The Snowball Tookit

Duke RDS2: Respondent-Driven Sampling for Respiratory Disease Surveillance was funded by the Centers for Disease Control and Prevention (CDC) to develop tools for rapid deployment in response to infectious disease outbreaks.

The project supported the development of The Snowball Toolkit, which consists of four components: (1) the Snowball Technology Platform that was built to support the study; (2) the Standard Operating Procedures for safe and effective sample collection for SARS-CoV-2; (3) the methods for the social mixing analyses utilized by the study; and (4) a predictive model developed with biometric data. The final Snowball Toolkit has been submitted to the sponsor, CDC, and is now publicly available.

The Duke Crucible developed the Snowball Platform, a rapidly-deployable, scalable Platform for respondent-driven sampling and data collection during infectious disease outbreaks. The code for the API and UI are publicly available (more information and links to GitHub repository).

Snowball Study Social Mixing Analysis — R code and example files for the social mixing analyses are available here:

The algorithm for biometric monitoring to identify signs of early infection using wearable devices was developed by the Big Ideas Lab at Duke and published in npj Digital Medicine.

The project described was supported by Grant/Cooperative Agreement Number 75D30120C09551 made to Duke University from the Centers for Disease Control and Prevention (CDC), US Department of Health and Human Services (HHS). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of CDC or US HHS. Dana K Pasquale (PI) was also supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), NIH, Grant Award 1R25HD079352-01A1.