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New Post-doctoral Associate Joins the PHDL

Angel Paternina, MD, MScThe Public Health Dynamics Laboratory is pleased to introduce Angel Paternina, MD, MSc, who has joined the department of Health Policy and Management as a post-doctoral associate. Dr. Paternina will work to develop a research program on the acquisition, integration and analysis of public health data to expand Project Tycho into a global, open access resource. His work will also include development of new analytical methods to visualize large scale disease data to detect patterns of associations between disease transmission and climate/demographic determinants.

Dr. Paternina earned his MD degree from the University of Cartagena, Colombia, and his MSc in Clinical Epidemiology from the National University of Colombia. He started his global health work in his native Colombia by studying the impact of rotavirus vaccination on child disease, reporting the effectiveness, impact and cost-effectiveness of the rotavirus vaccine to prevent rotavirus diarrheal disease and deaths in Colombia, Latin America and low and middle income countries worldwide. Since then, he has focused his research on the impact of different interventions in children and special populations, assessing in Colombia the cost-effectiveness of the varicella vaccine in children, HAART in HIV/AIDS population, mass pneumococcal vaccination in the elderly population, and the burden of H1N1 in pregnant women in Colombia during the pandemic. Currently, Dr. Paternina is an expert collaborator for the Global Burden of Disease study with the Institute for Health Metrics and Evaluation at the University of Washington, and is working with researchers from Latin America to identify the severity profile of some vector-borne diseases in Colombian children, including dengue and chikungunya.

NIH View of Data Science and BD2K

On September 12, 2016, Dr. Michelle Dunn from the NIH will be the distinguished speaker at the first lecture of the 2016-2017 PHDL Seminar Series. The NIH launched the Big Data to Knowledge (BD2K) initiative in 2012 to address the challenges of maximizing the use of biomedical research data. Dr. Dunn will give a retrospective of the development of data science at the NIH over the last few years, describing how it has evolved along with and in response to the development of data science in the broader scientific community. She will describe a major trans-NIH program, the Big Data to Knowledge Initiative, led by the NIH Associate Director for Data Science (ADDS), as well as the additional efforts of the ADDS office towards enabling the efficient management of biomedical Big Data. 

Michelle Dunn, PhDDr. Dunn is senior advisor for Data Science Training, Diversity and Outreach at the Office of the Associate Director for Data Science at the NIH. She leads the data science training efforts, advising on training, education and workforce development in biomedical data science. Prior to joining the NIH/OD, she was a program director at the National Cancer Institute. She received her Ph.D. in statistics from Carnegie Mellon University and her A.B. in applied mathematics from Harvard College. 

The seminar is open to the public and Grand Rounds approved.


2016 MIDAS Network Meeting

midas network meeting 2016

The University of Pittsburgh MIDAS Center for Excellence participated in the May 23-24, 2016 MIDAS Network Meeting in Reston, Virginia with four presentations and four posters. MIDAS investigators representing the Pitt Center of Excellence were: Donald Burke, PI, Mark Roberts, John Grefenstette, Wilbert van Panhuis, Derek Cummings (University of Florida), Logan Brooks and Roni Rosenfeld (Carnegie Mellon University), Jeanine Buchanich, and Hasan Guclu.

ages and stages poster

MIDAS uses ABM to examine if population structure is sufficient to generate area-level inequalities in influenza rates

RR adults

In New Haven County (NHC), CT, influenza hospitalization rates have been shown to increase with census tract poverty in multiple influenza seasons. In what is, to our knowledge, the first use of simulation models to examine the causes of differential poverty-related influenza rates, researchers in the Public Health Dynamics Laboratory used agent-based models with a census-informed, realistic representation of household size, age-structure, population density in NHC census tracts, and contact rates in workplaces, schools, households, and neighborhoods, and measured poverty-related differential influenza attack rates over the course of an epidemic. Simulated attack rates among adults increased with census tract poverty level. The study detected a steeper, earlier influenza rate increase in high-poverty census tracts, a finding that was corroborated with a temporal analysis of NHC surveillance data during the 2009 H1N1 pandemic. The ratio of the simulated adult AR in the highest- to lowest-poverty tracts was 33% of the ratio observed in surveillance data, leaving 67% of the inequality to be explained by other factors. Future models should quantify the capacity of individual behavioral and biological factors to generate influenza inequalities thus allowing us to prioritize interventions aimed at factors with the greatest explanatory power.

Read the paper at: http://www.biomedcentral.com/1471-2458/15/947

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