Modeling the dynamics of disease elimination

模拟疾病消除的动态

• 批准号: 10685327
• 负责人: Seth Blumberg
• 金额: $ 40.38万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10685327
• 关键词: 2019-nCoV; Address; Antibiotics; Area; Communicable Diseases; Communities; Community Health; Compensation; Computer Models; Contact Tracing; Data; Databases; Disease; Disease model; Drug resistance; Endemic Diseases; Epidemiology; Event; Goals; Heterogeneity; Individual; Infection; Intervention; Measurement; Modeling; Monitor; Pattern; Performance; Persons; Pharmaceutical Preparations; Population; Predisposition; Process; Public Health; Scanning; Site; Structure; Techniques; Testing; Trachoma; Treatment Efficacy; Vaccination; burden of illness; data integration; design; disease transmission; improved; in silico; mathematical model; methicillin resistant Staphylococcus aureus; neglected tropical diseases; pathogen; programs; simulation; statistics; tool; transmission process; vaccine acceptance

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.

消除传染病通常是公共卫生社区的目标。虽然这个目标很少 达到的，流行病学数据库的巨大扩展为测试提供了新的机会 关于用数学建模消除的假设。除了改善我们的科学 了解疾病传播，通过数学建模验证的假设为公众提供 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。该建议旨在开发一组相互联系的建模工具来支持消除 传染病。将考虑用于消除疾病的各种过程 包括使用大规模药物来消除被忽视的热带疾病，例如沙眼， 可预防疾病的疫苗接种，例如SARS-COV-2和抗生素管理，以减少药物 耐药感染，例如金黄色葡萄球菌（MRSA）。一个关键主题是 对消除疾病的亚临界传播的需求，这意味着新的平均数量 感染每个病例的原因小于一个。一个主要目标是阐明 消除边缘的亚临界疾病。传输异质性可能来自许多 机制，包括某些人的超级分解，易感性的口袋，例如在社区中 疫苗吸收低，并且有些人有可能感染许多人的接触结构 其他的。模拟各种疾病传播模式将用于开发不同的测量 传输异质性。此外，要推断和补偿观察错误的新技术将是 开发了整合观察过程的数据，例如确定的案例比例 通过联系跟踪程序进行追溯。传输动力学模型将用于识别 变速器 - 螺柱和超级公开者会危及消除。人，区域或活动 即使种群 R的电平均值可能小于1。在这个目标的第一阶段，我们将使用现有模型来 构建一套在计算机模拟中，以比较旨在的各种扫描统计数据的性能 发现疾病负担超出了偶然的期望。在第二阶段，我们将应用这些扫描 观察数据的统计数据。识别传输锅和超级阅读器允许优化 消除疾病的策略。为了消除疾病，不足以识别传播 - 热点或超级活动。需要一种策略来压制网站，事件或 导致更高的传输水平。我们将使用疾病的数学和计算模型 消除解决1）控制干预措施的影响，2）有限治疗的最佳分布 供应和3）监测治疗功效。

项目成果

Temporal Trends in Phenotypic Macrolide and Nonmacrolide Resistance for Streptococcus pneumoniae Nasopharyngeal Samples Up to 36 Months after Mass Azithromycin Administration in a Cluster-Randomized Trial in Niger.

• DOI: 10.4269/ajtmh.23-0431
• 发表时间: 2023-11-01
• 期刊: The American journal of tropical medicine and hygiene
• 作者: Hazel A;Arzika AM;Abdou A;Lebas E;Porco TC;Maliki R;Doan T;Lietman TM;Keenan JD;Blumberg S
• 通讯作者: Blumberg S