Epidemics最新文献_第6页

Estimating the effective reproduction number from wastewater (Rt): A methods comparison 估计废水的有效再生数（Rt）：一种方法的比较

IF 3 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-06-18 DOI: 10.1016/j.epidem.2025.100839

Dustin T. Hill , Yifan Zhu , Christopher Dunham , E. Joe Moran , Yiquan Zhou , Mary B. Collins , Brittany L. Kmush , David A. Larsen

Background

The effective reproduction number (R_t) is a dynamic indicator of current disease spread risk. Wastewater measurements of viral concentrations are known to correlate with clinical measures of diseases and have been incorporated into methods for estimating the R_t.

Methods

We review wastewater-based methods to estimate the R_t for SARS-CoV-2 based on similarity to the reference case-based R_t, ease of use, and computational requirements. Using wastewater data collected between August 1, 2022, and February 20, 2024, from 205 wastewater treatment plants across New York State, we fit eight wastewater R_t models identified from the literature. Each model is compared to the R_t estimated from case data for New York at the sewershed (wastewater treatment plant catchment area), county, and state levels.

Results

We find a high degree of similarity across all eight methods despite differences in model parameters and approach. Further, two methods based on the common measures of percent change and linear fit reproduced the R_t from case data very well and a GLM accurately predicted case data. Model output varied between spatial scales with some models more closely estimating sewershed R_t values than county R_t values. Similarity to clinical models was also highly correlated with the proportion of the population served by sewer in the surveilled communities (r = 0.77).

Conclusions

While not all methods that estimate R_t from wastewater produce the same results, they all provide a way to incorporate wastewater concentration data into epidemic modeling. Our results show that straightforward measures like the percent change can produce similar results of more complex models. Based on the results, researchers and public health officials can select the method that is best for their situation.

有效繁殖数（Rt）是当前疾病传播风险的动态指标。已知废水中病毒浓度的测量与疾病的临床测量相关，并已被纳入估计Rt的方法中。方法基于与参考病例Rt的相似性、易用性和计算要求，我们综述了基于废水的Rt估计方法。利用2022年8月1日至2024年2月20日期间从纽约州205家污水处理厂收集的废水数据，我们拟合了从文献中确定的8个废水Rt模型。每个模型都与纽约下水道（污水处理厂集水区）、县和州一级的病例数据估计的Rt进行比较。结果尽管在模型参数和方法上存在差异，但我们发现所有八种方法都具有高度的相似性。此外，基于变化百分比和线性拟合的两种常用测量方法可以很好地再现病例数据的Rt，并且GLM可以准确地预测病例数据。模型的输出在不同的空间尺度上存在差异，一些模型对下水道Rt值的估计比县Rt值更接近。与临床模型的相似性也与监测社区下水道服务的人口比例高度相关（r = 0.77）。虽然并非所有从废水中估计Rt的方法都产生相同的结果，但它们都提供了一种将废水浓度数据纳入流行病建模的方法。我们的结果表明，像百分比变化这样简单的度量可以在更复杂的模型中产生类似的结果。根据结果，研究人员和公共卫生官员可以选择最适合他们情况的方法。

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引用次数: 0

Incident COVID-19 infections before Omicron in the U.S. 在美国欧米克隆之前发生的COVID-19感染事件

IF 3 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-06-09 DOI: 10.1016/j.epidem.2025.100838

Rachel Lobay , Ajitesh Srivastava , Ryan J. Tibshirani , Daniel J. McDonald

The timing and magnitude of COVID-19 infections are of interest to the public and to public health, but these are challenging to ascertain due to the volume of undetected asymptomatic cases and reporting delays. Accurate estimates of COVID-19 infections based on finalized data can improve understanding of the pandemic and provide more meaningful quantification of disease patterns and burden. Therefore, we retrospectively estimate daily incident infections for each U.S. state prior to Omicron. To this end, reported COVID-19 cases are deconvolved to their likely date of infection onset using delay distributions estimated from the CDC line list. Then, a novel serology-driven model is used to scale these deconvolved cases to account for the unreported infections. The resulting infection estimates incorporate variant-specific incubation periods, reinfections, and waning antigenic immunity. They clearly demonstrate that reported cases failed to reflect the full extent of disease burden in all states. Most notably, infections were severely underreported during the Delta wave, with an estimated reporting rate as low as 6.3% in New Jersey, 7.3% in Maryland, and 8.4% in Nevada. Moreover, in 44 states, fewer than 1/3 of infections eventually appeared as case reports, and there were sustained periods where surges in infections were virtually undetectable through reported cases. This pattern was clearly illustrated by North and South Dakota during the spring of 2021, as well as by several Northeastern states during the Delta wave of late summer that year. While reported cases offered a convenient proxy of disease burden, they failed to capture the full extent of infections and severely underestimated the true disease burden. Our retrospective analysis also estimates other important quantities for every state, including variant-specific deconvolved cases, time-varying case ascertainment ratios, as well as infection-hospitalization and infection-fatality ratios.

COVID-19感染的时间和规模是公众和公共卫生关注的问题，但由于大量未发现的无症状病例和报告延误，这些问题很难确定。根据最终数据对COVID-19感染进行准确估计，可以增进对大流行的了解，并提供更有意义的疾病模式和负担量化。因此，我们回顾性地估计了美国各州在欧米克隆之前的每日感染事件。为此，根据CDC清单估计的延迟分布，将报告的COVID-19病例解卷积到其可能的感染发病日期。然后，使用一种新的血清学驱动模型对这些解卷积病例进行缩放，以解释未报告的感染。由此产生的感染估计包括变异特异性潜伏期、再感染和抗原免疫减弱。它们清楚地表明，报告的病例未能反映所有州疾病负担的全部程度。最值得注意的是，在三角洲波期间，感染严重低估，估计报告率在新泽西州低至6.3%，马里兰州为7.3%，内华达州为8.4%。此外，在44个州，不到三分之一的感染最终以病例报告的形式出现，并且在持续的一段时间里，感染的激增几乎无法通过报告的病例检测到。这种模式在2021年春季的北达科他州和南达科他州以及当年夏末的三角洲波期间的几个东北部州都得到了清楚的说明。虽然报告的病例提供了疾病负担的方便代表，但它们未能捕捉到感染的全部程度，并严重低估了真正的疾病负担。我们的回顾性分析还估计了每个州的其他重要数量，包括变异特异性反卷积病例，随时间变化的病例确定比率，以及感染住院率和感染病死率。

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引用次数: 0

Verifying infectious disease scenario planning for geographically diverse populations 核实地理上不同人群的传染病情景规划

IF 3 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-06-06 DOI: 10.1016/j.epidem.2025.100833

Jessica R. Conrad , Paul W. Fenimore , Kelly R. Moran , Marisa C. Eisenberg

In the face of the COVID-19 pandemic, the literature saw a spike in publications for epidemic models, and a renewed interest in capturing contact networks and geographic movement of populations. There remains a general lack of consensus in the modeling community around best practices for spatiotemporal epi-modeling, specifically as it pertains to the infection rate formulation and the underlying contact or mixing model.

We mathematically verify several common modeling assumptions in the literature, to prove when certain choices can provide consistent results across different geographic resolutions, population densities and patterns, and mixing assumptions. The most common infection rate formulation, a computationally low cost per capita infection rate assumption, fails the consistency tests for heterogeneous populations and gravity-weighting assumptions. Future modeling efforts in spatiotemporal disease modeling should be wary of this limitation, particularly when working with more heterogeneous or sparse populations.

Our results provide guidance for testing that a model preserves desirable properties even when model inputs mask potential problems due to symmetry or homogeneity. We also provide a recipe for performing this type of verification, strengthening decision support tools.

面对COVID-19大流行，流行病模型的出版物激增，人们对捕捉接触网络和人口地理流动的兴趣重新燃起。在建模界，对于时空epi建模的最佳实践，特别是在感染率公式和潜在的接触或混合模型方面，仍然普遍缺乏共识。我们在数学上验证了文献中几个常见的建模假设，以证明某些选择何时可以在不同的地理分辨率、人口密度和模式以及混合假设中提供一致的结果。最常见的感染率公式，即计算成本较低的人均感染率假设，无法通过异质人群和重力加权假设的一致性检验。未来在时空疾病建模方面的建模工作应该警惕这一限制，特别是在处理更异质或稀疏的种群时。我们的结果为测试提供了指导，即使当模型输入掩盖了由于对称性或同质性造成的潜在问题时，模型也保留了理想的属性。我们还提供了执行这种类型的验证的配方，加强决策支持工具。

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引用次数: 0

A Cluster-Aggregate-Pool (CAP) ensemble algorithm for improved forecast performance of influenza-like illness 提高流感样疾病预测性能的聚类-聚合-池（CAP）集成算法

IF 3 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-06-03 DOI: 10.1016/j.epidem.2025.100832

Ningxi Wei , Xinze Zhou , Wei-Min Huang , Thomas McAndrew

Seasonal influenza causes on average 425,000 hospitalizations and 32,000 deaths per year in the United States. Forecasts of influenza-like illness (ILI) — a surrogate for the proportion of patients infected with influenza — support public health decision making. The goal of an ensemble forecast of ILI is to increase accuracy and calibration compared to individual forecasts and to provide a single, cohesive prediction of future influenza. However, an ensemble may be composed of models that produce similar forecasts, causing issues with ensemble forecast performance and non-identifiability. To improve upon the above issues we propose a novel Cluster-Aggregate-Pool or ‘CAP’ ensemble algorithm that first groups together individual forecasts into clusters, aggregates forecasts that belong to the same cluster into a single forecast (called a cluster forecast), and then pools together cluster forecasts via a linear pool. We evaluated this algorithm on a benchmark dataset of 7 seasons of ILI plus forecasts generated by 27 individual models as part of the FluSight project. When compared to a non-CAP approach, we find that a CAP ensemble improves calibration by approximately 10% while maintaining similar accuracy to non-CAP alternatives. In addition, our CAP algorithm (i) generalizes past ensemble work associated with influenza forecasting and introduces a framework for future ensemble work, (ii) automatically accounts for missing forecasts from individual models, (iii) allows public health officials to participate in the ensemble by assigning individual models to clusters, and (iv) provide an additional signal about when peak influenza may be near.

在美国，季节性流感每年平均造成425,000人住院，32,000人死亡。流感样疾病（ILI）的预测——感染流感患者比例的替代指标——支持公共卫生决策。流感综合预报的目标是提高与单项预报相比的准确性和校准性，并提供对未来流感的单一、有凝聚力的预测。然而，一个集成可能由产生相似预测的模型组成，从而导致集成预测性能和不可识别性的问题。为了改进上述问题，我们提出了一种新的cluster - aggregate - pool或“CAP”集成算法，该算法首先将单个预测分组为集群，将属于同一集群的预测聚合为单个预测（称为集群预测），然后通过线性池将集群预测集合在一起。作为FluSight项目的一部分，我们在由27个独立模型生成的7个季节ILI和预测的基准数据集上对该算法进行了评估。与非CAP方法相比，我们发现CAP集成在保持与非CAP替代方法相似的精度的同时，将校准提高了约10%。此外，我们的CAP算法(i)概括了过去与流感预测相关的集成工作，并为未来的集成工作引入了一个框架，（ii）自动解释单个模型的缺失预测，（iii）允许公共卫生官员通过将单个模型分配给集群来参与集成，以及（iv）提供关于流感高峰何时可能接近的额外信号。

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引用次数: 0

Integrative modeling of the spread of serious infectious diseases and corresponding wastewater dynamics 严重传染病传播的综合建模和相应的废水动力学

IF 3 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-06-01 DOI: 10.1016/j.epidem.2025.100836

Nina Schmid , Julia Bicker , Andreas F. Hofmann , Karina Wallrafen-Sam , David Kerkmann , Andreas Wieser , Martin J. Kühn , Jan Hasenauer

The COVID-19 pandemic has emphasized the critical need for accurate disease modeling to inform public health interventions. Traditional reliance on confirmed infection data is often hindered by reporting delays and under-reporting, while antigen or antibody testing of a full cohort can be costly and impractical. Wastewater-based surveillance offers a promising alternative by detecting viral concentrations from fecal shedding, potentially providing a more accurate estimate of true infection prevalence. However, challenges remain in optimizing sampling protocols, locations, and normalization strategies, particularly in accounting for environmental factors like precipitation.

We present an integrative model that simulates the spread of serious infectious diseases by linking detailed infection dynamics with wastewater processes through viral shedding curves. Through comprehensive simulations, we examine how virus characteristics, precipitation events, measurement protocols, and normalization strategies affect the relationship between infection dynamics and wastewater measurements. Our findings reveal a complex relationship between disease prevalence and corresponding wastewater concentrations, with key variability sources including upstream sampling locations, continuous rainfall, and rapid viral decay. Notably, we find that flow rate normalization can be unreliable when rainwater infiltrates sewer systems. Despite these challenges, our study demonstrates that wastewater-based surveillance data can serve as a leading indicator of disease prevalence, predicting outbreak peaks before they occur. The proposed integrative model can thus be used to optimize wastewater-based surveillance, enhancing its utility for public health monitoring.

2019冠状病毒病大流行强调了对准确疾病建模的迫切需要，以便为公共卫生干预提供信息。传统上对确诊感染数据的依赖常常受到报告延迟和报告不足的阻碍，而对整个队列进行抗原或抗体检测可能既昂贵又不切实际。基于废水的监测通过检测粪便排出的病毒浓度提供了一种有希望的替代方案，可能提供对真实感染流行率的更准确估计。然而，在优化采样协议、位置和标准化策略方面仍然存在挑战，特别是在考虑降水等环境因素方面。我们提出了一个综合模型，通过病毒脱落曲线将详细的感染动力学与废水处理联系起来，模拟了严重传染病的传播。通过综合模拟，我们研究了病毒特征、降水事件、测量方案和标准化策略如何影响感染动态和废水测量之间的关系。我们的研究结果揭示了疾病流行与相应的废水浓度之间的复杂关系，主要变异性来源包括上游采样地点、连续降雨和快速病毒衰变。值得注意的是，我们发现当雨水渗入下水道系统时，流量归一化是不可靠的。尽管存在这些挑战，但我们的研究表明，基于废水的监测数据可以作为疾病流行的主要指标，在疫情发生之前预测疫情峰值。因此，所提出的综合模型可用于优化基于废水的监测，提高其对公共卫生监测的效用。

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引用次数: 0

A CFD-informed barn-level swine disease dissemination model and its use for ventilation optimization 基于cfd的猪舍级猪疾病传播模型及其在通风优化中的应用

IF 3 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-05-24 DOI: 10.1016/j.epidem.2025.100835

Maryam Safari , Christian Fleming , Jason A. Galvis , Aniruddha Deka , Felipe Sanchez , Gustavo Machado , Chi-An Yeh

The airborne spread of infectious livestock diseases plays a crucial role in the propagation of epidemics, particularly in populations confined to densely populated facilities, such as commercial swine barns. In this study, we present a framework to study airborne disease dissemination within commercial swine barns and facilitate the strategic design of control actions, including optimization of ventilation and placement of sick animals (sick pen). This framework is based on a susceptible–infected–recovered (SIR) model that accounts for the between-pen disease spread within swine barns. A pen-to-pen contact network is used to construct a transmission matrix according to the transport of airborne respiratory pathogens across pens in the barns, via our Reynolds-averaged Navier–Stokes computational fluid dynamics (CFD) solver. By employing this CFD-augmented SIR model, we demonstrated that the location of the sick pen and the barn ventilation configuration played crucial roles in modifying disease dissemination dynamics at the barn level. In addition, we examined the effect of natural ventilation through different curtain adjustments. We observed that curtain adjustments either suppress the disease spread by an average of 64.8% or exacerbate the outbreak potential by an average of 5.8%, compared to the scenario where side curtains are not raised. Furthermore, we optimize the ventilation configuration via the selection and placement of ventilation fans through the integration of the CFD-augmented framework with the genetic algorithm to minimize the dissemination of swine disease within barns. Compared to the original barn ventilation settings, our optimized ventilation system significantly reduced disease spread by an average of 20%. Our study demonstrates that the use of the proposed framework provides a detailed understanding of the flow physics and the transport of airborne pathogens, which facilitate the optimization of ventilation systems and strategic management of sick pens within the swine barns.

传染性牲畜疾病的空气传播在流行病的传播中起着至关重要的作用，特别是在被限制在人口密集设施（如商业猪舍）内的人群中。在本研究中，我们提出了一个研究商业猪舍内空气传播疾病的框架，并促进控制行动的战略设计，包括优化通风和病畜（病栏）的放置。这个框架是基于一个易感-感染-恢复（SIR）模型，该模型解释了猪舍内猪圈间疾病的传播。通过reynolds -average Navier-Stokes计算流体动力学（CFD）求解器，利用笔对笔的接触网络，根据空气传播的呼吸道病原体在畜舍内的传播构建传播矩阵。通过使用这种cfd增强SIR模型，我们证明了病栏的位置和畜棚通风配置在畜棚水平改变疾病传播动态方面起着至关重要的作用。此外，我们通过不同的窗帘调节来检验自然通风的效果。我们观察到，与不抬高侧窗帘的情况相比，调整窗帘或平均抑制疾病传播64.8%，或平均加剧爆发潜力5.8%。此外，我们通过将cfd增强框架与遗传算法相结合，通过通风机的选择和放置来优化通风配置，以最大限度地减少猪舍内猪疾病的传播。与原有的谷仓通风设置相比，我们优化的通风系统显着降低了疾病传播的平均20%。我们的研究表明，使用所提出的框架可以详细了解空气传播病原体的流动物理和运输，这有助于优化猪舍内的通风系统和病栏的战略管理。

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引用次数: 0

Assessing the effectiveness of travel control measures in preventing imported COVID-19 cases reveals the critical role of travel volume 评估旅行控制措施在预防新冠肺炎输入性病例中的有效性，揭示了旅行数量的关键作用

IF 3 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-05-15 DOI: 10.1016/j.epidem.2025.100837

Mingwei Li , Karen A. Grépin , Ru Zhang , Benjamin J. Cowling , Bingyi Yang

Background

Although travel control measures have played a key role in mitigating COVID-19 spread in certain regions, few empirical observational studies have specifically quantified their effectiveness in preventing the importation of infectious cases into communities. In Hong Kong, layered policies (e.g., mandatory quarantine, staggered testing protocols, and phased travel volume restriction) provided a natural experiment to disentangle these components. Our study evaluates the contributions of each measure to preventing imported infectious cases releasing to community.

Methods

We retrospectively assessed these measures' effectiveness in Hong Kong, utilizing data from eight countries during 2020–2021. Data on imported COVID-19 cases, including departure origins and time from arrival to report, was compiled. To estimate the SARS-CoV-2 prevalence among inbound travelers, we used a Bayesian framework that accounted for the disease history and testing sensitivity and fitted to cases detected on arrival and travel volumes. We compared the number of prevented infections under the implemented measures to a scenario where no measures were taken. We also conducted counterfactual analysis to examine the independent and marginal effects of individual measures.

Results

Stringent travel measures prevented 9821 (9065 – 10,564) importations from entering Hong Kong. Travel volume reductions had the greatest impact (93.0 % reduction, 95 % confidence interval, CI: 91.9 %-93.9 %), followed by mandatory quarantine (80.8 % reduction, 95 % CI: 75.7 % - 87.1 %). In-quarantine COVID-19 testing showed no substantial additional effectiveness in preventing infectious COVID-19 cases into community (81.8 % reduction, 95 % CI:74.8 %-87.1 %) beyond mandatory quarantine alone.

Conclusions

Our findings demonstrate that while stringent post-arrival measures effectively reduced community transmission of imported COVID-19 cases, travel volume reduction played a critical and independent role in limiting viral importation, regardless of post-arrival interventions.

尽管旅行控制措施在缓解COVID-19在某些地区的传播方面发挥了关键作用，但很少有实证观察性研究具体量化其在防止传染性病例输入社区方面的有效性。在香港，分层政策（例如，强制隔离、交错检测协议和分阶段旅行量限制）提供了一个自然的实验来解开这些组成部分。本研究评估了各项措施对预防输入性传染病向社区传播的贡献。方法利用2020-2021年八个国家的数据，回顾性评估了这些措施在香港的有效性。汇总了输入性COVID-19病例的数据，包括出发地和从抵达到报告的时间。为了估计入境旅客中SARS-CoV-2的流行率，我们使用了一个贝叶斯框架，该框架考虑了疾病史和检测敏感性，并适用于抵达时发现的病例和旅行量。我们比较了在实施措施和未采取措施的情况下预防感染的数量。我们还进行了反事实分析，以检验个别措施的独立和边际效应。结果严格的旅行措施阻止9821例（9065 ~ 10564例）入境。减少旅行量的影响最大（减少93.0 %，95% %置信区间，CI: 91.9 %-93.9 %），其次是强制隔离（减少80.8 %,95% % CI: 75.7 % - 87.1 %）。隔离内COVID-19检测显示，除了单独强制隔离外，在预防传染性COVID-19病例进入社区方面没有实质性的额外效果（减少81.8 %,95% % CI:74.8 %-87.1 %）。结论虽然严格的入境后措施有效减少了输入性COVID-19病例的社区传播，但无论是否采取入境后干预措施，减少旅行量在限制病毒输入方面发挥了关键而独立的作用。

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引用次数: 0

Ceasing sampling at wastewater treatment plants where viral dynamics are most predictable 停止在最容易预测病毒动态的污水处理厂取样

IF 3 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-05-09 DOI: 10.1016/j.epidem.2025.100834

Mo Liu, Devan G. Becker

Wastewater sampling has been shown to be an effective tool for monitoring the dynamics of an infectious disease. During the COVID-19 pandemic, many sampling sites were opened in order to capture as much information as possible. However, with the pandemic waning, not all sampling sites need to continue operating.

In this work, we investigate a method for evaluating sampling sites for which sampling can stop. We apply machine learning methods to predict the mutation frequencies from wastewater sites on the next day in one location based on the frequencies on previous days in other locations, then record the prediction error. The sites with the lowest prediction error are the ones that contain the least amount of unique information, and sampling can cease at those locations. We demonstrate a systematic approach to evaluating prediction errors and several interpretations of the error. We demonstrate this method on five locations in Switzerland, finding two locations that could be removed with minimal information loss.

废水取样已被证明是监测传染病动态的有效工具。在2019冠状病毒病大流行期间，开放了许多采样点，以便获取尽可能多的信息。然而，随着大流行的减弱，并非所有采样点都需要继续运作。在这项工作中，我们研究了一种评估采样点的方法，该采样点可以停止采样。我们应用机器学习方法，根据其他地点前几天的频率，预测一个地点第二天废水站点的突变频率，然后记录预测误差。预测误差最低的地点是包含最少量独特信息的地点，采样可以在这些地点停止。我们展示了评估预测误差的系统方法和对误差的几种解释。我们在瑞士的五个位置上演示了这种方法，找到了两个可以以最小的信息损失移除的位置。

引用次数: 0

Modeling the transmission dynamics of African swine fever virus within commercial swine barns: Quantifying the contribution of multiple transmission pathways 模拟非洲猪瘟病毒在商业猪舍内的传播动态：量化多种传播途径的贡献

IF 3 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-04-24 DOI: 10.1016/j.epidem.2025.100828

Aniruddha Deka , Jason A. Galvis , Christian Fleming , Maryam Safari , Chi-An Yeh , Gustavo Machado

The transmission of African swine fever virus (ASFV) within swine barns occurs through direct and indirect pathways. Identifying and quantifying the roles of ASFV dissemination within barns is crucial for developing disease control strategies. We created a stochastic transmission model to examine the ASFV dissemination dynamics through transmission routes within commercial swine barns. We consider seven transmission routes at three disease dynamics levels: within-pens, between-pens, and within-room transmission, along with the transfer of pigs between pens within rooms. We simulated ASFV spread within barns of various sizes and layouts from rooms with a median of 32 pens (IQR: 28-40), where each pen housed a median of 34 pigs (IQR: 29-36). Our model enables tracking the viral load in each pen and monitoring the disease status at the pen level. Results show that between-pen transmission pathways exhibited the highest contribution in spread, accounting for 66.76%, whereas within-pen and within-room pathways account for 26.12% and 7.12%, respectively. Nose-to-nose contact between pens was the primary dissemination route, comprising an average of 46.04%. On the other hand, aerosol transmission within pens had the lowest contribution, accounting for less than 1%. Furthermore, we show that the daily transfer of pigs between pens did not impact the spread of ASFV. On average, at the room level, the combined approach of passive daily surveillance and mortality-focused surveillance enabled ASFV detection within 18 (IQR: 16-19) days. The model allows us to monitor the viral load variation across the room over time, revealing that most of the viral load accumulates in pens closer to the exhaust fans after a month. This work significantly deepens our understanding of ASFV spread within commercial swine production farms in the U.S. and highlights the main transmission pathways that should be prioritized when implementing ASFV countermeasure actions at the room level.

非洲猪瘟病毒（ASFV）在猪舍内通过直接和间接途径传播。确定和量化猪舍内ASFV传播的作用对于制定疾病控制策略至关重要。我们建立了一个随机传播模型，通过商业猪舍内的传播途径来研究非洲猪瘟的传播动态。我们考虑了三种疾病动力学水平上的七种传播途径：围栏内、围栏之间和房间内传播，以及猪在房间内围栏之间的转移。我们模拟了不同大小和布局的猪舍内ASFV的传播，每个猪舍中位数为32个猪圈（IQR: 28-40），每个猪圈中位数为34头猪（IQR: 29-36）。我们的模型可以跟踪每个笔中的病毒载量，并在笔的水平上监测疾病状态。结果表明，笔间传播途径对传播的贡献最大，占66.76%，笔内传播途径和室内传播途径分别占26.12%和7.12%。猪栏间的鼻对鼻接触是主要传播途径，平均占46.04%。另一方面，围栏内的气溶胶传播贡献最低，占不到1%。此外，我们表明猪在猪圈之间的日常转移并不影响非洲猪瘟的传播。平均而言，在房间一级，被动每日监测和以死亡率为重点的监测相结合的方法能够在18天（IQR: 16-19）内发现非洲猪瘟病毒。该模型使我们能够监测房间内病毒载量随时间的变化，揭示出一个月后，大多数病毒载量积聚在靠近排气扇的笔中。这项工作大大加深了我们对美国商业养猪场中ASFV传播的理解，并强调了在室内实施ASFV对策行动时应优先考虑的主要传播途径。

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引用次数: 0

Modelling the interplay between responsive individual vaccination decisions and the spread of SARS-CoV-2 对反应性个体疫苗接种决策与SARS-CoV-2传播之间的相互作用进行建模

IF 3 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-04-23 DOI: 10.1016/j.epidem.2025.100831

Karina Wallrafen-Sam , Maria Garcia Quesada , Benjamin A. Lopman , Samuel M. Jenness

Background

COVID-19 vaccine hesitancy proved to be a major barrier to higher uptake, but it is unclear whether interventions targeting hesitancy could result in substantial prevention benefits. Epidemic models that represent vaccine decision-making psychology can provide insight into the potential impact of vaccine promotion interventions in the context of the COVID-19 pandemic and future epidemics of vaccine-preventable diseases.

Methods

We coupled a network- and agent-based model of SARS-CoV-2 transmission with a social-psychological vaccination decision-making model in which vaccine side effects and breakthrough infections could “nudge” individuals towards vaccine resistance while spikes in COVID-19 hospitalizations could nudge them towards vaccine willingness. This model was parameterized and calibrated to represent the COVID-19 epidemic in Georgia, USA from January 2021 to August 2022. We modelled counterfactual scenarios in which increases to resistant-to-willing nudges were combined with decreases to willing-to-resistant nudges. We compared cumulative vaccine doses administered, SARS-CoV-2 incidence, and COVID-related deaths across scenarios.

Results

Increasing the probability of hospitalization-prompted resistant-to-willing nudges increased vaccine uptake by as much as 5.4 % and decreased SARS-CoV-2 incidence by as much as 4.0 %. In contrast, decreasing the probability of breakthrough infection-related willing-to-resistant nudges had a negligible impact on further vaccination and disease outcomes.

Conclusions

Vaccine promotion interventions that address community-level factors influencing decision-making may have a greater ability to avert SARS-CoV-2 infections than those targeted to individual vaccination and infection history. Additionally, reactive vaccine promotion interventions may have only limited prevention benefits in the short term, suggesting that attention should be paid to formulating interventions that accurately anticipate the case curve.

背景：covid -19疫苗犹豫被证明是提高吸收率的主要障碍，但目前尚不清楚针对犹豫的干预措施是否能带来实质性的预防益处。代表疫苗决策心理的流行病模型可以深入了解在COVID-19大流行和未来疫苗可预防疾病流行背景下疫苗促进干预措施的潜在影响。方法将基于网络和代理的SARS-CoV-2传播模型与社会心理疫苗接种决策模型相结合，其中疫苗副作用和突破性感染可能“推动”个体对疫苗产生耐药性，而COVID-19住院治疗的高峰可能推动他们对疫苗的意愿。对该模型进行了参数化和校准，以代表2021年1月至2022年8月在美国佐治亚州的COVID-19流行。我们模拟了反事实的情景，在这种情况下，从抵抗到愿意的推动的增加与从愿意到抵抗的推动的减少相结合。我们比较了不同情况下的累积疫苗剂量、SARS-CoV-2发病率和covid相关死亡。结果增加住院诱导的耐药-自愿轻推的概率，可使疫苗接种率提高5.4% %，使SARS-CoV-2发病率降低4.0% %。相比之下，降低与感染相关的突破意愿的可能性，对进一步接种疫苗和疾病结果的影响可以忽略不计。结论针对社区层面决策影响因素的疫苗推广干预措施可能比针对个人疫苗接种和感染史的干预措施更能避免SARS-CoV-2感染。此外，反应性疫苗推广干预措施在短期内可能只有有限的预防效益，这表明应注意制定能够准确预测病例曲线的干预措施。

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