Epidemics最新文献_第7页

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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引用次数: 0

Accounting for the geometry of the respiratory tract in viral infections 在病毒感染中考虑呼吸道的几何形状

IF 3 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-04-23 DOI: 10.1016/j.epidem.2025.100829

Thomas Williams , James M. McCaw , James M. Osborne

Increasingly, experimentalists and modellers alike have come to recognise the important role of spatial structure in infection dynamics. Almost invariably, spatial computational models of viral infections — as with in vitro experimental systems — represent the tissue as wide and flat, which is often assumed to be representative of the entire affected tissue within the host. However, this assumption fails to take into account the distinctive geometry of the respiratory tract in the context of viral infections. The respiratory tract is characterised by a tubular, branching structure, and moreover is spatially heterogeneous: deeper regions of the lung are composed of far narrower airways and are associated with more severe infection. Here, we extend a typical multicellular model of viral dynamics to account for two essential features of the geometry of the respiratory tract: the tubular structure of airways, and the branching process between airway generations. We show that, with this more realistic tissue geometry, the dynamics of infection are substantially changed compared to standard computational and experimental approaches, and that the resulting model is equipped to tackle important biological phenomena that do not arise in a flat host tissue, including viral lineage dynamics, and heterogeneity in immune responses to infection in different regions of the respiratory tree. Our findings suggest aspects of viral dynamics which current in vitro systems may be insufficient to describe, and points to several features of respiratory infections which can be experimentally assessed.

越来越多的实验学家和建模者都开始认识到空间结构在感染动力学中的重要作用。几乎无一例外，病毒感染的空间计算模型——与体外实验系统一样——将组织表示为宽而平，这通常被认为是宿主体内整个受影响组织的代表。然而，这种假设没有考虑到在病毒感染的情况下呼吸道的独特几何形状。呼吸道的特点是管状分支结构，而且在空间上是不均匀的：肺的较深区域由窄得多的气道组成，并且与更严重的感染有关。在这里，我们扩展了典型的多细胞病毒动力学模型，以解释呼吸道几何结构的两个基本特征：气道的管状结构和气道世代之间的分支过程。我们表明，与标准的计算和实验方法相比，这种更真实的组织几何结构大大改变了感染的动力学，并且由此产生的模型能够解决在扁平宿主组织中不会出现的重要生物现象，包括病毒谱系动力学，以及呼吸树不同区域对感染的免疫反应的异质性。我们的研究结果表明，目前体外系统可能不足以描述的病毒动力学方面，并指出呼吸道感染的几个特征，可以通过实验评估。

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引用次数: 0

Estimating social contact rates for the COVID-19 pandemic using Google mobility and pre-pandemic contact surveys 利用谷歌流动性和大流行前接触调查估计COVID-19大流行的社会接触率

IF 3 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-04-23 DOI: 10.1016/j.epidem.2025.100830

Em Prestige , Pietro Coletti , Jantien Backer , Nicholas G. Davies , W. John Edmunds , Christopher I. Jarvis

During the COVID-19 pandemic, aggregated mobility data was frequently used to estimate changing social contact rates. By taking pre-pandemic contact matrices, and transforming these using pandemic-era mobility data, infectious disease modellers attempted to predict the effect of large-scale behavioural changes on contact rates. This study explores the most accurate method for this transformation, using pandemic-era contact surveys as ground truth. We compared four methods for scaling synthetic contact matrices: two using fitted regression models and two using “naïve” mobility or mobility squared models. The regression models were fitted using the CoMix contact survey and Google mobility data from the UK over March 2020 – March 2021. The four models were then used to scale synthetic contact matrices—a representation of pre-pandemic behaviour—using mobility data from the UK, Belgium and the Netherlands to predict the number of contacts expected in “work” and “other” settings for a given mobility level. We then compared partial reproduction numbers estimated from the four models with those calculated directly from CoMix contact matrices across the three countries. The accuracy of each model was assessed using root mean squared error. The fitted regression models had substantially more accurate predictions than the naïve models, even when models were applied to out-of-sample data from the UK, Belgium and the Netherlands. Across all countries investigated, the linear fitted regression model was the most accurate and the naïve model using mobility alone was the least accurate. When attempting to estimate social contact rates during a pandemic without the resources available to conduct contact surveys, using a model fitted to data from another pandemic context is likely to be an improvement over using a “naïve” model based on mobility data alone. If a naïve model is to be used, mobility squared may be a better predictor of contact rates than mobility per se.

在2019冠状病毒病大流行期间，经常使用汇总的流动性数据来估计不断变化的社会接触率。传染病建模者采用大流行前的接触矩阵，并利用大流行时期的流动性数据对其进行转换，试图预测大规模行为变化对接触率的影响。本研究探索了这种转变的最准确方法，使用大流行时期的接触调查作为基础真相。我们比较了四种合成接触矩阵的缩放方法：两种使用拟合回归模型，两种使用“naïve”迁移率或迁移率平方模型。回归模型使用CoMix接触调查和英国2020年3月至2021年3月的谷歌流动性数据进行拟合。然后使用这四个模型来缩放合成接触矩阵（流行病前行为的表示），使用来自英国、比利时和荷兰的流动性数据来预测给定流动性水平下“工作”和“其他”环境中预期的接触人数。然后，我们比较了从四个模型估计的部分再生产数量与直接从CoMix接触矩阵计算的三个国家的部分再生产数量。每个模型的准确性用均方根误差来评估。拟合的回归模型比naïve模型有更准确的预测，即使模型应用于来自英国、比利时和荷兰的样本外数据。在所有被调查的国家中，线性拟合回归模型是最准确的，而单独使用流动性的naïve模型是最不准确的。在没有可用资源进行接触调查的情况下，试图估计大流行期间的社会接触率时，使用符合另一种大流行背景数据的模型可能比使用仅基于流动数据的“naïve”模型更好。如果使用naïve模型，迁移率的平方可能比迁移率本身更好地预测接触率。

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引用次数: 0

Detection of surges of SARS-Cov-2 using nonparametric Hawkes models 利用非参数霍克斯模型检测 SARS-Cov-2 的激增

IF 3 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-04-12 DOI: 10.1016/j.epidem.2025.100824

Sophie Phillips , George Mohler , Frederic Schoenberg

Hawkes point process models have been shown to forecast the number of daily new cases of epidemic diseases, including SARS-CoV-2 (Covid-19), with high accuracy. Here, we explore how accurately Hawkes models forecast surges of Covid-19 in the United States. We use Hawkes models to estimate the effective reproduction rate

R_{t}

and transmission density parameters for Covid-19 case counts in each of the 50 United States, then forecast

R_{t}

in future weeks with simple exponential smoothing. A classifier based on

R_{t} > x

is applied to predict upcoming surges in cases each week from August 2020 to December 2021, using only data available up to that week. At false alarm rates below 5%, the forecasts based on

R_{t}

are correct more often than forecasts based on smoothing the raw case count data, achieving a maximum accuracy of 90% with

R_{t} > 1.39

. The optimal decision boundary uses a combination of

R_{t}

and observed data.

霍克斯点过程模型已被证明能高精度地预测包括 SARS-CoV-2 （Covid-19）在内的流行性疾病的每日新增病例数。在此，我们探讨了霍克斯模型预测美国 Covid-19 突增病例的准确性。我们使用霍克斯模型估计了美国 50 个州中每个州的 Covid-19 病例数的有效繁殖率 Rt 和传播密度参数，然后用简单的指数平滑法预测了未来几周的 Rt。基于 Rt>x 的分类器仅使用截至 2020 年 8 月至 2021 年 12 月的数据预测每周即将出现的病例激增。在误报率低于 5%的情况下，基于 Rt 的预测比基于平滑原始病例数数据的预测更准确，Rt>1.39 的最高准确率达到 90%。最佳决策边界使用 Rt 和观测数据的组合。

{"title":"Detection of surges of SARS-Cov-2 using nonparametric Hawkes models","authors":"Sophie Phillips , George Mohler , Frederic Schoenberg","doi":"10.1016/j.epidem.2025.100824","DOIUrl":"10.1016/j.epidem.2025.100824","url":null,"abstract":"<div><div>Hawkes point process models have been shown to forecast the number of daily new cases of epidemic diseases, including SARS-CoV-2 (Covid-19), with high accuracy. Here, we explore how accurately Hawkes models forecast surges of Covid-19 in the United States. We use Hawkes models to estimate the effective reproduction rate <span><math><msub><mrow><mi>R</mi></mrow><mrow><mi>t</mi></mrow></msub></math></span> and transmission density parameters for Covid-19 case counts in each of the 50 United States, then forecast <span><math><msub><mrow><mi>R</mi></mrow><mrow><mi>t</mi></mrow></msub></math></span> in future weeks with simple exponential smoothing. A classifier based on <span><math><mrow><msub><mrow><mi>R</mi></mrow><mrow><mi>t</mi></mrow></msub><mo>></mo><mi>x</mi></mrow></math></span> is applied to predict upcoming surges in cases each week from August 2020 to December 2021, using only data available up to that week. At false alarm rates below 5%, the forecasts based on <span><math><msub><mrow><mi>R</mi></mrow><mrow><mi>t</mi></mrow></msub></math></span> are correct more often than forecasts based on smoothing the raw case count data, achieving a maximum accuracy of 90% with <span><math><mrow><msub><mrow><mi>R</mi></mrow><mrow><mi>t</mi></mrow></msub><mo>></mo><mn>1</mn><mo>.</mo><mn>39</mn></mrow></math></span>. The optimal decision boundary uses a combination of <span><math><msub><mrow><mi>R</mi></mrow><mrow><mi>t</mi></mrow></msub></math></span> and observed data.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"51 ","pages":"Article 100824"},"PeriodicalIF":3.0,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830099","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 effect of COVID-19 vaccination on change in contact and implications for transmission COVID-19疫苗接种对接触改变的影响及其对传播的影响

IF 3 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-04-09 DOI: 10.1016/j.epidem.2025.100827

Carol Y. Liu , Aaron Siegler , Patrick Sullivan , Samuel M. Jenness , Stefan Flasche , Benjamin Lopman , Kristin Nelson

Background

Monitoring human behavior as epidemic intelligence can critically complement traditional surveillance systems during epidemics. Retrospective analysis of novel behavioral data streams initiated during the COVID-19 pandemic help illustrate their utility. During the pandemic, behavior changed rapidly and was increasingly influenced by individual choice in response to changes such as newly available vaccines. Vaccines provided substantial protection against severe disease and deaths; however, their effect on behavior is understudied and it is unclear if vaccine effects against infection fully offset relaxation of social distancing behaviors.

Methods & results

We analyzed data from a longitudinal cohort sampled from U.S. households that measured contact rates, risk mitigation and COVID-19 vaccination status between August 2020-April 2022. Contact rates universally increased across survey rounds among all sociodemographic groups, but unvaccinated individuals had persistently higher contact rates. Using a multilevel generalized linear mixed effects model, we found that individuals who newly completed a primary vaccine series had an additional increase of 1.93 (95 % CI: 0.27–3.59) contacts compared to individuals who remained unvaccinated. Using observed contact rates to estimate transmission, we found that observed increases in contact rates were not fully offset by vaccine protection against infection, but transmission was still maintained below levels without distancing and vaccination despite clusters of individuals with high contact and no vaccination.

Conclusion

We estimated changes in contact rates following vaccination and inferred the joint effect of changes in vaccination and contacts on population-level transmission, finding that observed increases in contact rates were not fully offset by vaccine effects. Our work highlights the potential utility of ongoing longitudinal monitoring of contact patterns during epidemics.

作为流行病情报监测人类行为可以在流行病期间对传统监测系统进行重要补充。对COVID-19大流行期间发起的新行为数据流的回顾性分析有助于说明它们的实用性。在大流行期间，行为发生了迅速变化，并越来越多地受到个人选择的影响，以应对诸如新获得的疫苗等变化。疫苗为预防严重疾病和死亡提供了实质性保护；然而，它们对行为的影响尚未得到充分研究，目前尚不清楚疫苗对感染的影响是否完全抵消了放松社交距离行为的影响。方法,我们分析了从美国家庭抽样的纵向队列数据，这些数据测量了2020年8月至2022年4月期间的接触率、风险缓解和COVID-19疫苗接种状况。在所有社会人口群体的调查中，接触率普遍增加，但未接种疫苗的个体接触率持续较高。使用多层次广义线性混合效应模型，我们发现，与未接种疫苗的个体相比，新完成一次疫苗系列的个体接触者增加了1.93（95 % CI: 0.27-3.59）。使用观察到的接触率来估计传播，我们发现观察到的接触率的增加并没有被预防感染的疫苗保护完全抵消，但传播仍然保持在没有保持距离和接种疫苗的水平以下，尽管有高接触和未接种疫苗的个体聚集。结论我们估计了接种疫苗后接触率的变化，并推断了接种疫苗和接触的变化对人群水平传播的共同影响，发现观察到的接触率的增加并没有被疫苗效应完全抵消。我们的工作强调了在流行期间对接触方式进行持续纵向监测的潜在效用。

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引用次数: 0

Optimizing influenza vaccine allocation by age using cost-effectiveness analysis: A comparison of 6720 vaccination program scenarios in children and adults in Belgium 使用成本-效果分析优化按年龄分配流感疫苗：比利时6720例儿童和成人疫苗接种方案的比较

IF 3 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-04-05 DOI: 10.1016/j.epidem.2025.100826

Regina Manansala , Joke Bilcke , Lander Willem , Niel Hens , Philippe Beutels

Background

Many European countries prioritize groups for annual influenza vaccination based on risk of severe disease and death. This has resulted in relatively high influenza vaccination coverage in older adults in Belgium. However, coverage is much lower in younger adults and negligible in children. Children and young adults are known to play a major role in the transmission dynamics of influenza. Thus, an important policy question is how influenza vaccines can be optimally allocated across age groups, taking indirect effects into account.

Methods

We adapted a dynamic transmission model to reproduce influenza seasonality in Belgium comparing 6720 mutually exclusive vaccination options, including current practice. Vaccination options were defined by different combinations of coverage level changes in nine age groups. We performed an economic evaluation comparing all options from a healthcare payer perspective. Quality-adjusted life-years (QALYs) were the primary health outcome. We expressed parametric uncertainty using the Incremental Net Monetary Benefits (INMB) approach.

Results

Of all the vaccination options considered, over 90 % dominated the current Belgian vaccination strategy in terms of cost-effectiveness. Children were estimated to contribute a substantial indirect protective effect to the overall population. The most cost-effective program increases vaccination coverage rates for children to 90 %, 50–64 years old to 48 %, and 65–74 years old to 75 %.

Discussion

Overall QALY gains can be maximized in seasonal influenza vaccination programs at acceptable costs by achieving high vaccination coverage in childhood age groups. Programmatic and ethical concerns towards such an implementation in the Belgian context need to be separately considered.

背景：许多欧洲国家根据严重疾病和死亡风险确定每年流感疫苗接种的优先群体。这使得比利时老年人的流感疫苗接种覆盖率相对较高。然而，年轻人的覆盖率要低得多，儿童的覆盖率可以忽略不计。众所周知，儿童和年轻人在流感的传播动态中发挥着重要作用。因此，一个重要的政策问题是，在考虑到间接影响的情况下，如何在各年龄组之间最佳地分配流感疫苗。方法采用动态传播模型再现比利时的流感季节性，比较6720种相互排斥的疫苗接种方案，包括目前的做法。疫苗接种方案由9个年龄组的不同覆盖水平变化组合来定义。我们从医疗保健支付者的角度对所有选择进行了经济评估。质量调整生命年（QALYs）是主要健康结局。我们使用增量净货币效益（INMB）方法来表达参数不确定性。结果在考虑的所有疫苗接种方案中，超过90% %的疫苗接种策略在成本效益方面占主导地位。据估计，儿童对整个人口起到了相当大的间接保护作用。最具成本效益的规划将儿童的疫苗接种率提高到90% %，50-64岁提高到48% %，65-74岁提高到75% %。通过在儿童年龄组中实现高疫苗接种覆盖率，季节性流感疫苗接种规划可以可接受的成本最大限度地提高总体质量aly收益。在比利时的情况下，对这种执行的方案和伦理问题需要分别加以考虑。

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引用次数: 0

Machine learning approaches for real-time ZIP code and county-level estimation of state-wide infectious disease hospitalizations using local health system data 机器学习方法实时邮政编码和县级估计全州传染病住院使用当地卫生系统数据

IF 3 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-04-03 DOI: 10.1016/j.epidem.2025.100823

Tanvir Ahammed , Md Sakhawat Hossain , Christopher McMahan , Lior Rennert

The lack of conventional methods of estimating real-time infectious disease burden in granular regions inhibits timely and efficient public health response. Comprehensive data sources (e.g., state health department data) typically needed for such estimation are often limited due to 1) substantial delays in data reporting and 2) lack of geographic granularity in data provided to researchers. Leveraging real-time local health system data presents an opportunity to overcome these challenges. This study evaluates the effectiveness of machine learning and statistical approaches using local health system data to estimate current and previous COVID-19 hospitalizations in South Carolina. Random Forest models demonstrated consistently higher average median percent agreement accuracy compared to generalized linear mixed models for current weekly hospitalizations across 123 ZIP codes (72.29 %, IQR: 63.20–75.62 %) and 28 counties (76.43 %, IQR: 70.33–81.16 %) with sufficient health system coverage. To account for underrepresented populations in health systems, we combined Random Forest models with Classification and Regression Trees (CART) for imputation. The average median percent agreement was 61.02 % (IQR: 51.17–72.29 %) for all ZIP codes and 72.64 % (IQR: 66.13–77.69 %) for all counties. Median percent agreement for cumulative hospitalizations over the previous 6 months was 80.98 % (IQR: 68.99–89.66 %) for all ZIP codes and 81.17 % (IQR: 68.55–91.33 %) for all counties. These findings emphasize the effectiveness of utilizing real-time health system data to estimate infectious disease burden. Moreover, the methodologies developed in this study can be adapted to estimate hospitalizations for other diseases, offering a valuable tool for public health officials to respond swiftly and effectively to various health crises.

由于缺乏估算细粒度地区实时传染病负担的常规方法，无法及时有效地采取公共卫生应对措施。由于 1) 数据报告严重滞后，2) 提供给研究人员的数据缺乏地理粒度，此类估算通常所需的综合数据源（如州卫生部门数据）往往受到限制。利用当地卫生系统的实时数据为克服这些挑战提供了机会。本研究评估了机器学习和统计方法的有效性，这些方法使用当地卫生系统数据来估算南卡罗来纳州当前和以往的 COVID-19 住院情况。在 123 个邮政编码（72.29%，IQR：63.20-75.62%）和 28 个有足够医疗系统覆盖范围的县（76.43%，IQR：70.33-81.16%）中，随机森林模型与广义线性混合模型相比，在当前每周住院情况方面显示出更高的平均中位数百分比一致性准确率。为了考虑到医疗系统中代表性不足的人群，我们将随机森林模型与分类和回归树 (CART) 结合起来进行估算。所有邮政编码和所有县的平均一致率中位数分别为 61.02 %（IQR：51.17-72.29 %）和 72.64 %（IQR：66.13-77.69 %）。在所有邮政编码中，前 6 个月累计住院治疗的中位同意率为 80.98 %（IQR：68.99-89.66 %），在所有县中为 81.17 %（IQR：68.55-91.33 %）。这些发现强调了利用实时卫生系统数据估算传染病负担的有效性。此外，本研究开发的方法还可用于估算其他疾病的住院人数，为公共卫生官员迅速有效地应对各种卫生危机提供了宝贵的工具。

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引用次数: 0

Analysis insights to support the use of wastewater and environmental surveillance data for infectious diseases and pandemic preparedness 分析见解，支持将废水和环境监测数据用于传染病和大流行防范

IF 3 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-03-28 DOI: 10.1016/j.epidem.2025.100825

KM O’Reilly , MJ Wade , K. Farkas , F. Amman , A. Lison , JD Munday , J. Bingham , ZE Mthombothi , Z. Fang , CS Brown , RR Kao , L. Danon

Wastewater-based epidemiology is the detection of pathogens from sewage systems and the interpretation of these data to improve public health. Its use has increased in scope since 2020, when it was demonstrated that SARS-CoV-2 RNA could be successfully extracted from the wastewater of affected populations. In this Perspective we provide an overview of recent advances in pathogen detection within wastewater, propose a framework for identifying the utility of wastewater sampling for pathogen detection and suggest areas where analytics require development. Ensuring that both data collection and analysis are tailored towards key questions at different stages of an epidemic will improve the inference made. For analyses to be useful we require methods to determine the absence of infection, early detection of infection, reliably estimate epidemic trajectories and prevalence, and detect novel variants without reliance on consensus sequences. This research area has included many innovations that have improved the interpretation of collected data and we are optimistic that innovation will continue in the future.

基于废水的流行病学是从污水系统中检测病原体并对这些数据进行解释以改善公共卫生。自2020年以来，其使用范围有所扩大，当时证明可以从受影响人群的废水中成功提取SARS-CoV-2 RNA。在这一观点中，我们概述了废水中病原体检测的最新进展，提出了一个确定废水采样用于病原体检测的框架，并提出了分析需要发展的领域。确保数据收集和分析都针对流行病不同阶段的关键问题进行调整，将改进所作的推断。为了使分析有用，我们需要确定是否存在感染、早期发现感染、可靠地估计流行轨迹和流行程度，以及在不依赖于共识序列的情况下发现新的变异的方法。这一研究领域包含了许多改进了对收集数据的解释的创新，我们乐观地认为创新将在未来继续。

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引用次数: 0

Does spatial information improve forecasting of influenza-like illness? 空间信息能改善流感样疾病的预测吗？

IF 3 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-03-18 DOI: 10.1016/j.epidem.2025.100820

Gabrielle Thivierge , Aaron Rumack , F. William Townes

Seasonal influenza forecasting is critical for public health and individual decision making. We investigate whether the inclusion of data about influenza activity in neighboring states can improve point predictions and distribution forecasting of influenza-like illness (ILI) in each US state using statistical regression models. Using CDC FluView ILI data from 2010–2019, we forecast weekly ILI in each US state with quantile, linear, and Poisson autoregressive models fit using different combinations of ILI data from the target state, neighboring states, and the US population-weighted average. Scoring with root mean squared error and weighted interval score indicated that the covariate sets including neighbors and/or the US weighted average ILI showed slightly higher accuracy than models fit only using lagged ILI in the target state, on average. Additionally, the improvement in performance when including neighbors was similar to the improvement when including the US average instead, suggesting the proximity of the neighboring states is not the driver of the slight increase in accuracy. There is also clear within-season and between-season variability in the effect of spatial information on prediction accuracy.

季节性流感预测对公共卫生和个人决策至关重要。我们使用统计回归模型研究是否将邻近州流感活动数据纳入可以改善美国每个州流感样疾病（ILI）的点预测和分布预测。利用CDC FluView 2010-2019年的ILI数据，我们使用分位数、线性和泊松自回归模型对美国每个州的每周ILI进行预测，这些模型使用来自目标州、邻近州和美国人口加权平均值的ILI数据的不同组合进行拟合。用均方根误差和加权区间得分进行评分表明，包含邻居和/或美国加权平均ILI的协变量集平均比仅使用滞后ILI拟合的模型在目标状态下的准确率略高。此外，包括邻居时的性能改进与包括美国平均水平时的改进相似，这表明邻近州的接近程度并不是准确性略有提高的驱动因素。空间信息对预测精度的影响也存在明显的季节内和季节间变异性。

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