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Disease Modelling and Public Health

HOST-A-BSaumyadipta Pyne, scientific director of the Public Health Dynamics Laboratory of Pitt Public Health, and co-editors Arni Srinivasa Rao and C.R. Rao have recently published a 2-volume title, 'Disease Modelling and Public Health', as part of the highly regarded Handbook of Statistics series from Elsevier.

Their title addresses new challenges in existing and emerging diseases over 30 comprehensive chapters covering a variety of topics including mathematical modelling of mass screening and parameter estimation, agent-based models for infectious disease transmission, reaction diffusion equations and their application to bacterial communication, Bayesian disease mapping, real time estimation of the case fatality ratio and risk factor of death, dynamic risk prediction for cardiovascular diseases, methodological advances such as spike and slab methods in disease modelling and finite mixture models, and models of individual and collective behaviour for Public Health Epidemiology.

It covers the lack of availability of complete data relating to disease symptoms and disease epidemiology, one of the biggest challenges facing vaccine developers, public health planners, epidemiologists and health sector researchers. The editors believe that it will serve as a vital resource for statisticians who need to access a number of different methods for assessing epidemic spread in population or in formulating public health policy.

Pitt Public Health Faculty Presents PHDL Resources at APHA

Studies and surveys have shown that using information technology to analyze big health datasets and guide publicElizabeth Van Nostrand health decisions can improve health equity, but the majority of community health center leaders and staff report receiving little to no training in health informatics. At the November 2018 Annual American Public Health Association Meeting and Expo in Atlanta, GA, Elizabeth Van Nostrand, JD, associate director for law and policy in Pitt Public Health’s Center for Public Health Practice, shared a training protocol designed to remedy this gap and be replicated nationwide. “There is so much information collected by community health centers, health departments, hospitals and other public health services – ranging from vaccination records to blood pressure screenings – that could give us insights about the public health needs of a community.” Van Nostrand, a PHDL faculty member, directs the Mid-Atlantic Regional Public Health Training Center and explained the goal is not to tell a center that this is the best informatics tool for them but to help them recognize their needs and learn how informatics can serve them.

Van Nostrand shared four free, open access public health informatics tools developed at Pitt Public Health. Two of these tools were developed at the Pitt Public Health Dynamics Laboratory. The first tool, FRED (A Framework for Reconstructing Epidemiological Dynamics), aids the public health workforce in preparing for and responding to disasters. The second tool, Project Tycho, is a repository of data from all weekly disease reports for the US dating back to 1888 and provides surveillance data for the advancement of science and technology through research applications.

FRED Presented at 2017 Pitt Innovation Showcase

On October 18, 2017, the Public Health Dynamics Laboratory at the University of Pittsburgh Graduate School of Public Health was honored as a presenter of their modeling platform, FRED, at the 2017 PittFRED Logo new Innovation Showcase. FRED (A Framework for Reconstructing Epidemiological Dynamics), is a customizable modeling platform that supports decision making and forecasting based on the dynamic interactions of humans in their daily social interactions. To learn more about FRED, click here.

PHDL Scientists Featured in The Economist

The statistics are staggering, and it is hard to overstate the scale of use and abuse of drugs in the U.S. A model developed by Dr. Graph showing the increase in opioid deaths.Hawre Jalal, faculty researcher at the Public Health Dynamics Laboratory (PHDL) at the University of Pittsburgh Graduate School of Public Health, demonstrates that the typical overdose victim is becoming younger and more urban, as reported by The Economist on October 26, 2017 in The shifting toll of America’s drug epidemic. The results (“Sub-epidemics within the Opioid Epidemic”, H. Jalal, J. Buchanich, L. Balmert, M. Roberts and D. Burke) presented by Dr. Jalal at the October 2017 Annual Society for Decision Making (SMDM) Meeting in Pittsburgh, PA, shows red alerts for U.S. drug overdose deaths per 100,000 population by age, demographics and drug type. The highest rates of prescription-opioid abuse can be found among midde-aged rural whites, including women. By contrast, both fentanyl and heroin users tend to be much younger, more likely to live in cities, somewhat more racially diverse and overwhelmingly male.

In another article, Forecasting the opioid epidemic, on October 28, 2017, The Economist raised the questions: When will the opioid epidemic peak? And how many will it kill? Dr. Donald Burke, dean of the Graduate School of Public Health, University of Pittsburgh, points out that the number of fatal drug overdoses has doubled every eight years for the past 37. Epidemiologists are frantically scrambling to go beyond simple best-guess estimates to dynamic models that can forecast addiction andGraph showing projected deaths from opioids. overdoses more accurately. Dr. Jalal and other scientists from the PHDL at the University of Pittsburgh are developing a dynamic transmission disease model of the opioid epidemic, matching data in the national drug-use survey to outcomes in mortality. These results (“A dynamic transmission disease model of the opioid epidemic”, D. Sinclair, H. Jalal, M. Roberts, D. Burke) presented by Dr. David Sinclair, PHDL post-doctoral researcher, at the 2017 Annual SMDM Meeting. predict that prescription opioid deaths will rise slowly to about 20,000 a year within the next five years, but heroin and fentanyl deaths will increase markedly to 72,000 per year by 2025.

PHDL Participates in SMDM Annual Meeting

PHDL-Table-SMDM-Oct2017-image2

The 39th Annual North American Meeting of the Society for Medical Decision Making (SMDM) was held in Pittsburgh, PA, October 22-25, 2017. The focus this year was on “Better Decisions Through Better Data Processes”. The Society for Medical Decision Making has a long history of developing methodologies which take advantage of complex data structures to enhance medical decision making and advance policy formation. The 2017 Annual Meeting explored themes to ensure the credibility and usability of the Society’s efforts and to promote their vision of an integrated approach to health care decision making, through wise use and thoughtful communication of data.

Several Public Health Dynamics Laboratory (PHDL) faculty and postdocs participated in teaching short courses, and oral and poster presentations. The PHDL also participated in an exhibitor’s booth to share information on many of the PHDL software tools, including a demo of FRED (Framework for Reconstructing Epidemiological Dynamics).

 David Sinclair SMDM 2017Hawre Jalal SMSM 2017

Click here to see our presenters

New PHDL Scientific Director

PyneSaumyadipta Pyne, PhD, joined the Public Health Dynamics Laboratory on October 1, 2017, as scientific director and will also serve as an associate professor in the Department of Biostatistics, both at the University of Pittsburgh Graduate School of Public Health.

Pyne was previously a professor at the Indian Institute of Public Health, Hyderabad. He formerly held the prestigious post of P.C. Mahalanobis Chair and professor and head of bioinformatics in the C.R. Rao Advanced Institute of Mathematics, Statistics and Computer Science. He is a former senior research fellow of the Indian Statistical Institute. Pyne is the founding chairman of the Computer Society of India’s Special Interest Group on Big Data Analytics and leads the Health Analytics Network.

Pyne received his doctorate from the State University of New York at Stony Brook, working simultaneously in the departments of computer science, and molecular genetics and microbiology. He conducted his postdoctoral research at Broad Institute of MIT and Harvard then worked at the Dana-Farber Cancer Institute, Harvard Medical School. His research interests include big data in life sciences and health informatics, computational statistics, and high-dimensional data modeling. He is actively engaged in promoting big data research and training activities.

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