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Ensemble2: Scenarios ensembling for communication and performance analysis Ensemble2:用于通信和性能分析的情景组合
IF 3.8 3区 医学 Q2 INFECTIOUS DISEASES Pub Date : 2024-02-08 DOI: 10.1016/j.epidem.2024.100748
Clara Bay , Guillaume St-Onge , Jessica T. Davis , Matteo Chinazzi , Emily Howerton , Justin Lessler , Michael C. Runge , Katriona Shea , Shaun Truelove , Cecile Viboud , Alessandro Vespignani

Throughout the COVID-19 pandemic, scenario modeling played a crucial role in shaping the decision-making process of public health policies. Unlike forecasts, scenario projections rely on specific assumptions about the future that consider different plausible states-of-the-world that may or may not be realized and that depend on policy interventions, unpredictable changes in the epidemic outlook, etc. As a consequence, long-term scenario projections require different evaluation criteria than the ones used for traditional short-term epidemic forecasts. Here, we propose a novel ensemble procedure for assessing pandemic scenario projections using the results of the Scenario Modeling Hub (SMH) for COVID-19 in the United States (US). By defining a “scenario ensemble” for each model and the ensemble of models, termed “Ensemble2”, we provide a synthesis of potential epidemic outcomes, which we use to assess projections’ performance, bypassing the identification of the most plausible scenario. We find that overall the Ensemble2 models are well-calibrated and provide better performance than the scenario ensemble of individual models. The ensemble procedure accounts for the full range of plausible outcomes and highlights the importance of scenario design and effective communication. The scenario ensembling approach can be extended to any scenario design strategy, with potential refinements including weighting scenarios and allowing the ensembling process to evolve over time.

在 COVID-19 大流行期间,情景建模在公共卫生政策决策过程中发挥了至关重要的作用。与预测不同,情景预测依赖于对未来的具体假设,这些假设考虑了可能实现也可能不实现的不同可信的世界状态,并取决于政策干预、流行病前景的不可预测变化等。因此,长期情景预测需要与传统短期流行病预测不同的评估标准。在此,我们利用美国 COVID-19 的情景模拟中心(SMH)的结果,提出了一种评估大流行情景预测的新型集合程序。通过为每个模型和称为 "Ensemble2 "的模型集合定义一个 "情景集合",我们提供了一个潜在流行病结果的综合体,用来评估预测的性能,绕过了确定最合理情景的过程。我们发现,总体而言,"Ensemble2 "模型校准良好,其性能优于单个模型的情景集合。集合程序考虑了所有可能的结果,突出了情景设计和有效沟通的重要性。情景集合方法可扩展到任何情景设计策略,可能的改进包括对情景加权和允许集合过程随时间演变。
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引用次数: 0
Quantifying the impact of interventions against Plasmodium vivax: A model for country-specific use 量化对间日疟原虫干预措施的影响:针对具体国家的使用模式
IF 3.8 3区 医学 Q2 INFECTIOUS DISEASES Pub Date : 2024-02-05 DOI: 10.1016/j.epidem.2024.100747
C. Champagne , M. Gerhards , J.T. Lana , A. Le Menach , E. Pothin

In order to evaluate the impact of various intervention strategies on Plasmodium vivax dynamics in low endemicity settings without significant seasonal pattern, we introduce a simple mathematical model that can be easily adapted to reported case numbers similar to that collected by surveillance systems in various countries. The model includes case management, vector control, mass drug administration and reactive case detection interventions and is implemented in both deterministic and stochastic frameworks. It is available as an R package to enable users to calibrate and simulate it with their own data. Although we only illustrate its use on fictitious data, by simulating and comparing the impact of various intervention combinations on malaria risk and burden, this model could be a useful tool for strategic planning, implementation and resource mobilization.

为了评估各种干预策略对低流行率环境中无显着季节性模式的间日疟原虫动态的影响,我们引入了一个简单的数学模型,该模型可轻松适用于与各国监测系统收集的病例数类似的报告病例数。该模型包括病例管理、病媒控制、大规模用药和反应性病例检测等干预措施,可在确定性和随机性框架内实施。该模型以 R 软件包的形式提供,用户可以使用自己的数据对其进行校准和模拟。虽然我们仅在虚构数据上说明了其用途,但通过模拟和比较各种干预组合对疟疾风险和负担的影响,该模型可以成为战略规划、实施和资源调动的有用工具。
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引用次数: 0
Estimating the population effectiveness of interventions against COVID-19 in France: A modelling study 估算法国 COVID-19 干预措施的人口效果:一项模型研究
IF 3.8 3区 医学 Q2 INFECTIOUS DISEASES Pub Date : 2024-02-02 DOI: 10.1016/j.epidem.2024.100744
Iris Ganser , David L. Buckeridge , Jane Heffernan , Mélanie Prague , Rodolphe Thiébaut

Background

Non-pharmaceutical interventions (NPIs) and vaccines have been widely used to manage the COVID-19 pandemic. However, uncertainty persists regarding the effectiveness of these interventions due to data quality issues, methodological challenges, and differing contextual factors. Accurate estimation of their effects is crucial for future epidemic preparedness.

Methods

To address this, we developed a population-based mechanistic model that includes the impact of NPIs and vaccines on SARS-CoV-2 transmission and hospitalization rates. Our statistical approach estimated all parameters in one step, accurately propagating uncertainty. We fitted the model to comprehensive epidemiological data in France from March 2020 to October 2021. With the same model, we simulated scenarios of vaccine rollout.

Results

The first lockdown was the most effective, reducing transmission by 84 % (95 % confidence interval (CI) 83–85). Subsequent lockdowns had diminished effectiveness (reduction of 74 % (69–77) and 11 % (9–18), respectively). A 6 pm curfew was more effective than one at 8 pm (68 % (66–69) vs. 48 % (45–49) reduction), while school closures reduced transmission by 15 % (12–18). In a scenario without vaccines before November 2021, we predicted 159,000 or 168 % (95 % prediction interval (PI) 70-315) more deaths and 1,488,000 or 300 % (133-492) more hospitalizations. If a vaccine had been available after 100 days, over 71,000 deaths (16,507–204,249) and 384,000 (88,579–1,020,386) hospitalizations could have been averted.

Conclusion

Our results highlight the substantial impact of NPIs, including lockdowns and curfews, in controlling the COVID-19 pandemic. We also demonstrate the value of the 100 days objective of the Coalition for Epidemic Preparedness Innovations (CEPI) initiative for vaccine availability.

背景非药物干预措施(NPI)和疫苗已被广泛用于控制 COVID-19 大流行。然而,由于数据质量问题、方法上的挑战以及不同的背景因素,这些干预措施的效果仍存在不确定性。为了解决这个问题,我们建立了一个基于人群的机理模型,其中包括非传染性肺炎疫苗和疫苗对 SARS-CoV-2 传播和住院率的影响。我们的统计方法一步估算了所有参数,准确地传播了不确定性。我们将该模型与法国 2020 年 3 月至 2021 年 10 月的综合流行病学数据进行了拟合。结果第一次封锁最为有效,传播率降低了 84%(95% 置信区间 (CI) 83-85)。随后的封锁效果有所减弱(分别减少了 74% (69-77) 和 11% (9-18))。下午 6 点的宵禁比晚上 8 点的宵禁更有效(分别减少 68% (66-69) 和 48% (45-49)),而学校关闭则减少了 15% (12-18)的传播。在 2021 年 11 月之前没有疫苗的情况下,我们预测死亡人数将增加 15.9 万或 194%(95% 预测区间 (PI) 74-424),住院人数将增加 148.8 万或 340%(136-689)。如果在 100 天后可以获得疫苗,则可以避免 71,000 多例死亡(16,507-204,249 例)和 384,000 例住院(88,579-1,020,386 例)。我们还证明了流行病防备创新联盟 (CEPI) 倡议的 100 天目标在疫苗供应方面的价值。
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引用次数: 0
Reproducibility of COVID-era infectious disease models COVID 时代传染病模型的可重复性
IF 3.8 3区 医学 Q2 INFECTIOUS DISEASES Pub Date : 2024-01-23 DOI: 10.1016/j.epidem.2024.100743
Alec S. Henderson , Roslyn I. Hickson , Morgan Furlong , Emma S. McBryde , Michael T. Meehan

Infectious disease modelling has been prominent throughout the COVID-19 pandemic, helping to understand the virus’ transmission dynamics and inform response policies. Given their potential importance and translational impact, we evaluated the computational reproducibility of infectious disease modelling articles from the COVID era. We found that four out of 100 randomly sampled studies released between January 2020 and August 2022 could be completely computationally reproduced using the resources provided (e.g., code, data, instructions) whilst a further eight were partially reproducible. For the 100 most highly cited articles from the same period we found that 11 were completely reproducible with a further 22 partially reproducible. Reflecting on our experience, we discuss common issues affecting computational reproducibility and how these might be addressed.

在 COVID-19 大流行期间,传染病建模一直很受关注,它有助于了解病毒的传播动态并为应对政策提供依据。鉴于其潜在的重要性和转化影响,我们评估了 COVID 时代传染病模型文章的计算可重复性。我们发现,在 2020 年 1 月至 2022 年 8 月间发布的 100 篇随机抽样研究中,有 4 篇可以利用所提供的资源(如代码、数据、说明)进行完全计算重现,另有 8 篇可部分重现。在同期引用率最高的 100 篇文章中,我们发现有 11 篇可完全重现,另有 22 篇可部分重现。根据我们的经验,我们讨论了影响计算可重复性的常见问题以及如何解决这些问题。
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引用次数: 0
Differences between the true reproduction number and the apparent reproduction number of an epidemic time series 流行病时间序列的真实繁殖数与表观繁殖数之间的差异
IF 3.8 3区 医学 Q2 INFECTIOUS DISEASES Pub Date : 2024-01-13 DOI: 10.1016/j.epidem.2024.100742
Oliver Eales , Steven Riley

The time-varying reproduction number R(t) measures the number of new infections per infectious individual and is closely correlated with the time series of infection incidence by definition. The timings of actual infections are rarely known, and analysis of epidemics usually relies on time series data for other outcomes such as symptom onset. A common implicit assumption, when estimating R(t) from an epidemic time series, is that R(t) has the same relationship with these downstream outcomes as it does with the time series of incidence. However, this assumption is unlikely to be valid given that most epidemic time series are not perfect proxies of incidence. Rather they represent convolutions of incidence with uncertain delay distributions. Here we define the apparent time-varying reproduction number, RA(t), the reproduction number calculated from a downstream epidemic time series and demonstrate how differences between RA(t) and R(t) depend on the convolution function. The mean of the convolution function sets a time offset between the two signals, whilst the variance of the convolution function introduces a relative distortion between them. We present the convolution functions of epidemic time series that were available during the SARS-CoV-2 pandemic. Infection prevalence, measured by random sampling studies, presents fewer biases than other epidemic time series. Here we show that additionally the mean and variance of its convolution function were similar to that obtained from traditional surveillance based on mass-testing and could be reduced using more frequent testing, or by using stricter thresholds for positivity. Infection prevalence studies continue to be a versatile tool for tracking the temporal trends of R(t), and with additional refinements to their study protocol, will be of even greater utility during any future epidemics or pandemics.

时变繁殖数 R(t) 衡量每个感染个体新感染的数量,根据定义,它与感染发病率的时间序列密切相关。实际感染的时间很少为人所知,对流行病的分析通常依赖于其他结果(如症状出现)的时间序列数据。在根据流行病时间序列估计 R(t) 时,一个常见的隐含假设是 R(t) 与这些下游结果的关系与它与发病率时间序列的关系相同。然而,鉴于大多数流行病时间序列并不是发病率的完美替代物,这一假设不太可能成立。相反,它们代表了发病率与不确定延迟分布的卷积。在此,我们定义了表观时变繁殖数 RA(t),即从下游流行病时间序列计算出的繁殖数,并展示了 RA(t) 和 R(t) 之间的差异如何取决于卷积函数。卷积函数的均值设定了两个信号之间的时间偏移,而卷积函数的方差则在两个信号之间引入了相对失真。我们介绍了 SARS-CoV-2 大流行期间流行病时间序列的卷积函数。与其他流行病时间序列相比,通过随机抽样研究测量的感染率偏差较小。我们在此表明,此外,其卷积函数的均值和方差与基于大规模检测的传统监测所获得的均值和方差相似,可以通过增加检测频率或使用更严格的阳性阈值来减少偏差。感染率研究仍然是跟踪 R(t) 时间趋势的多功能工具,随着研究方案的进一步完善,它在未来的流行病或大流行中将发挥更大的作用。
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引用次数: 0
The impact of inaccurate assumptions about antibody test accuracy on the parametrisation and results of infectious disease models of epidemics 关于抗体检测准确性的不准确假设对传染病流行模型的参数化和结果的影响
IF 3.8 3区 医学 Q2 INFECTIOUS DISEASES Pub Date : 2024-01-09 DOI: 10.1016/j.epidem.2024.100741
Madhav Chaturvedi , Denise Köster , Nicole Rübsamen , Veronika K Jaeger , Antonia Zapf , André Karch

The parametrisation of infectious disease models is often done based on epidemiological studies that use diagnostic and serology tests to establish disease prevalence or seroprevalence in the population being modelled. During outbreaks of an emerging infectious disease, tests are often used, both for disease control and epidemiological studies, before studies evaluating their accuracy in the population have concluded, with assumptions made about accuracy parameters like sensitivity and specificity. In this simulation study, we simulated such an outbreak, based on the case study of COVID-19, and found that inaccurate parametrisation of infectious disease models due to assumptions about antibody test accuracy in a seroprevalence study can cause modelling results that inform public health decisions to be inaccurate; for example, in our simulation setup, assuming that antibody test specificity was 0.99 instead of 0.90 when it was in fact 0.90 led to an average relative difference of 0.78 in model-projected peak hospitalisations, even when test sensitivity and all other parameters were accurately characterised. We therefore suggest that methods to speed up test evaluation studies are vitally important in the public health response to an emerging outbreak.

传染病模型的参数化通常是在流行病学研究的基础上进行的,流行病学研究使用诊断 和血清学检验来确定被模拟人群中的疾病流行率或血清流行率。在新发传染病爆发期间,在对其在人群中的准确性进行评估的研究得出结论之前,通常会使用检测方法来进行疾病控制和流行病学研究,并对敏感性和特异性等准确性参数进行假设。在这项模拟研究中,我们以 COVID-19 为案例,模拟了这样一次疫情爆发,并发现由于在血清流行研究中对抗体检测准确性的假设而导致的传染病模型参数设置不准确,会导致为公共卫生决策提供依据的模型结果不准确;例如,在我们的模拟设置中,假设抗体检测特异性为 0.例如,在我们的模拟设置中,假设抗体检测特异性为 0.99 而非 0.90,而实际上是 0.90,导致模型预测的住院高峰平均相对差异为 0.78,即使检测灵敏度和所有其他参数都准确描述。因此,我们建议,加快测试评估研究的方法对于公共卫生应对新出现的疫情至关重要。
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引用次数: 0
Inferring transmission routes for foot-and-mouth disease virus within a cattle herd using approximate Bayesian computation 利用近似贝叶斯计算推断口蹄疫病毒在牛群中的传播路线
IF 3.8 3区 医学 Q2 INFECTIOUS DISEASES Pub Date : 2024-01-08 DOI: 10.1016/j.epidem.2024.100740
John Ellis , Emma Brown, Claire Colenutt, David Schley , Simon Gubbins

To control an outbreak of an infectious disease it is essential to understand the different routes of transmission and how they contribute to the overall spread of the pathogen. With this information, policy makers can choose the most efficient methods of detection and control during an outbreak. Here we assess the contributions of direct contact and environmental contamination to the transmission of foot-and-mouth disease virus (FMDV) in a cattle herd using an individual-based model that includes both routes. Model parameters are inferred using approximate Bayesian computation with sequential Monte Carlo sampling (ABC-SMC) applied to data from transmission experiments and the 2007 epidemic in Great Britain. This demonstrates that the parameters derived from transmission experiments are applicable to outbreaks in the field, at least for closely related strains. Under the assumptions made in the model we show that environmental transmission likely contributes a majority of infections within a herd during an outbreak, although there is a lot of variation between simulated outbreaks. The accumulation of environmental contamination not only causes infections within a farm, but also has the potential to spread between farms via fomites. We also demonstrate the importance and effectiveness of rapid detection of infected farms in reducing transmission between farms, whether via direct contact or the environment.

要控制传染病的爆发,就必须了解不同的传播途径,以及它们是如何促成病原体的整体传播的。有了这些信息,决策者就能在疫情爆发时选择最有效的检测和控制方法。在此,我们使用一个基于个体的模型,评估了直接接触和环境污染对口蹄疫病毒(FMDV)在牛群中传播的贡献。模型参数采用近似贝叶斯计算和序列蒙特卡罗采样(ABC-SMC)推断,并将其应用于来自传播实验和 2007 年英国疫情的数据。这表明,从传播实验中得出的参数适用于实地疫情,至少对于密切相关的菌株是如此。根据模型中的假设,我们发现在疫情爆发期间,环境传播很可能会造成牛群中的大部分感染,尽管不同的模拟疫情之间存在很大差异。环境污染的积累不仅会造成猪场内部的感染,还有可能通过寄生虫在猪场之间传播。我们还证明了快速检测受感染猪场对减少猪场间传播(无论是通过直接接触还是环境传播)的重要性和有效性。
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引用次数: 0
Epidemiological impact of a large number of false negative SARS-CoV-2 test results in South West England during September and October 2021 2021 年 9 月和 10 月期间英格兰西南部出现大量假阴性 SARS-CoV-2 检测结果对流行病学的影响
IF 3.8 3区 医学 Q2 INFECTIOUS DISEASES Pub Date : 2024-01-06 DOI: 10.1016/j.epidem.2023.100739
L. Hounsome , D. Herr , R. Bryant , R. Smith , L. Loman , J. Harris , U. Youhan , E. Dzene , P. Hadjipantelis , H. Long , T. Laurence , S. Riley , F. Cumming

During September and October 2021, a substantial number of Polymerase Chain Reaction (PCR) tests in England processed at a single laboratory were incorrectly reported as negative. We estimate the number of false negative test results issued and investigate the epidemiological impact of this incident. We estimate the number of COVID-19 cases that would have been reported had the sensitivity of the laboratory test procedure not dropped for the period 2 September to 12 October. In addition, by making comparisons between the most affected local areas and comparator populations, we estimate the number of additional infections, cases, hospitalisations and deaths that could have occurred as a result of increased transmission due to false negative test results.We estimate that around 39,000 tests may have been false negatives during this period and, as a direct result of this incident, the most affected areas in the South-West of England could have experienced between 6000 and 34,000 additional reportable cases, with a central estimate of around 24,000 additional reportable cases. Using modelled relationships between key variables, we estimate that this central estimate could have translated to approximately 55,000 additional infections.Each false negative likely led to around 1.5 additional infections. The incident is likely to have had a measurable impact on cases and infections in the affected areas in the South-West of England.

Impact statement

These results indicate the significant negative impact of incorrect testing on COVID outcomes; and make a substantial contribution to understanding the impact of testing systems and the need to ensure high accuracy in testing and reporting of results.

2021 年 9 月和 10 月期间,英格兰一家实验室处理的大量聚合酶链反应 (PCR) 检测结果被错误地报告为阴性。我们估计了 9 月 2 日至 10 月 12 日期间,如果实验室检测程序的灵敏度没有下降,本应报告的 COVID-19 病例数。此外,通过在受影响最严重的地区和参照人群之间进行比较,我们估算了由于假阴性检测结果导致传播增加而可能造成的额外感染、病例、住院和死亡人数。我们估计,在此期间可能有约 39,000 例检测结果为假阴性,由于此次事件的直接影响,英格兰西南部受影响最严重的地区可能会增加 6,000 到 34,000 例可报告病例,中心估计值为增加约 24,000 例可报告病例。通过模拟关键变量之间的关系,我们估计这一中心估计值可能会导致约 55,000 例额外感染。该事件很可能对英格兰西南部受影响地区的病例和感染产生了可衡量的影响。影响声明这些结果表明,错误检测对 COVID 结果产生了重大负面影响;对了解检测系统的影响以及确保检测和结果报告高度准确性的必要性做出了重大贡献。
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引用次数: 0
The US COVID-19 and Influenza Scenario Modeling Hubs: Delivering long-term projections to guide policy 美国 COVID-19 和流感情景建模中心:为指导政策提供长期预测
IF 3.8 3区 医学 Q2 INFECTIOUS DISEASES Pub Date : 2023-12-29 DOI: 10.1016/j.epidem.2023.100738
Sara L. Loo , Emily Howerton , Lucie Contamin , Claire P. Smith , Rebecca K. Borchering , Luke C. Mullany , Samantha Bents , Erica Carcelen , Sung-mok Jung , Tiffany Bogich , Willem G. van Panhuis , Jessica Kerr , Jessi Espino , Katie Yan , Harry Hochheiser , Michael C. Runge , Katriona Shea , Justin Lessler , Cécile Viboud , Shaun Truelove

Between December 2020 and April 2023, the COVID-19 Scenario Modeling Hub (SMH) generated operational multi-month projections of COVID-19 burden in the US to guide pandemic planning and decision-making in the context of high uncertainty. This effort was born out of an attempt to coordinate, synthesize and effectively use the unprecedented amount of predictive modeling that emerged throughout the COVID-19 pandemic. Here we describe the history of this massive collective research effort, the process of convening and maintaining an open modeling hub active over multiple years, and attempt to provide a blueprint for future efforts. We detail the process of generating 17 rounds of scenarios and projections at different stages of the COVID-19 pandemic, and disseminating results to the public health community and lay public. We also highlight how SMH was expanded to generate influenza projections during the 2022–23 season. We identify key impacts of SMH results on public health and draw lessons to improve future collaborative modeling efforts, research on scenario projections, and the interface between models and policy.

2020 年 12 月至 2023 年 4 月期间,COVID-19 情景建模中心(SMH)对美国的 COVID-19 负担进行了多月预测,以在高度不确定的情况下指导大流行规划和决策。这项工作是为了协调、综合和有效利用在 COVID-19 大流行期间出现的前所未有的大量预测建模工作。在此,我们描述了这一大规模集体研究工作的历史、召集和维护一个活跃多年的开放式建模中心的过程,并试图为未来的工作提供一个蓝图。我们详细介绍了在 COVID-19 大流行的不同阶段生成 17 轮情景和预测的过程,以及向公共卫生界和普通公众传播结果的过程。我们还重点介绍了如何将SMH扩展到2022-23流感季节的流感预测。我们确定了 SMH 结果对公共卫生的主要影响,并总结了经验教训,以改进未来的合作建模工作、情景预测研究以及模型与政策之间的衔接。
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引用次数: 0
Predicting the impact of COVID-19 non-pharmaceutical intervention on short- and medium-term dynamics of enterovirus D68 in the US 预测 COVID-19 非药物干预措施对美国 D68 型肠道病毒中短期动态的影响
IF 3.8 3区 医学 Q2 INFECTIOUS DISEASES Pub Date : 2023-12-18 DOI: 10.1016/j.epidem.2023.100736
Sang Woo Park , Kevin Messacar , Daniel C. Douek , Alicen B. Spaulding , C. Jessica E. Metcalf , Bryan T. Grenfell

Recent outbreaks of enterovirus D68 (EV-D68) infections, and their causal linkage with acute flaccid myelitis (AFM), continue to pose a serious public health concern. During 2020 and 2021, the dynamics of EV-D68 and other pathogens have been significantly perturbed by non-pharmaceutical interventions against COVID-19; this perturbation presents a powerful natural experiment for exploring the dynamics of these endemic infections. In this study, we analyzed publicly available data on EV-D68 infections, originally collected through the New Vaccine Surveillance Network, to predict their short- and long-term dynamics following the COVID-19 interventions. Although long-term predictions are sensitive to our assumptions about underlying dynamics and changes in contact rates during the NPI periods, the likelihood of a large outbreak in 2023 appears to be low. Comprehensive surveillance data are needed to accurately characterize future dynamics of EV-D68. The limited incidence of AFM cases in 2022, despite large EV-D68 outbreaks, poses further questions for the timing of the next AFM outbreaks.

最近爆发的肠道病毒 D68(EV-D68)感染及其与急性弛缓性脊髓炎(AFM)的因果关系继续构成严重的公共卫生问题。在 2020 年和 2021 年期间,EV-D68 和其他病原体的动态受到了针对 COVID-19 的非药物干预措施的显著干扰;这种干扰为探索这些地方性感染的动态提供了一个强大的自然实验。在本研究中,我们分析了最初通过新疫苗监测网络收集到的有关 EV-D68 感染的公开数据,以预测 COVID-19 干预后的短期和长期动态。虽然长期预测对我们关于新疫苗监测网期间潜在动态和接触率变化的假设很敏感,但 2023 年爆发大规模疫情的可能性似乎很低。要准确描述 EV-D68 的未来动态,需要全面的监测数据。尽管爆发了大规模的EV-D68疫情,但2022年的AFM病例发生率有限,这对下一次AFM疫情爆发的时间提出了进一步的问题。
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引用次数: 0
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Epidemics
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