Core B: Data Management and Statistics

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Abstract The major function of the Data Management and Analysis Core will be to support the data analytic needs of all three projects. Data that the research projects will generate traverses a number of scales from within host to data describing dengue disease at village, district and province scales. Data generated by the research projects also encompasses a number of different fields of scientific inquiry from clinical infectious disease to immunology to virology to entomology. The Core will provide support to individual research projects in the management of data generated. The Data Management and Analysis Core will be led by Dr. Derek Cummings at the University of Florida and include Dr. Rodriguez-Barraquer at the University of California San Francisco. The specific aims of the Data Management and Analysis Core are to provide support to individual projects in each phase of their execution from design to conduct to analysis. Members of the analysis core will regularly communicate with project investigators to develop specific plans for the collection, entry, security, quality control, storage, formatting, and backup of data from each project. The Core has and will continue to provide consultation on the design of studies. Feedback on the success of individual elements and the consistency with assumptions at the design phase will be assessed in an ongoing manner. The Core will assist in analyses in each of the projects individually and facilitate integrative analyses that include data from multiple projects. The Core will also develop and utilize novel methods in four major areas of analysis: 1) studies of transmission dynamics utilizing sequence data 2) spatial and temporal studies of the transmission of dengue using mechanistic models and statistical models that can account for spatial and temporal clustering of observations 3) mechanistic and phenomenological models of within-host processes including immune-dynamics and virus dynamics 4) imputation and data augmentation approaches to expand collected data in order to characterize the uncertainty associated with unobserved aspects of the transmission and infection.