DBMI’s has a long-standing history in population informatics efforts, including nearly 20 years of experience in infectious disease modeling and developing methods for real-time detection and assessment of disease outbreaks. Historically, the RODS biosurveillance laboratory is home to three large projects that work with health departments to create surveillance systems: the RODS Open Source Project, the Pennsylvania RODS, and the National Retail Data Monitor (NRDM). These projects benefit the public and also benefit the research by grounding our work in actual public health practice and by collecting surveillance data for algorithm validation and investigations into the value of different types of novel data for outbreak detection.
Examples of projects include algorithm development, assessment of novel types of surveillance data, grid computing, natural language processing, and analyses of detectability. DBMI’s responses to the COVID-19 pandemic include development of (1) a Bayesian outbreak detection and characterization system capable of modeling outbreaks of novel diseases; (2) transfer learning approaches for applying
outbreaks models trained in one geographic region to predict outbreaks in other; (3) Bayesian networks to identify likely cases of COVID-19; and (4) COVID-19 infection fatality rate estimates based on death counts.
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