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。 有感染风险的人口，在这些环境中的infecityuse transmente传播的纵向研究可以 对传染病和易感性的确定材料产生独特的见解，进化 随着时间的流逝，受感染的个体（感染性验证）和控制有效的聚会性 策略（例如，疫苗接种或掩盖）。 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，以实现研究的巨大潜力 传染病，开发用户友好和多功能的软件工具至关重要 为关闭关闭接触设置而设计的统计方法也必须支持适当的计算。 统计能力和尺寸尺寸尺寸的尺寸的尺寸有助于某些设计观测研究和介入 在这些环境中进行的试验。 近距离接触组中一种多样性疾病的选择（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炒，多才多艺和计算有效的 R包称为transtat。 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 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）。 计算观察性研究的功率和样本量，并在紧密的情况下进行IND干预试验。 该目标将支持在房屋中的感染性透射率横向研究的设计 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 构建Capace以在结果和协变量中处理丢失的数据，并解释未观察到的异质 - 透气性的基因（例如，超级公布）。 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 不确定性。 神学家通过研究感染性转移传播来产生详细且可靠的科学见解 通过这些见解进行关闭，与政策制定者，公共卫生的联系 公众共同努力控制流行病。