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: 10703508
• 负责人: Eben Kenah
• 金额: $ 40.1万
• 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/10703508
• 关键词: 2019-nCoV; Adult; Binomial Model; Child; Cholera; Chronic Disease; Communicable Diseases; Computer software; Data; Data Set; Development; Documentation; Ebola; Effectiveness; Effectiveness of Interventions; Epidemiologist; Epidemiology; Event; Evolution; Generations; Heterogeneity; Hospitals; Household; Housing; Individual; Infection; Infectious Disease Epidemiology; Influenza; Intervention Trial; Longitudinal Studies; Manuals; 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; Specific qualifier value; Statistical Methods; Time; Treatment Efficacy; Uncertainty; Vaccinated; Vaccination; Vaccinee; Viral; Work; Workplace; clinical trial enrollment; code development; design; discrete time; disease transmission; disorder control; effectiveness evaluation; emerging pathogen; epidemic response; 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.

项目概要/摘要 家庭、教室、医院、工作场所和其他密切接触场所是病毒传播的主要场所。 因为它们使流行病学家能够遵循明确的定义。 有感染风险的人群，对这些环境中传染病传播的纵向研究可以 对传染性和易感性的决定因素、疾病的演变产生独特的见解 受感染个体随时间的传染性（传染性概况）以及控制的有效性 然而，此类研究很少进行，并且经常进行分析。 荷兰国际集团为慢性病或人口水平监测数据设计的统计方法，可以重新 认识到这些研究在为公共卫生应对措施提供信息方面的巨大潜力。 传染病，开发用户友好且多功能的软件工具至关重要 该软件还必须支持正确的计算。 统计功效和样本量的关系，以帮助设计观察性研究和干预措施 基于我们在方法研究和代码开发方面的丰富经验。 密切接触人群中多种传染病的预防（包括流感、埃博拉、诺如病毒、 霍乱、SARS-CoV-2 等），我们建议开发一种用户友好、多功能且计算高效的 R 包称为 TranStat。我们的团队由流行病学家、生物统计学家和计算生物学家组成。 将实现以下具体目标： (1) 集成离散时间的独立实现 将二项式模型和连续时间成对生存模型链接到单个 R 包中这一目标。 将统一两个包的数据输入、模型规范和输出格式，同时改进用户 (2) 开发仿真工具 计算密切接触环境中观察研究和干预试验的功效和样本量。 这一目标将支持传染病在室内传播的流行病学研究的设计 (3) 可以为控制策略提供信息的场所、教室、聚集住房设施、工作场所等。 建立处理结果和协变量中缺失数据并解释未观察到的异质性的能力 遗传性（例如超级传播）这一目标将允许 TranStat 用户保留部分- 在分析中观察数据，以最大限度地提高统计能力，同时避免偏差并准确量化 集成的、扩展的、免费提供的 TranStat 软件包将允许流行病。 科学家通过研究传染病的传播来产生详细和可靠的科学见解 通过这些见解，TranStat 将帮助决策者、公共卫生官员、 与广大人民群众共同努力，更加有效地控制疫情。