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Leveraging advances in data-driven deep learning methods for hybrid epidemic modeling 利用数据驱动的深度学习方法的进步进行混合流行病建模
IF 3 3区 医学 Q2 INFECTIOUS DISEASES Pub Date : 2024-06-24 DOI: 10.1016/j.epidem.2024.100782
Shi Chen , Daniel Janies , Rajib Paul , Jean-Claude Thill

Mathematical modeling of epidemic dynamics is crucial to understand its underlying mechanisms, quantify important parameters, and make predictions that facilitate more informed decision-making. There are three major types of models: mechanistic models including the SEIR-type paradigm, alternative data-driven (DD) approaches, and hybrid models that combine mechanistic models with DD approaches. In this paper, we summarize our work in the COVID-19 Scenario Modeling Hub (SMH) for more than 12 rounds since early 2021 for informed decision support. We emphasize the importance of deep learning techniques for epidemic modeling via a flexible DD framework that substantially complements the mechanistic paradigm to evaluate various future epidemic scenarios. We start with a traditional curve-fitting approach to model cumulative COVID-19 based on the underlying SEIR-type mechanisms. Hospitalizations and deaths are modeled as binomial processes of cases and hospitalization, respectively. We further formulate two types of deep learning models based on multivariate long short term memory (LSTM) to address the challenges of more traditional DD models. The first LSTM is structurally similar to the curve fitting approach and assumes that hospitalizations and deaths are binomial processes of cases. Instead of using a predefined exponential curve, LSTM relies on the underlying data to identify the most appropriate functions, and is capable of capturing both long-term and short-term epidemic behaviors. We then relax the assumption of dependent inputs among cases, hospitalizations, and death. Another type of LSTM that handles all input time series as parallel signals, the independent multivariate LSTM, is developed. Independent multivariate LSTM can incorporate a wide range of data sources beyond traditional case-based epidemiological surveillance. The DD framework unleashes its potential in big data era with previously neglected heterogeneous surveillance data sources, such as syndromic, environment, genomic, serologic, infoveillance, and mobility data. DD approaches, especially LSTM, complement and integrate with the mechanistic modeling paradigm, provide a feasible alternative approach to model today’s complex socio-epidemiological systems, and further leverage our ability to explore different scenarios for more informed decision-making during health emergencies.

疫情动态的数学模型对于了解其基本机制、量化重要参数以及做出有助于做出更明智决策的预测至关重要。目前主要有三种模型:包括 SEIR 型范例在内的机理模型、替代性数据驱动(DD)方法以及将机理模型与 DD 方法相结合的混合模型。在本文中,我们总结了自 2021 年初以来,我们在 COVID-19 场景建模中心(SMH)为知情决策支持所做的超过 12 轮的工作。我们强调了深度学习技术在流行病建模中的重要性,即通过灵活的 DD 框架,对机理范式进行实质性补充,以评估各种未来流行病情景。我们首先采用传统的曲线拟合方法,根据 SEIR 类型的基本机制对累积 COVID-19 进行建模。住院和死亡分别被模拟为病例和住院的二项过程。我们进一步制定了两种基于多变量长短期记忆(LSTM)的深度学习模型,以应对更多传统 DD 模型所面临的挑战。第一种 LSTM 在结构上类似于曲线拟合方法,假定住院和死亡是病例的二项过程。LSTM 不使用预定义的指数曲线,而是依靠基础数据来确定最合适的函数,并且能够捕捉长期和短期的流行病行为。然后,我们放宽了病例、住院和死亡之间依赖输入的假设。我们还开发了另一种将所有输入时间序列作为并行信号处理的 LSTM,即独立多变量 LSTM。独立多变量 LSTM 可纳入传统病例流行病监测以外的各种数据源。在大数据时代,DD 框架可以利用以前被忽视的异构监测数据源(如综合征、环境、基因组、血清学、信息监测和流动性数据)释放其潜力。DD 方法,尤其是 LSTM,补充并整合了机理建模范式,为当今复杂的社会流行病学系统建模提供了一种可行的替代方法,并进一步提高了我们探索不同情景的能力,从而在卫生紧急情况下做出更明智的决策。
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引用次数: 0
Corrigendum to “The impact of inaccurate assumptions about antibody test accuracy on the parametrisation and results of infectious disease models of epidemics” [Epidemics 46 (2024) 100741] 对 "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"[Epidemics 46 (2024) 100741]的更正。
IF 3.8 3区 医学 Q1 Medicine Pub Date : 2024-06-01 DOI: 10.1016/j.epidem.2024.100766
Madhav Chaturvedi , Denise Köster , Nicole Rübsamen , Veronika K. Jaeger , Antonia Zapf , André Karch
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引用次数: 0
Scenario design for infectious disease projections: Integrating concepts from decision analysis and experimental design 传染病预测的情景设计:整合决策分析和实验设计的概念
IF 3.8 3区 医学 Q1 Medicine Pub Date : 2024-06-01 DOI: 10.1016/j.epidem.2024.100775
Michael C. Runge , Katriona Shea , Emily Howerton , Katie Yan , Harry Hochheiser , Erik Rosenstrom , William J.M. Probert , Rebecca Borchering , Madhav V. Marathe , Bryan Lewis , Srinivasan Venkatramanan , Shaun Truelove , Justin Lessler , Cécile Viboud

Across many fields, scenario modeling has become an important tool for exploring long-term projections and how they might depend on potential interventions and critical uncertainties, with relevance to both decision makers and scientists. In the past decade, and especially during the COVID-19 pandemic, the field of epidemiology has seen substantial growth in the use of scenario projections. Multiple scenarios are often projected at the same time, allowing important comparisons that can guide the choice of intervention, the prioritization of research topics, or public communication. The design of the scenarios is central to their ability to inform important questions. In this paper, we draw on the fields of decision analysis and statistical design of experiments to propose a framework for scenario design in epidemiology, with relevance also to other fields. We identify six different fundamental purposes for scenario designs (decision making, sensitivity analysis, situational awareness, horizon scanning, forecasting, and value of information) and discuss how those purposes guide the structure of scenarios. We discuss other aspects of the content and process of scenario design, broadly for all settings and specifically for multi-model ensemble projections. As an illustrative case study, we examine the first 17 rounds of scenarios from the U.S. COVID-19 Scenario Modeling Hub, then reflect on future advancements that could improve the design of scenarios in epidemiological settings.

在许多领域,情景建模已成为探索长期预测以及预测如何取决于潜在干预措施和关键不确定性的重要工具,与决策者和科学家都息息相关。在过去十年中,尤其是在 COVID-19 大流行期间,流行病学领域对情景预测的使用大幅增加。通常会同时预测多种情景,以便进行重要的比较,从而指导干预措施的选择、研究课题的优先顺序或公众沟通。假设情景的设计是其能否为重要问题提供信息的核心。在本文中,我们借鉴了决策分析和实验统计设计领域的知识,提出了一个流行病学情景设计框架,该框架也适用于其他领域。我们确定了情景设计的六个不同基本目的(决策制定、敏感性分析、态势感知、前景扫描、预测和信息价值),并讨论了这些目的如何指导情景的结构。我们讨论了情景设计的内容和过程的其他方面,广泛适用于所有环境,特别适用于多模型集合预测。作为一个说明性案例研究,我们研究了美国 COVID-19 情景建模中心的前 17 轮情景,然后思考了可改进流行病学环境中情景设计的未来进展。
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引用次数: 0
Social contacts in Switzerland during the COVID-19 pandemic: Insights from the CoMix study COVID-19 大流行期间瑞士的社会接触:CoMix 研究的启示
IF 3.8 3区 医学 Q1 Medicine Pub Date : 2024-06-01 DOI: 10.1016/j.epidem.2024.100771
Martina L. Reichmuth , Leonie Heron , Philippe Beutels , Niel Hens , Nicola Low , Christian L. Althaus

To mitigate the spread of SARS-CoV-2, the Swiss government enacted restrictions on social contacts from 2020 to 2022. In addition, individuals changed their social contact behavior to limit the risk of COVID-19. In this study, we aimed to investigate the changes in social contact patterns of the Swiss population. As part of the CoMix study, we conducted a survey consisting of 24 survey waves from January 2021 to May 2022. We collected data on social contacts and constructed contact matrices for the age groups 0–4, 5–14, 15–29, 30–64, and 65 years and older. We estimated the change in contact numbers during the COVID-19 pandemic to a synthetic pre-pandemic contact matrix. We also investigated the association of the largest eigenvalue of the social contact and transmission matrices with the stringency of pandemic measures, the effective reproduction number (Re), and vaccination uptake. During the pandemic period, 7084 responders reported an average number of 4.5 contacts (95% confidence interval, CI: 4.5–4.6) per day overall, which varied by age and survey wave. Children aged 5–14 years had the highest number of contacts with 8.5 (95% CI: 8.1–8.9) contacts on average per day and participants that were 65 years and older reported the fewest (3.4, 95% CI: 3.2–3.5) per day. Compared with the pre-pandemic baseline, we found that the 15–29 and 30–64 year olds had the largest reduction in contacts. We did not find statistically significant associations between the largest eigenvalue of the social contact and transmission matrices and the stringency of measures, Re, or vaccination uptake. The number of social contacts in Switzerland fell during the COVID-19 pandemic and remained below pre-pandemic levels after contact restrictions were lifted. The collected social contact data will be critical in informing modeling studies on the transmission of respiratory infections in Switzerland and to guide pandemic preparedness efforts.

为减少 SARS-CoV-2 的传播,瑞士政府颁布了 2020 年至 2022 年的社会接触限制措施。此外,个人也改变了其社会接触行为,以限制 COVID-19 的风险。在本研究中,我们旨在调查瑞士人口社会接触模式的变化。作为 CoMix 研究的一部分,我们在 2021 年 1 月至 2022 年 5 月期间进行了 24 次调查。我们收集了社会接触数据,并构建了 0-4 岁、5-14 岁、15-29 岁、30-64 岁和 65 岁及以上年龄组的接触矩阵。我们估算了 COVID-19 大流行期间接触人数与大流行前合成接触矩阵的变化情况。我们还研究了社会接触和传播矩阵的最大特征值与大流行措施的严格程度、有效繁殖数 (Re) 和疫苗接种率之间的关联。在大流行期间,7084 名受访者报告的平均接触次数为每天 4.5 次(95% 置信区间:4.5-4.6),各年龄段和调查波次有所不同。5-14 岁儿童的接触次数最多,平均每天为 8.5 次(95% 置信区间:8.1-8.9),而 65 岁及以上的参与者报告的接触次数最少(3.4 次,95% 置信区间:3.2-3.5)。与大流行前的基线相比,我们发现 15-29 岁和 30-64 岁人群的接触次数减少最多。我们没有发现社会接触和传播矩阵的最大特征值与措施的严格程度、Re 或疫苗接种率之间存在统计学意义上的显著关联。在 COVID-19 大流行期间,瑞士的社会接触人数有所下降,在解除接触限制后仍低于大流行前的水平。收集到的社会接触数据对瑞士呼吸道传染病传播模型研究和指导大流行准备工作至关重要。
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引用次数: 0
Agent-based modeling of the COVID-19 pandemic in Florida 佛罗里达州 COVID-19 大流行的代理建模
IF 3.8 3区 医学 Q1 Medicine Pub Date : 2024-06-01 DOI: 10.1016/j.epidem.2024.100774
Alexander N. Pillai , Kok Ben Toh , Dianela Perdomo , Sanjana Bhargava , Arlin Stoltzfus , Ira M. Longini Jr , Carl A.B. Pearson , Thomas J. Hladish

The onset of the COVID-19 pandemic drove a widespread, often uncoordinated effort by research groups to develop mathematical models of SARS-CoV-2 to study its spread and inform control efforts. The urgent demand for insight at the outset of the pandemic meant early models were typically either simple or repurposed from existing research agendas. Our group predominantly uses agent-based models (ABMs) to study fine-scale intervention scenarios. These high-resolution models are large, complex, require extensive empirical data, and are often more detailed than strictly necessary for answering qualitative questions like “Should we lockdown?” During the early stages of an extraordinary infectious disease crisis, particularly before clear empirical evidence is available, simpler models are more appropriate. As more detailed empirical evidence becomes available, however, and policy decisions become more nuanced and complex, fine-scale approaches like ours become more useful. In this manuscript, we discuss how our group navigated this transition as we modeled the pandemic. The role of modelers often included nearly real-time analysis, and the massive undertaking of adapting our tools quickly. We were often playing catch up with a firehose of evidence, while simultaneously struggling to do both academic research and real-time decision support, under conditions conducive to neither. By reflecting on our experiences of responding to the pandemic and what we learned from these challenges, we can better prepare for future demands.

COVID-19 大流行的爆发推动了各研究小组广泛而又往往缺乏协调地开发 SARS-CoV-2 的数学模型,以研究其传播情况并为控制工作提供信息。在疫情爆发之初,对洞察力的迫切需求意味着早期的模型通常要么很简单,要么是从现有的研究议程中挪用过来的。我们小组主要使用基于代理的模型(ABM)来研究精细的干预方案。这些高分辨率模型庞大、复杂,需要大量的经验数据,而且往往比回答 "我们是否应该封锁 "等定性问题所需的数据更为详细。在特殊传染病危机的早期阶段,特别是在有明确的经验证据之前,更适合使用简单的模型。然而,随着更详细的经验证据的出现,以及政策决策变得更加细微和复杂,像我们这样的精细方法就变得更加有用了。在本手稿中,我们将讨论我们的研究小组在建立大流行病模型的过程中是如何驾驭这一转变的。建模者的角色往往包括近乎实时的分析,以及快速调整工具的艰巨任务。我们经常要在大量证据面前奋起直追,同时还要努力开展学术研究和实时决策支持,而这两方面的条件对我们都不利。通过反思我们应对大流行病的经验以及从这些挑战中学到的东西,我们可以更好地应对未来的需求。
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引用次数: 0
SIR… or MADAM? The impact of privilege on careers in epidemic modelling 先生......还是夫人?特权对流行病建模职业的影响。
IF 3.8 3区 医学 Q1 Medicine Pub Date : 2024-06-01 DOI: 10.1016/j.epidem.2024.100769
Anne Cori

As we emerge from what may be the largest global public health crises of our lives, our community of epidemic modellers is naturally reflecting. What role can modelling play in supporting decision making during epidemics? How could we more effectively interact with policy makers? How should we design future disease surveillance systems? All crucial questions. But who is going to be addressing them in 10 years’ time? With high burnout and poor attrition rates in academia, both magnified in our field by our unprecedented efforts during the pandemic, and with low wages coinciding with inflation at its highest for decades, how do we retain talent? This is a multifaceted challenge, that I argue is underpinned by privilege. In this perspective, I introduce the notion of privilege and highlight how various aspects of privilege (namely gender, ethnicity, sexual orientation, language and caring responsibilities) may affect the ability of individuals to access to and progress within academic modelling careers. I propose actions that members of the epidemic modelling research community may take to mitigate these issues and ensure we have a more diverse and equitable workforce going forward.

在我们刚刚摆脱可能是有生以来最大的全球公共卫生危机时,我们的流行病建模者群体自然会进行反思。建模在支持流行病期间的决策方面能发挥什么作用?我们如何才能更有效地与决策者互动?我们应该如何设计未来的疾病监测系统?这些都是至关重要的问题。但 10 年后谁来解决这些问题?学术界的职业倦怠率很高,自然减员率很低,而我们在大流行病期间所做的前所未有的努力更加剧了这两种情况,再加上低工资与几十年来最高的通货膨胀率,我们如何留住人才?这是一个多方面的挑战,我认为其根源在于特权。在这一观点中,我介绍了特权的概念,并强调了特权的各个方面(即性别、种族、性取向、语言和照顾责任)可能会如何影响个人进入学术建模职业并取得进步的能力。我提出了流行病建模研究界成员可以采取的行动,以缓解这些问题,确保我们拥有一支更加多元化和公平的工作队伍。
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引用次数: 0
Dynamic contact networks of residents of an urban jail in the era of SARS-CoV-2 SARS-CoV-2 时代城市监狱居民的动态接触网络
IF 3.8 3区 医学 Q1 Medicine Pub Date : 2024-05-15 DOI: 10.1016/j.epidem.2024.100772
Samuel M. Jenness , Karina Wallrafen-Sam , Isaac Schneider , Shanika Kennedy , Matthew J. Akiyama , Anne C. Spaulding

Background

In custodial settings such as jails and prisons, infectious disease transmission is heightened by factors such as overcrowding and limited healthcare access. Specific features of social contact networks within these settings have not been sufficiently characterized, especially in the context of a large-scale respiratory infectious disease outbreak. The study aims to quantify contact network dynamics within the Fulton County Jail in Atlanta, Georgia.

Methods

Jail roster data were utilized to construct social contact networks. Rosters included resident details, cell locations, and demographic information. This analysis involved 6702 male residents over 140,901 person days. Network statistics, including degree, mixing, and dissolution (movement within and out of the jail) rates, were assessed. We compared outcomes for two distinct periods (January 2022 and April 2022) to understand potential responses in network structures during and after the SARS-CoV-2 Omicron variant peak.

Results

We found high cross-sectional network degree at both cell and block levels. While mean degree increased with age, older residents exhibited lower degree during the Omicron peak. Block-level networks demonstrated higher mean degrees than cell-level networks. Cumulative degree distributions increased from January to April, indicating heightened contacts after the outbreak. Assortative age mixing was strong, especially for younger residents. Dynamic network statistics illustrated increased degrees over time, emphasizing the potential for disease spread.

Conclusions

Despite some reduction in network characteristics during the Omicron peak, the contact networks within the Fulton County Jail presented ideal conditions for infectious disease transmission. Age-specific mixing patterns suggested unintentional age segregation, potentially limiting disease spread to older residents. This study underscores the necessity for ongoing monitoring of contact networks in carceral settings and provides valuable insights for epidemic modeling and intervention strategies, including quarantine, depopulation, and vaccination, laying a foundation for understanding disease dynamics in such environments.Top of Form

背景在监狱和看守所等拘禁环境中,传染病的传播因过度拥挤和医疗条件有限等因素而加剧。这些环境中社会接触网络的具体特征尚未得到充分描述,尤其是在大规模呼吸道传染病爆发的背景下。本研究旨在量化佐治亚州亚特兰大市富尔顿县监狱内的接触网络动态。花名册包括居民详细信息、牢房位置和人口统计信息。本次分析涉及 6702 名男性囚犯,历时 140,901 人天。我们评估了网络统计数据,包括程度、混合率和解散率(在监狱内外的流动)。我们比较了两个不同时期(2022 年 1 月和 2022 年 4 月)的结果,以了解网络结构在 SARS-CoV-2 Omicron 变异高峰期间和之后的潜在反应。虽然平均程度随年龄的增长而增加,但老年居民在 Omicron 峰值期间的程度较低。街区级网络的平均程度高于小区级网络。累积度分布在 1 月到 4 月间有所增加,这表明疫情爆发后接触的增加。年龄偏好混合很强,尤其是年轻居民。结论尽管在奥密克龙疫情高峰期网络特征有所下降,但富尔顿县监狱内的接触网络仍为传染病的传播提供了理想的条件。特定年龄的混合模式表明存在无意的年龄隔离,这可能会限制疾病向年长居民的传播。这项研究强调了对囚禁环境中的接触网络进行持续监测的必要性,并为疫情建模和干预策略(包括隔离、消除人口和疫苗接种)提供了宝贵的见解,为了解此类环境中的疾病动态奠定了基础。
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引用次数: 0
A simulation-based approach for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data 一种基于模拟的方法,用于从时间汇总的疾病发病率时间序列数据中估算随时间变化的繁殖数量
IF 3.8 3区 医学 Q1 Medicine Pub Date : 2024-05-14 DOI: 10.1016/j.epidem.2024.100773
I. Ogi-Gittins , W.S. Hart , J. Song , R.K. Nash , J. Polonsky , A. Cori , E.M. Hill , R.N. Thompson

Tracking pathogen transmissibility during infectious disease outbreaks is essential for assessing the effectiveness of public health measures and planning future control strategies. A key measure of transmissibility is the time-dependent reproduction number, which has been estimated in real-time during outbreaks of a range of pathogens from disease incidence time series data. While commonly used approaches for estimating the time-dependent reproduction number can be reliable when disease incidence is recorded frequently, such incidence data are often aggregated temporally (for example, numbers of cases may be reported weekly rather than daily). As we show, commonly used methods for estimating transmissibility can be unreliable when the timescale of transmission is shorter than the timescale of data recording. To address this, here we develop a simulation-based approach involving Approximate Bayesian Computation for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data. We first use a simulated dataset representative of a situation in which daily disease incidence data are unavailable and only weekly summary values are reported, demonstrating that our method provides accurate estimates of the time-dependent reproduction number under such circumstances. We then apply our method to two outbreak datasets consisting of weekly influenza case numbers in 2019–20 and 2022–23 in Wales (in the United Kingdom). Our simple-to-use approach will allow accurate estimates of time-dependent reproduction numbers to be obtained from temporally aggregated data during future infectious disease outbreaks.

在传染病爆发期间跟踪病原体的传播性对于评估公共卫生措施的有效性和规划未来的控制策略至关重要。衡量传播性的一个关键指标是随时间变化的繁殖数,在一系列病原体爆发期间,可从疾病发病时间序列数据中实时估算出该繁殖数。在频繁记录疾病发病率的情况下,估算随时间变化的繁殖数的常用方法是可靠的,但这些发病率数据通常是按时间汇总的(例如,病例数可能是每周而不是每天报告)。正如我们所展示的,当传播的时间尺度短于数据记录的时间尺度时,常用的可传播性估算方法可能并不可靠。为了解决这个问题,我们在此开发了一种基于模拟的方法,该方法涉及近似贝叶斯计算(Approximate Bayesian Computation),用于从时间聚合的疾病发病率时间序列数据中估计随时间变化的繁殖数量。我们首先使用了一个模拟数据集,该数据集代表了一种无法获得每日疾病发病率数据而只能报告每周汇总值的情况,证明了我们的方法在这种情况下能够准确估计随时间变化的繁殖数量。然后,我们将我们的方法应用于两个疫情数据集,包括英国威尔士 2019-20 年和 2022-23 年的每周流感病例数。我们的方法简单易用,可以在未来的传染病爆发期间,从时间聚合数据中获得随时间变化的繁殖数量的准确估计。
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引用次数: 0
Unveiling ecological/evolutionary insights in HIV viral load dynamics: Allowing random slopes to observe correlational changes to CpG-contents and other molecular and clinical predictors 揭示 HIV 病毒载量动态中的生态/进化观点:通过随机斜率观察 CpG 含量及其他分子和临床预测因子的相关变化。
IF 3.8 3区 医学 Q1 Medicine Pub Date : 2024-05-14 DOI: 10.1016/j.epidem.2024.100770
Rocío Carrasco-Hernández , Humberto Valenzuela-Ponce , Maribel Soto-Nava , Claudia García-Morales , Margarita Matías-Florentino , Joel O. Wertheim , Davey M. Smith , Gustavo Reyes-Terán , Santiago Ávila-Ríos

In the context of infectious diseases, the dynamic interplay between ever-changing host populations and viral biology demands a more flexible modeling approach than common fixed correlations. Embracing random-effects regression models allows for a nuanced understanding of the intricate ecological and evolutionary dynamics underlying complex phenomena, offering valuable insights into disease progression and transmission patterns. In this article, we employed a random-effects regression to model an observed decreasing median plasma viral load (pVL) among individuals with HIV in Mexico City during 2019–2021. We identified how these functional slope changes (i.e. random slopes by year) improved predictions of the observed pVL median changes between 2019 and 2021, leading us to hypothesize underlying ecological and evolutionary factors. Our analysis involved a dataset of pVL values from 7325 ART-naïve individuals living with HIV, accompanied by their associated clinical and viral molecular predictors. A conventional fixed-effects linear model revealed significant correlations between pVL and predictors that evolved over time. However, this fixed-effects model could not fully explain the reduction in median pVL; thus, prompting us to adopt random-effects models. After applying a random effects regression model—with random slopes and intercepts by year—, we observed potential "functional changes" within the local HIV viral population, highlighting the importance of ecological and evolutionary considerations in HIV dynamics: A notably stronger negative correlation emerged between HIV pVL and the CpG content in the pol gene, suggesting a changing immune landscape influenced by CpG-induced innate immune responses that could impact viral load dynamics. Our study underscores the significance of random effects models in capturing dynamic correlations and the crucial role of molecular characteristics like CpG content. By enriching our understanding of changing host-virus interactions and HIV progression, our findings contribute to the broader relevance of such models in infectious disease research. They shed light on the changing interplay between host and pathogen, driving us closer to more effective strategies for managing infectious diseases.

Significance of the study

This study highlights a decreasing trend in median plasma viral loads among ART-naïve individuals living with HIV in Mexico City between 2019 and 2021. It uncovers various predictors significantly correlated with pVL, shedding light on the complex interplay between host-virus interactions and disease progression. By employing a random-slopes model, the researchers move beyond traditional fixed-effects models to better capture dynamic correlations and evolutionary changes in HIV dynamics. The discovery of a stronger negative correlation between pVL and CpG content in HIV-pol sequences suggests potential changes in the immune landscape and innate immune

在传染病方面,不断变化的宿主种群和病毒生物学之间的动态相互作用要求采用比普通固定相关性更灵活的建模方法。采用随机效应回归模型可以细致入微地了解复杂现象背后错综复杂的生态和进化动态,为疾病的发展和传播模式提供有价值的见解。在这篇文章中,我们采用随机效应回归来模拟墨西哥城艾滋病毒感染者在 2019-2021 年期间观察到的血浆病毒载量(pVL)中位数下降的情况。我们确定了这些功能斜率变化(即按年份划分的随机斜率)如何改善了对 2019 年至 2021 年期间观察到的 pVL 中位数变化的预测,从而提出了潜在生态和进化因素的假设。我们的分析涉及 7325 名抗逆转录病毒疗法无效的艾滋病毒感染者的 pVL 值数据集及其相关的临床和病毒分子预测因子。传统的固定效应线性模型显示 pVL 与随时间演变的预测因子之间存在显著的相关性。然而,这种固定效应模型无法完全解释中位 pVL 的下降,因此促使我们采用随机效应模型。在应用随机效应回归模型--按年份设置随机斜率和截距--后,我们观察到了当地 HIV 病毒种群中潜在的 "功能变化",突出了生态和进化因素在 HIV 动态变化中的重要性:HIV pVL 与 pol 基因中的 CpG 含量之间出现了明显更强的负相关,这表明受 CpG 诱导的先天性免疫反应影响,免疫环境正在发生变化,这可能会影响病毒载量的动态变化。我们的研究强调了随机效应模型在捕捉动态相关性方面的重要性,以及 CpG 含量等分子特征的关键作用。我们的研究结果丰富了我们对不断变化的宿主-病毒相互作用和艾滋病进展的理解,有助于此类模型在传染病研究中发挥更广泛的作用。它们揭示了宿主与病原体之间不断变化的相互作用,使我们更接近管理传染病的更有效策略。研究意义:本研究强调了 2019 年至 2021 年期间墨西哥城抗逆转录病毒疗法无效的艾滋病毒感染者血浆病毒载量中位数的下降趋势。它揭示了与 pVL 显著相关的各种预测因素,揭示了宿主-病毒相互作用和疾病进展之间复杂的相互作用。通过采用随机斜率模型,研究人员超越了传统的固定效应模型,更好地捕捉到了艾滋病毒动态变化中的动态相关性和进化变化。在 HIV-pol 序列中发现 pVL 与 CpG 含量之间存在更强的负相关,这表明免疫环境和先天免疫反应可能发生变化,为进一步研究 HIV 感染环境变化的适应性变化和反应开辟了途径。该研究强调分子特征是 pVL 的预测因素,这为病毒的流行病学和进化研究增添了宝贵的见解,为在人群水平上了解和管理 HIV 感染提供了新的途径。
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引用次数: 0
When do we need multiple infectious disease models? Agreement between projection rank and magnitude in a multi-model setting 何时需要多种传染病模型?多模型环境下预测等级与规模之间的一致性
IF 3.8 3区 医学 Q1 Medicine Pub Date : 2024-04-17 DOI: 10.1016/j.epidem.2024.100767
La Keisha Wade-Malone , Emily Howerton , William J.M. Probert , Michael C. Runge , Cécile Viboud , Katriona Shea

Mathematical models are useful for public health planning and response to infectious disease threats. However, different models can provide differing results, which can hamper decision making if not synthesized appropriately. To address this challenge, multi-model hubs convene independent modeling groups to generate ensembles, known to provide more accurate predictions of future outcomes. Yet, these hubs are resource intensive, and how many models are sufficient in a hub is not known. Here, we compare the benefit of predictions from multiple models in different contexts: (1) decision settings that depend on predictions of quantitative outcomes (e.g., hospital capacity planning), where assessments of the benefits of multi-model ensembles have largely focused; and (2) decisions settings that require the ranking of alternative epidemic scenarios (e.g., comparing outcomes under multiple possible interventions and biological uncertainties). We develop a mathematical framework to mimic a multi-model prediction setting, and use this framework to quantify how frequently predictions from different models agree. We further explore multi-model agreement using real-world, empirical data from 14 rounds of U.S. COVID-19 Scenario Modeling Hub projections. Our results suggest that the value of multiple models could be different in different decision contexts, and if only a few models are available, focusing on the rank of alternative epidemic scenarios could be more robust than focusing on quantitative outcomes. Although additional exploration of the sufficient number of models for different contexts is still needed, our results indicate that it may be possible to identify decision contexts where it is robust to rely on fewer models, a finding that can inform the use of modeling resources during future public health crises.

数学模型有助于公共卫生规划和应对传染病威胁。然而,不同的模型可能会得出不同的结果,如果不加以适当综合,就会妨碍决策。为了应对这一挑战,多模型中心召集了独立的建模小组来生成集合,众所周知,这样可以更准确地预测未来结果。然而,这些中心需要耗费大量资源,而且一个中心有多少模型才足够也不得而知。在此,我们比较了在不同情况下多个模型预测的益处:(1) 依赖于定量结果预测的决策环境(如医院容量规划),对多模型集合效益的评估主要集中于此;(2) 需要对替代流行病情景进行排序的决策环境(如比较多种可能的干预措施和生物不确定性下的结果)。我们开发了一个数学框架来模拟多模型预测环境,并使用该框架来量化不同模型预测一致的频率。我们利用来自 14 轮美国 COVID-19 情景建模中心预测的实际经验数据,进一步探讨了多模型一致性。我们的结果表明,在不同的决策环境下,多种模型的价值可能不同,如果只有少数几种模型可用,那么关注备选流行病情景的等级可能比关注定量结果更稳健。虽然仍需进一步探索不同情况下模型的足够数量,但我们的结果表明,有可能确定在哪些决策情况下依靠较少的模型更稳健,这一发现可为未来公共卫生危机期间模型资源的使用提供参考。
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Epidemics
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