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.

项目总结/文摘