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

项目摘要/摘要 家庭、教室、医院、工作场所和其他密切接触的环境是 许多传染性病原体的传播。因为他们允许流行病学家跟踪一个很好的fiNed 在这些环境中对有感染风险的人群进行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产生独特的见解， 感染个体随时间变化的繁殖力(fiLE的传染性)，以及控制的有效性 战略(例如，接种疫苗或掩蔽)。然而，这样的研究很少进行，而且经常被分析给我们- 为慢性病或人口级监测数据设计的统计方法，可以重新计算 结果产生了严重的偏见。认识到这些研究的巨大潜力，为公共卫生应对措施提供信息 对于传染病，至关重要的是开发用户友好和通用的软件工具，以提供访问 为近距离接触设置设计的统计方法。该软件还必须支持正确的计算- 统计能力和样本量的变化，以辅助观察性研究和干预研究的设计。 在这些环境下进行的试验。基于我们在方法研究和代码开发方面的丰富经验- 在密切接触人群(包括fl、埃博拉、诺沃克病毒、 霍乱，SARS-CoV-2等)，我们建议开发一个用户友好，通用的，和计算有效的fi 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 将实现以下特定的fic目标：(1)集成离散时间的独立实现 链二项模型和连续时间成对生存模型到单个R包。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-fi 友好性、计算效率、功能和文档。(2)开发仿真工具 计算近距离接触环境下观察性研究和干预试验的功率和样本量。 这一目标将支持室内传染病传播流行病学研究的设计-- 货舱、教室、集合住房设施、工作场所等，这些都可以为控制策略提供信息。(3)至 建设能力，以处理结果和协变量中的缺失数据，并解释未观察到的异质性 遗传性(例如，超级传播)。这一目标将允许TranStat的用户保留部分- 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 加剧了不确定性。集成的、扩展的和免费提供的TranStat包将允许流行- 科学家通过研究传染病的传播来产生详细和可靠的科学fic见解 关闭联系人组。通过这些洞察，TRANSTAT将帮助政策制定者、fiCIAL的公共卫生、 公众共同努力，更有效地控制疫情。

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.