(Alison Galvani, Madhav Marathe leads)
Human behavior can impact public health in myriad ways. Human behavior affects disease transmission, for instance by determining the numbers of contacts an individual has. It also impacts medical decision-making and thus the likelihood of successful implementation of public health policies, such as vaccination recommendations. Traditionally epidemiological modelers have assumed that optimal vaccination coverage will also achieve perfect adherence from the public. However, behavioral modeling has revealed that strategies that are optimal for the population are not necessarily optimal for the individual, depending on the utilities and externalities of specific policies. Consequently, modeling that considers both transmission dynamics and human behavior can facilitate the design of public health strategies that are both effective and cost-effective, as well as likely to achieve a high level of public adherence. The goals of this theme are: (1) to improve the predictability of likelihood that alternative public health policies will be effective and cost-effective; (2) to further our understanding of the factors that motivate individuals in making medical decisions, taking into account the interplay among biological systems, individual decision-making and social and cultural influences; (3) to advance uncertainty quantification for the parameterization of population and multi-scale models from survey and epidemiological data;(4) to achieve and maintain a higher, uniform standard of patient care while reducing cost and patient discomfort.
MSM 2014 Meeting
Agenda – Thursday, September 4th
10:45AM-10:55AM – Madhav Marathe, Ph.D.
10:55AM-11:20AM – Naren Rmakrishnan, Ph.D.
11:20AM-11:45AM - Shweta Bansal, Ph.D.
Presenter Bios and Abstracts
Naren Ramakrishnan, PhD
Thomas L. Phillips Professor of Engineering at Virginia Tech and director of the university's Discovery Analytics Center
Topic of Talk. Data Analytics for Public Health Event Forecasting
Abstract. We describe recent work aimed at forecasting influenza-like illness (ILI) case counts from a multitude of physical and social data sources. Supported by the IARPA Open Source Indicators (OSI) program, our EMBERS system generates weekly forecasts of ILI case counts along with estimates of epicurve characteristics. Key research issues include fusing disparate data streams and models, accounting for uncertainties in initial flu counts, and modeling the distinct dynamics of different flu strains. We cover the selective superiorities of different data sources and lessons learned from ablation studies.
Presenter Bio. Naren Ramakrishnan is the Thomas L. Phillips Professor of Engineering at Virginia Tech and director of the university's Discovery Analytics Center. His research interests include data mining and knowledge discovery for domains such as intelligence analysis, sustainability, forecasting, and health informatics. His work has been supported by NSF, NIH, NEH, DHS, DARPA, IARPA, DTRA, ONR, General Motors, HP Labs, and NEC Labs. He currently leads the multi-university/industry IARPA OSI EMBERS project focused on forecasting significant societal events (disease outbreaks, civil unrest, elections) from open source datasets. He received his PhD in Computer Sciences from Purdue University.
Shweta Bansal, Ph.D.
Assistant Professor, Department of Biology
Topic of Talk. Network Models for Infectious Disease Dynamics
Abstract. The spread of directly transmitted infectious diseases through populations depends fundamentally on the underlying patterns of contacts between individuals. Contact networks provide an individual-level description of these epidemiologically relevant patterns, and can elucidate infectious disease dynamics to answer key public and animal health questions.
Presenter Bio. Shweta Bansal is an Assistant Professor in the Department of Biology at Georgetown University and a Faculty Fellow of the Research and Policy in Infectious Disease Dynamics (RAPIDD) Program at the Fogarty International Center, NIH. She is a mathematical biologist and her research brings mathematical models to challenges in infectious disease ecology, epidemiology and evolution. She focuses on the complex links between host population behavior and pathogen ecology, characterized through network models, and studies how this interaction shapes population-level infectious disease dynamics and evolutionary potential. Bansal is also keen to use this approach to provide a principled and quantitative method to analyze and inform public and animal health policy, and works on systems ranging from influenza in humans to foot and mouth disease in cattle.