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

美英合作：消除地方病的适应性监测和控制

基本信息

批准号：
1911962
负责人：
Matthew Ferrari
金额：
$ 170.73万
依托单位：
Pennsylvania State Univ University Park
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1911962&HistoricalAwards=false
关键词：
US UK Collab Adaptive surveillance

项目摘要

This research will develop theory and methods for disease surveillance systems. Such systems are crucial for the control and elimination of animal and human infectious diseases. For newly emerging diseases, as well as those that have not been well studied, the development of evidence-based policy is hampered by a lack of locally-specific data. This lack is also faced by many low-and-middle income countries. The project will use outbreaks of foot-and-mouth disease (FMD) virus in Turkey as a case-study. Working with well-documented surveillance on historical outbreaks, this project will identify what data are necessary to estimate disease burden and outbreak risk, and will evaluate FMD mitigation strategies. The researchers will then work with veterinary training workshops in Kenya and Uganda to translate lessons learned from the Turkey case-study to the development of surveillance programs in East Africa. The resulting theory and methods will serve as a guide for developing surveillance systems for a wide variety of animal and human diseases in the US and elsewhere. The project also will provide an international research experience to US graduate students and post-doctoral scholars.This project will develop theory and methods for the scale-up and adaptation of infectious disease surveillance systems in tandem with the development of dynamic disease transmission models to support disease control and elimination policies. While surveillance data will always advance scientific understanding, model-based prioritization of data collection and surveillance system design will lead to more efficient data collection to specifically support policy goals. This research will retrospectively analyze a time series of FMD virus outbreaks in livestock in Turkey between 2001-2012. This data set, which includes detailed records of the location, timing, size, and virus strain for each outbreak is uncommonly well-resolved and represents a gold standard of a nearly perfect surveillance system for a livestock disease. This project will first fit spatially-explicit stochastic models of farm-to-farm FMD transmission to the full data to derive the best-case understanding of dynamics, and predictability, of an FMD outbreak; this analysis will be the first country-scale model of endemic FMD transmission. Then, the researchers will strip away elements of the data to assess the minimally sufficient data necessary to estimate the burden of FMD disease, estimate the risk of FMD outbreaks, and evaluate FMD mitigation strategies, relative to the best-case analysis. The result will be novel methods for the integration of current model predictions into the optimal allocation of future surveillance to detect and/or mitigate outbreaks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项研究将开发疾病监测系统的理论和方法。这种系统对于控制和消除动物和人类传染病至关重要。对于新出现的疾病以及尚未得到充分研究的疾病，基于循证的政策的制定因缺乏本地特定数据而受到阻碍。许多低水平收入国家也面临着这种缺乏。该项目将使用土耳其的脚和口疾病（FMD）病毒的爆发作为病例研究。该项目应对历史暴发的有据可查的监视，将确定哪些数据估计疾病负担和爆发风险，并评估FMD缓解策略。然后，研究人员将与肯尼亚和乌干达的兽医培训研讨会合作，将从土耳其案例研究中学到的教训转化为东非监视计划的制定。由此产生的理论和方法将作为为美国和其他地方各种动物和人类疾病开发监视系统的指南。该项目还将为美国的研究生和博士后学者提供国际研究经验。该项目将开发理论和方法，以与发展动态疾病传播模型相结合地扩大和适应传染病监测系统，以支持疾病控制和消除政策。虽然监视数据将始终提高科学理解，但基于模型的数据收集和监视系统设计的优先级将导致更有效的数据收集，以特别支持政策目标。这项研究将回顾性地分析2001 - 2012年间土耳其牲畜中FMD病毒暴发的时间序列。该数据集包括每次爆发的位置，时机，大小和病毒菌株的详细记录，这是罕见的良好解决方案，代表了用于牲畜疾病的几乎完美监视系统的金标准。该项目将首先拟合农场FMD传输的空间说明随机模型，以获取对FMD爆发的动力学和可预测性的最佳理解；该分析将是第一个国家规模的FMD传播模型。然后，研究人员将剥离数据的要素，以评估相对于最佳分析，评估估计FMD疾病负担，估计FMD爆发的风险并评估FMD缓解策略所需的最小数据。结果将是将当前模型预测整合到未来监视的最佳分配中以检测和/或减轻疫情的最佳分配的新方法。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛的影响审查标准来评估的支持。