The Public Health Dynamics Laboratory (PHDL) at the graduate school of Public Health at the University of Pittsburgh is a mathematical modeling group that uses computational methods to represent diseases and potential interventions. Initially funded through the Modeling Infectious Disease Agents Study (MIDAS), the PHDL has developed expertise in large scale, geospatially accurate agent-based modeling, primarily with a concentration on infectious diseases. These models require significant amounts of data, and we have championed he use of “synthetic” populations that statistically represent the real populations of interest. This talk will describe the ongoing modeling efforts at PHDL, our development of dynamic synthetic populations that age, marry, divorce and create social networks, and acquire non-infectious diseases, and describe our aspirational goals of the integration of individual data with complex, mechanistically-based models.
Many valuable datasets that could be used to counter epidemic threats are not used due to challenges in accessing and standardizing datasets, and in integrating data into novel analyses such as epidemic simulation. Our research aims to improve the acquisition, standardization, and integration of information about epidemic threats.
Internet support groups (ISGs) that enable individuals with similar conditions to assess and exchange self-help information and emotional support have proliferated in recent years. However, their benefit has not been established as randomized trials.
Microbial communities exist throughout the biosphere including the associations they form with humans, plants and animals. Understanding the diversity and genetic complexity of these communities, along with the interactions they undertake both within communities and with their environment has given rise to the concept of the study of the microbiome. With improvements in molecular biology, computational power, and high throughput technologies such as the advent of next generation sequencing, new opportunities exist to study the microbiome from multiple environments including their role in human health and disease. However, in order to maximize the information we can realize from these data types, careful considerations need to be made in terms of study design, and new tools and approaches are needed for data generation, analysis and interpretation. This talk will provide an overview of these topics using examples from microbiome studies.
In this talk, we will discuss some of the recent research on improving the performance of personalized recommender systems by using knowledge graphs (KG) to uncover the long range preferences of users.
Cancer is mainly caused by heterogeneous somatic genome alterations (SGAs). Genome-scale data from individual patients are now readily available, and it is anticipated that precisely targeting specific genomic alterations of individual tumors will bring in more effective therapies. However, there are 3 major gaps that hinder the translation of the genome data of a tumor to personalized therapy.
Adverse drug reactions (ADRs) are dangerous and expensive. Idiosyncratic ADRs, especially rare and severe hypersensitivity-driven ADRs, are the leading cause of medicine withdrawal and termination of clinical development.
The Cancer Genome Atlas project has generated a daunting amount of genomic and deep sequencing data for tens of thousands of human tumors. This provided unprecedented opportunities to systematically analyze the cancer genomes to discover driving genetic alternations and develop novel therapeutics. In the past a few years, we have developed the computational approaches that interrogates multiple levels of genomic data to reveal cancer-causal genes and therapeutic targets.
Posada: According to the substance abuse and mental health services administration (SAMHSA) 43.1 million of adults (18.1%) in the US experienced some form of mental illness. Hospitalization for these of diseases is increasing at a faster rate than any other type of hospitalization. From the psychiatric hospitalized patients, 15% of all discharges were readmitted within 30 days and the costs among those readmissions are higher than for any other readmission cause. Moreover, CMS releases individual hospital readmission rates to the public as an indicator of quality of care.