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

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.