US-UK Collab: Adaptive surveillance and control for the elimination of endemic disease

美英合作：消除地方病的适应性监测和控制

• 批准号: BB/T004312/1
• 负责人: Michael Tildesley
• 金额: $ 90.5万
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FT004312%2F1
• 关键词: US; UK; Collab; Adaptive; surveillance

Quantitative observations of infectious disease incidence define the scope of human and animal health problems, and, over time, reveal the underlying dynamics that allow us to develop, and then evaluate, effective control strategies. As such, surveillance systems are crucial to developing evidence based policy. Developing an efficient surveillance network requires knowledge of the system under surveillance. This proposal develops a framework for the evolution of surveillance systems and their adaptation in tandem with the development of models to support decision-making, changes in control and elimination policies, and the evolving dynamics of disease transmission in response to control. In many settings where surveillance networks remain rudimentary (e.g. in under-resourced settings or diseases that are not yet considered priorities), development of evidence-based policy is hampered by lack of locally-specific demographic, surveillance, serotype, and movement/connectivity data. While efforts to collect these data will always advance scientific understanding, we propose that model-based prioritization of data collection and surveillance system design will lead to more efficient targeting of data collection to specifically support disease control and eradication goals. This work will develop general methods for model-based prioritization of data collection and surveillance system design using highly resolved, long-term, strain-specific surveillance data on foot-and-mouth disease (FMD) virus incidence in Turkey from 2001-2012. This proposed work will develop the first country-scale, strain-specific models of endemic FMD transmission. FMD is a significant livestock disease in many low and middle income countries (LMICs) and the models developed here will provide insight into the mechanisms of FMD persistence and serve as a guide for the development of locally specific models to support policy development in endemic countries. This proposal will develop novel methods for developing hybrid surveillance systems to efficiently mix 1) passive surveillance with model-based allocation of active surveillance effort, and 2) field diagnosis with diagnostic confirmation. The data available for Turkey reflect a "gold standard" for what could be achieved in counties that are currently developing FMD control and elimination plans. The methods that will be developed in this proposal will address the iterative development of empirical models and surveillance systems for FMD and will provide a roadmap for countries that currently do not have highly resolved surveillance data. The co-investigators will engage with FMD managers in Turkey, through annual meetings, to transfer models and insights on surveillance and control strategies, and to develop additional control strategies to be evaluated with the models developed herein. The co-investigators will further engage with FMD managers in Kenya and Uganda through existing EuFMD training workshops to develop applications of lessons from the Turkey case-study in those countries. Many control programs for endemic disease suffer from limited, or imperfect, data collection. The examples and methods developed in this proposal will be broadly applicable to human and animal disease settings where surveillance, models, and policy need to be developed in tandem. The co-investigators for this proposal have a long history of engagement with both human and animal health policy organizations and will work to translate lessons from the FMD case study to additional settings. Junior project staff will be trained on the methods developed in this proposal in application to FMD and encouraged to develop applications in other endemic animal and human disease systems (e.g. avian influenza, measles, rubella, meningitis, rotavirus) studied by the assembled co-PIs. These individuals will serve a valuable role in the onward translation of quantitative science to regional and national agencies throughout their careers.

传染病发病率的定量观察定义了人类和动物健康问题的范围，随着时间的推移，揭示了潜在的动态，使我们能够制定，然后评估，有效的控制策略。因此，监测系统对于制定循证政策至关重要。建立一个有效的监测网络需要了解监测系统。该提案为监测系统的演变及其调整制定了一个框架，同时制定了支持决策的模型，控制和消除政策的变化，以及疾病传播对控制的不断演变的动态。在许多监测网络仍处于初级阶段的情况下（例如，在资源不足的情况下或尚未被视为优先事项的疾病），缺乏当地特定的人口统计、监测、血清型和移动/连接数据阻碍了循证政策的制定。虽然收集这些数据的努力总是会促进科学的理解，我们建议，基于模型的数据收集和监测系统设计的优先顺序将导致更有效的数据收集，以专门支持疾病控制和根除目标。这项工作将开发基于模型的数据收集和监测系统设计的优先级的一般方法，使用高分辨率，长期，株特异性监测数据口蹄疫（FMD）病毒在土耳其的发病率从2001年至2012年。这项拟议的工作将开发第一个国家规模的、特定毒株的口蹄疫地方性传播模型。口蹄疫是许多低收入和中等收入国家（LMICs）的一种重要牲畜疾病，这里开发的模型将深入了解口蹄疫持续存在的机制，并作为制定当地特定模型的指南，以支持流行国家的政策制定。该提案将开发用于开发混合监视系统的新方法，以有效地混合1）被动监视与基于模型的主动监视工作分配，以及2）现场诊断与诊断确认。土耳其的现有数据反映了目前正在制定口蹄疫控制和消除计划的国家可以实现的“黄金标准”。本提案中将开发的方法将解决口蹄疫经验模型和监测系统的迭代开发问题，并将为目前没有高分辨率监测数据的国家提供路线图。共同研究人员将通过年度会议与土耳其的口蹄疫管理人员接触，以转移有关监测和控制策略的模型和见解，并制定其他控制策略，以使用本文开发的模型进行评估。共同调查人员将通过现有的欧洲口蹄疫培训讲习班，进一步与肯尼亚和乌干达的口蹄疫管理人员接触，以便在这些国家应用土耳其案例研究的经验教训。地方病的许多控制方案都受到数据收集有限或不完善的影响。本提案中提出的例子和方法将广泛适用于需要同时制定监测、模型和政策的人类和动物疾病环境。该提案的共同研究者与人类和动物卫生政策组织有着长期的合作历史，并将努力将口蹄疫案例研究的经验教训转化为其他环境。初级项目工作人员将接受培训，了解本建议书中开发的方法在口蹄疫中的应用，并鼓励他们开发在其他地方性动物和人类疾病系统（如禽流感、麻疹、风疹、脑膜炎、轮状病毒）中的应用，这些疾病系统是由联合主要研究者研究的。这些人将在他们的职业生涯中为区域和国家机构的定量科学的向前翻译发挥宝贵的作用。

项目成果

Modelling seasonality of Lassa fever incidences and vector dynamics in Nigeria.

• DOI: 10.1371/journal.pntd.0011543
• 发表时间: 2023-11
• 期刊: PLoS neglected tropical diseases
• 影响因子: 3.8

Estimated survival functions from Causes of delayed outbreak responses and their impacts on epidemic spread

根据延迟爆发反应的原因及其对流行病传播的影响估计生存函数

• DOI: 10.6084/m9.figshare.14096946
• 发表时间: 2021
• 作者: Tao Y

Supplementary Figures and tables for 'Vote-processing rules for combining control recommendations from multiple models'

“组合多个模型的控制建议的投票处理规则”的补充图和表

• DOI: 10.6084/m9.figshare.20180446
• 发表时间: 2022
• 作者: Probert W

Temporal variations in logistical quantities from Causes of delayed outbreak responses and their impacts on epidemic spread

疫情反应延迟的原因及其对疫情传播的影响导致物流量随时间变化

• DOI: 10.6084/m9.figshare.14096940
• 发表时间: 2021
• 作者: Tao Y

Vote-processing rules for combining control recommendations from multiple models.

投票处理规则，用于结合来自多个模型的控制建议。

• DOI: 10.1098/rsta.2021.0314
• 发表时间: 2022-10-03
• 期刊: PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
• 影响因子: 5
• 作者: Probert, William J. M.;Nicol, Sam;Ferrari, Matthew J.;Li, Shou-Li;Shea, Katriona;Tildesley, Michael J.;Runge, Michael C.
• 通讯作者: Runge, Michael C.