Modeling the dynamics of disease elimination

消灭传染病往往是公共卫生界的目标。虽然这个目标很少 实现，流行病学数据库的巨大扩展提供了新的机会，以测试 关于数学模型消除的假设。除了提高我们的科学 了解疾病传播，通过数学建模验证的假设为公众提供了 卫生从业人员对如何消除特定病原体进行更结构化的定量评估， 可以实现。该提案旨在开发一套相互关联的建模工具，以支持消除 传染性疾病。将考虑用于实现疾病消除的各种过程 包括使用大规模药物管理来消除被忽视的热带疾病，如沙眼， 针对SARS-CoV-2等可预防疾病的疫苗接种，以及抗生素管理工作， 耐甲氧西林金黄色葡萄球菌（MRSA）等耐药感染。一个关键的主题是 消除疾病的亚临界传播要求，这意味着新的平均数量 每个病例引起的感染少于一个。一个主要的目标是阐明的传输动力学 濒临灭绝的亚临界疾病。传播异质性可能来自于许多 机制包括某些个体的超级脱落，易感性的口袋，如在一个社区 低疫苗摄取和接触结构，其中一些人有可能感染许多人， 他人对各种疾病传播模式的模拟将用于制定不同的测量方法 传播的异质性。此外，还将推出推断和补偿观测误差的新技术， 综合观察过程的数据，如查明的病例比例， 通过接触者追踪计划进行回顾。传输动态模型将用于识别 传播热点和超级传播者，可能危及消除。人物、地区或事件 传播潜力的增加可以维持地方病的传播，即使人口- R的水平平均值可以小于1。在实现这一目标的第一阶段，我们将利用现有模型， 构建一套计算机模拟，以比较各种扫描统计数据的性能， 偶然发现超出预期的疾病负担。在第二阶段，我们将应用这些扫描 统计到观测数据。识别传输热点和超读取器允许优化 疾病消除策略。为了消灭疾病，仅仅确定传播途径是不够的- 热点或超级传播活动。需要一种策略来压制那些 导致更高水平的传播。我们将使用数学和计算模型来研究疾病 消除，以解决1）控制干预措施的影响，2）有限治疗的最佳分配 3）监测治疗效果。

Elimination of an infectious disease is often a goal of the public health community. Although that goal is rarely achieved, the tremendous expansion of epidemiological databases provides new opportunities to test hypotheses concerning elimination with mathematical modeling. Besides improving our scientific understanding of disease transmission, hypotheses validated through mathematical modeling provide public health practitioners with a more structured, quantitative assessment of how elimination of specific pathogens can be achieved. This proposal aims to develop an interconnected set of modeling tools to support elimination of communicable diseases. A variety of processes used to achieve disease elimination will be considered including use of mass drug administration to eliminate neglected tropical diseases such as trachoma, vaccination for preventable diseases such as SARS-CoV-2, and antibiotic stewardship efforts to curtail drug resistant infections such as methicillin-resistant Staphylococcus aureus (MRSA). A key theme is the requirement of subcritical transmission for disease elimination, meaning that the average number of new infections each case causes is less than one. A major goal is to elucidate the transmission dynamics of subcritical diseases on the brink of elimination. Transmission heterogeneity may arise from many mechanisms including super-shedding of certain individuals, pockets of susceptibility such as in a community with low vaccine uptake, and contact structure in which some individuals have the potential to infect many others. Simulations of various patterns of disease transmission will be used to develop distinct measurements of transmission heterogeneity. In addition, new techniques to infer and compensate for observation error will be developed that integrate data on the observation process, such as the proportion of cases identified retrospectively via contact tracing programs. Models of transmission dynamics will be used to identify transmission-hotspots and superspreaders that can jeopardize elimination. People, areas, or events that have increased transmission potential can maintain endemic disease transmission even though the population- level average value of R may be less than one. In the first stage of this objective, we will use existing models to construct a suite of in silico simulations to compare the performance of various scan statistics designed to detect disease burden beyond what is expected by chance. In the second stage, we will apply these scan statistics to observational data. Identification of transmission-hotspots and supersreaders permits optimization of disease elimination strategies. To eliminate disease, it is insufficient to merely identify transmission- hotspots or superspreading activity. A strategy is needed for suppressing the sites, events, or people that cause higher levels of transmission. We will use mathematical and computational models for disease elimination to address 1) the impact of control interventions, 2) the optimal distribution of a limited treatment supply, and 3) monitoring of treatment efficacy.