Big Data, Analytics, and Modeling at Pitt Public Health

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Presentation transcript:

Big Data, Analytics, and Modeling at Pitt Public Health Eleanor Feingold Professor, Departments of Human Genetics and Biostatistics Associate Dean for Education Senior Associate Dean September, 2016

Traditional Strengths Classical biostatistical methodology Large clinical trials and population-based epidemiological studies Statistical genetics

Newer Trends Settings that have never used data much before are discovering it. Classical scientific datasets are getting bigger and bigger. People are discovering creative uses for large-scale “full-capture” consumer-type data. Data-driven models are being embraced as policy tools. Example: Collect patient satisfaction data after clinic visits. Collect 20,000 cases and 20,000 controls and sequence their genomes. Predict flu outbreaks by real-time monitoring of google searches for “flu symptoms.” Use FRED to show the effects of different measles vaccination rates.

How do these new trends change what we do as quantitative scientists? Mathematical/statistical methodology not too different (depending on who you ask) - Sometimes new data types - Sophisticated data cleaning needed

How do these new trends change what we do as quantitative scientists? 2. New skill sets need to be brought in - Computational issues in managing the data - Computational issues in processing the data - Visualization (both design and tech expertise)

How do these new trends change what we do as quantitative scientists? 3. Culture change away from inference (testing) and toward discovery (hypothesis generation)

Example: Google flu prediction

Research Highlights Public Health Dynamics Laboratory Department of Biostatistics Genomics Imaging Health record data Social dynamics Geospatial data

Genomics

Big Data and the Opioid Epidemic

Educational Highlights Statistical genetics/genomics curriculum New social dynamics course series Data analysis internships at local agencies and health plans Proposed T32 Proposed health analytics MS degree Proposed genomic bioinformatics MS degree and statistical genetics certificate