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

本研究将为疾病监测系统发展理论和方法。这些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，以及那些尚未得到充分研究的疾病，由于缺乏当地具体数据，妨碍了循证政策的制定。许多低收入和中等收入国家也面临这种缺乏。该项目将利用土耳其爆发的口蹄疫病毒作为案例研究。该项目将与记录良好的历史疫情监测工作合作，确定估算疾病负担和疫情风险所需的数据，并将评估口蹄疫缓解战略。然后，研究人员将与肯尼亚和乌干达的兽医培训讲习班合作，将从土耳其案例研究中吸取的经验教训转化为东非监测项目的发展。由此产生的理论和方法将为在美国和其他地方开发针对各种动物和人类疾病的监测系统提供指导。该项目还将为美国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，以扩大和调整传染病监测系统，同时开发动态疾病传播模型，以支持疾病控制和消除政策。虽然监测数据总是会促进科学理解，但基于模型的数据收集优先级和监测系统设计将导致更有效的数据收集，以具体支持政策目标。本研究将回顾性分析2001-2012年期间土耳其牲畜中口蹄疫病毒暴发的时间序列。该数据集包括每次暴发的地点、时间、规模和病毒毒株的详细记录，解决得非常好，代表了近乎完美的牲畜疾病监测系统的黄金标准。该项目将首先将农场到农场口蹄疫传播的空间明确随机模型与完整数据相匹配，以获得对口蹄疫爆发动态和可预测性的最佳案例理解；这一分析将是第一个国家级口蹄疫传播模型。然后，研究人员将剥离数据的元素，以评估估算口蹄疫负担、估算口蹄疫爆发风险和评估口蹄疫缓解策略所需的最低限度的数据，相对于最佳案例分析。其结果将是将当前模型预测整合到未来监测的最佳分配中，以发现和/或减轻疫情的新方法。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.