Developing TranStat: A user-friendly R package for the analysis of infectious disease transmission and control among close contacts

开发 TranStat：一个用户友好的 R 包，用于分析密切接触者之间的传染病传播和控制

• 批准号: 10576467
• 负责人: Eben Kenah
• 金额: $ 38.32万
• 依托单位: UNIVERSITY OF GEORGIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-12 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10576467
• 关键词: 2019-nCoV; Adult; Binomial Model; Cholera; Chronic Disease; Clinical Trials; Communicable Diseases; Computer software; Data; Data Set; Development; Documentation; Ebola; Effectiveness; Effectiveness of Interventions; Enrollment; Epidemic; Epidemiologist; Epidemiology; Event; Evolution; Generations; Heterogeneity; Hospitals; Household; Housing; Individual; Infectious Disease Epidemiology; Influenza; Intervention; Intervention Trial; Longitudinal Studies; Manuals; Masks; Modeling; Names; Norovirus; Observational Study; Outcome; Output; Pathogenicity; Persons; Policy Maker; Population; Populations at Risk; Predisposition; Prophylactic treatment; Public Health; Research Methodology; Research Personnel; Risk; Sample Size; Software Tools; Statistical Methods; Time; Uncertainty; Vaccinated; Vaccination; Vaccinee; Work; Workplace; base; child protection; code development; design; discrete time; disease transmission; disorder control; effectiveness evaluation; emerging pathogen; epidemiology study; experience; flexibility; high risk; improved; infection risk; insight; intervention program; longitudinal analysis; pathogen; research and development; respiratory pathogen; response; simulation; surveillance data; tool; transmission process; user-friendly; vaccine efficacy

Project summary/abstract Households, classrooms, hospitals, workplaces, and other close contact settings are major venues for the spread of many infectious pathogens. Because they allow epidemiologists to follow a well-defined population at risk of infection, longitudinal studies of infectious disease transmission in these settings can generate unique insights into the determinants of infectiousness and susceptibility, the evolution of in- fectiousness over time in infected individuals (the infectiousness profile), and the effectiveness of control strategies (e.g., vaccination or masking). However, such studies are rarely done and are often analyzed us- ing statistical methods designed for chronic diseases or population-level surveillance data, which can re- sult in severe bias. To realize the enormous potential of these studies to inform public health responses to infectious diseases, it is critical to develop user-friendly and versatile software tools that provide access to statistical methods designed for close contact settings. This software must also support the proper calcu- lation of statistical power and sample size in order to aid the design of observational studies and interven- tion trials in these settings. Based on our extensive experience in methodological research and code devel- opment for a variety of infectious diseases in close contact groups (including influenza, Ebola, norovirus, cholera, SARS-CoV-2, etc.), we propose to develop a user-friendly, versatile, and computationally efficient R package called TranStat. Our team of epidemiologists, biostatistician, and computational biologists will achieve the following Specific Aims: (1) To integrate independent implementations of discrete-time chain binomial models and continuous-time pairwise survival models into a single R package. This aim will unify data input, model specification, and output formats for the two packages while improving user- friendliness, computational efficiency, functionality, and documentation. (2) To develop simulation tools to calculate power and sample size for observational studies and intervention trials in close contact settings. This aim will support the design of epidemiological studies of infectious disease transmission in house- holds, classrooms, congregate housing facilities, workplaces, etc., that can inform control strategies. (3) To build capacity to handle missing data in outcomes and covariates and to account for unobserved hetero- geneity in transmissibility (e.g., superspreading). This aim will allow users of TranStat to retain partially- observed data in their analyses to maximize statistical power while avoiding bias and accurately quanti- fying uncertainty. The integrated, expanded, and freely available TranStat package will allow epidemi- ologists to generate detailed and reliable scientific insights by studying infectious disease transmission in close contact groups. Through these insights, TranStat will help policy-makers, public health officials, and the public work together to control epidemics more effectively.

项目摘要/摘要 家庭，教室，医院，工作场所和其他紧密的接触设置是主要的场所 许多感染性病原体的传播。因为它们允许流行病学家遵循良好的确定 有感染风险的人口，在这些情况下对感染疾病传播的纵向研究可以 对传染性和敏感性的决定者产生独特的见解，进化 受感染个体（感染性的传染性）和控制的有效性的态度 策略（例如疫苗接种或掩盖）。但是，此类研究很少进行，并且经常被分析 为慢性疾病或人口水平监测数据设计的统计方法 严重偏见。实现这些研究的巨大潜力，以告知公共卫生的回应 传染病，开发用户友好和多功能的软件工具至关重要 统计方法专为紧密的联系设置而设计。该软件还必须支持适当的计算 统计能力和样本量的局面，以帮助设计观察性研究和中间 在这些环境中进行的试验。根据我们在方法论研究和代码开发方面的丰富经验 - 在密切接触组中的各种传染病的选项（包括流体，埃博拉病毒，诺如病毒， 霍乱，sars-cov-2等），我们建议开发一种用户友好，多功能和计算有效的 R包称为Transtat。我们的流行病学家，生物统计学家和计算生物学家团队 将实现以下特定目的：（1）整合离散时间的独立实现 链二项式模型和连续的成对生存模型中的单个R包装。这个目标 将统一两个软件包的数据输入，模型规范和输出格式，同时改善用户 - 友好，计算效率，功能和文档。 （2）开发模拟工具 在密切接触设置中计算观测研究和干预试验的功率和样本量。 该目标将支持在房屋中传播传染病传播流行病学研究的设计。 持有，教室，聚集住房设施，工作场所等，可以为控制策略提供信息。 （3）到 建立在结果和协变量中处理丢失数据的能力，并解释未观察到的异质 - 托米置态中的基因（例如，超级广播）。此目标将使Transtat的用户可以部分保留 - 在他们的分析中观察到数据以最大化统计能力，同时避免偏见并准确量化 fy不确定性。集成，扩展和免费的可用transtat软件包将允许epidemi- 神学家通过研究传染病的传播来产生详细且可靠的科学见解 关闭联系人组。通过这些见解，Transtat将帮助决策者，公共卫生官员， 公众共同努力，以更有效地控制流行病。