-Riley气溶胶介导的风险集成在一起。使用元人口结构来模拟多斑块环境（例如，单独的工作场所或学校），我们探讨了通风差异如何影响流行病的可能性。固定的少数个体被建模为“气溶胶”，这与过度分散的现实世界传播模式（例如SARS-CoV-2）一致。模拟结果表明，改善高危区域的通风和提高不同环境的基线通风都能降低风险。参数扫描每小时空气变化（ACH, 2-12）显示出早期饱和的非线性效益。这些发现强调了有针对性的通风策略的必要性，并显示了小世界效应如何放大异质性驱动的传播。VAPOR为将通风公平与流行病控制联系起来提供了一个框架。

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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 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。

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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 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。

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引用次数: 0

The impact of household physical distancing and its timing on the transmission of SARS-CoV-2: Insights from a household transmission evaluation study 家庭保持身体距离及其时间对SARS-CoV-2传播的影响：来自家庭传播评估研究的见解

IF 2.4 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-12-01 DOI: 10.1016/j.epidem.2025.100868

Pietro Coletti , Niel Hens , Christel Faes , Huong Q. McLean , Edward A. Belongia , Melissa Rolfes , Alexandra Mellis , Carrie Reed , Jessica Biddle , Ahra Kim , Yuwei Zhu , H. Keipp Talbot , Carlos G. Grijalva

Background

Studies on SARS-CoV-2 household transmission often assume random mixing, overlooking detailed contact patterns and the timing of physical distancing.

Methods

To address this, we examined interactions within 280 households, including 280 index cases and 544 members, enrolled from April 2020 to April 2021 in Nashville, Tennessee, and central Wisconsin. Eligible households were enrolled within 7 days of index case symptom onset if at least one member was initially asymptomatic. Participants were monitored for 14 days, with symptoms and respiratory specimens collected daily, and contact data retrospectively assessed at three time points: the day before index case symptom onset, the day before enrollment, and 14 days post-enrollment. We fitted Exponential Random Graph Models to the contact pattern to identify drivers of household contact. We used the fitted household models to inform a two-level mixing model to account for community infection risk, and we calibrated it to the infection data. We then used the calibrated model to study different implementation of physical distancing.

Results

Contact patterns showed a significant reduction in physical interactions after infection awareness, particularly avoidance of index cases, with a 77% reduction in contact density (95% CI [65%-84%],

p < 0.001

). Simulations from the two-level mixing model indicated that initiating contact reductions at symptom onset could lower secondary infections by over 25% in households of 4-5 members.

Conclusions

These results demonstrate how behavior changes following infection awareness reduce transmission. Implementing physical distancing earlier, at symptom onset, could further limit secondary infections and enhance household transmission control.

背景：关于SARS-CoV-2家庭传播的研究往往假设随机混合，忽略了详细的接触模式和保持身体距离的时间。方法：为了解决这个问题，我们研究了2020年4月至2021年4月在田纳西州纳什维尔和威斯康星州中部登记的280个家庭的相互作用，其中包括280个指标病例和544个成员。如果至少有一名成员最初无症状，则在指标病例症状出现后7天内登记符合条件的家庭。对参与者进行为期14天的监测，每天采集症状和呼吸道标本，并在三个时间点回顾性评估接触者数据：指标病例症状出现前一天、入组前一天和入组后14天。我们将指数随机图模型拟合到接触模式中，以确定家庭接触的驱动因素。我们使用拟合的家庭模型来告知两级混合模型，以考虑社区感染风险，并将其校准为感染数据。然后，我们使用校准模型来研究物理距离的不同实施方式。结果：接触模式显示，在感染意识后，身体互动显著减少，特别是避免指数病例，接触密度降低77% （95% CI[65%-84%]）。结论：这些结果表明，感染意识后行为的改变如何减少传播。及早在出现症状时保持身体距离，可进一步限制继发感染并加强家庭传播控制。

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引用次数: 0

Integrating macroeconomic and public health impacts in social planning policies for pandemic response 将宏观经济和公共卫生影响纳入应对大流行病的社会规划政策

IF 2.4 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-12-01 DOI: 10.1016/j.epidem.2025.100873

Ofer Cornfeld , Kaicheng Niu , Oded Neeman , Michael Roswell , Gabi Steinbach , Stephen J. Beckett , Yorai Wardi , Joshua S. Weitz , Eran Yashiv

Infectious disease outbreaks with pandemic potential present challenges for mitigation and control. Policymakers must reduce disease-associated morbidity and mortality while also minimizing socioeconomic costs of interventions. At present, robust decision frameworks that integrate epidemic and macroeconomic dynamics to inform policy choices, given uncertainty in the current and future state of the outbreak and economic activity, are not widely available. In this study, we propose and analyze an economic-epidemic model to identify robust planning policies that limit epidemic impacts while maintaining economic activity. We compare alternative fixed, dynamic open-loop optimal control, and feedback control policies via a welfare loss framework. We find that open-loop policies that adjust employment dynamically while maintaining a flat epidemic curve outperform fixed employment reduction policies. However, open-loop policies are highly sensitive to misestimation of parameters associated with intrinsic disease strength and feedback between economic activity and transmission, leading to potentially significant increases in welfare loss. In contrast, feedback control policies guided by open-loop dynamical targets of the time-varying reproduction number perform near-optimally when parameters are well-estimated, while significantly outperforming open-loop policies whenever disease transmission and population-scale behavioral response parameters are misestimated — as they inevitably are. Our study provides a template for integrating principled economic models with epidemic scenarios to identify policy vulnerabilities and expand policy options in preparation for future pandemics. Across disease scenarios, we show that policies that temporarily limit economic activity and disease transmission reduce both disease-driven mortality and cumulative loss of economic activity. Our study suggests that future preparedness depends on feasible, robust, and adaptive policies and can help avoid false dichotomies in choosing between public health and economic outcomes.

具有大流行可能性的传染病暴发对缓解和控制提出了挑战。决策者必须降低与疾病相关的发病率和死亡率，同时尽量减少干预措施的社会经济成本。鉴于目前和未来疫情状况以及经济活动的不确定性，目前还没有广泛提供强有力的决策框架，将流行病和宏观经济动态结合起来，为政策选择提供信息。在本研究中，我们提出并分析了一个经济流行病模型，以确定在保持经济活动的同时限制流行病影响的稳健规划政策。我们通过福利损失框架比较了不同的固定、动态开环最优控制和反馈控制策略。我们发现，在保持平坦的流行病曲线的同时动态调整就业的开环政策优于固定的就业减少政策。然而，开环政策对与内在疾病强度和经济活动与传播之间的反馈相关的参数的错误估计高度敏感，从而导致福利损失的潜在显著增加。相比之下，当参数估计良好时，由时变繁殖数的开环动态目标指导的反馈控制策略表现接近最优，而当疾病传播和种群尺度的行为反应参数被错误估计时（这是不可避免的），反馈控制策略的表现明显优于开环策略。我们的研究提供了一个模板，用于将有原则的经济模型与流行病情景相结合，以确定政策脆弱性并扩大政策选择，为未来的流行病做准备。在各种疾病情况下，我们表明，暂时限制经济活动和疾病传播的政策既降低了疾病导致的死亡率，也降低了经济活动的累积损失。我们的研究表明，未来的准备工作取决于可行、稳健和适应性强的政策，并有助于避免在公共卫生和经济结果之间做出选择时出现错误的二分法。

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