RTI International* (RTI) proposes to team with IBM, SAS, and Emory and Duke Universities to serve as the Informatics Group (IG) for the """"""""Pilot Projects for Models of Infectious Disease Agent Study (MIDAS)"""""""" as specified in RFA-GM-03-008. The IG will provide access to a wide array of data sources and models, provide computational and analytic tools, create simulations and develop statistical tests, and enable non-MIDAS users access to models through user-friendly interfaces, publicly available data, and reports. RTI envisions a dynamic, state-of-the-art web-portal with continually evolving models, accruing data sources, and documentation of findings. Informatics and analytic tools will be continually expanding to accommodate new data types and modeling requests. Analytic and data needs will evolve as models are used to test new options in public health response or to model emerging infections. RTI fully appreciates the additional requirements of enhanced computing capabilities, 24 hours a day, 7 days a week monitoring of the web site, and ability to respond rapidly and effectively to health security situations. We propose to host a beta-testing site for other infectious disease models and informatics tools on the portal. We have assembled an IG team with expertise that addresses the scientific and technical requirements. The proposed Principal Investigator Dr. Wagener and Co-Principal Investigators Dr. Cuticchia and Mr. Cooley bring a combined experience that encompasses infectious disease modeling, informatics, genetics, public health, and informatics technology. The IG team will be substantially enhanced with the computing capacity and information technology expertise of IBM and informatics tools and solutions of SAS. Their efforts will be supported by a project manager and an advisory group spanning infectious disease, public health, biomathematics, and informatics expertise. With a large scientific and technical staff of a wide range of expertise to draw upon, RTI has experience using and analyzing data from health, environmental, social, agricultural, and biologic domains. Having over 30 years experience coordinating multisite studies, providing technology innovations and addressing public health issues, RTI is well equipped to assume the role of the IG and to help the National Institute of General Medical Sciences accomplish this expanded mission.
| Bruhn, Mark C; Munoz, Breda; Cajka, James et al. (2012) Synthesized Population Databases: A Geospatial Database of US Poultry Farms. Methods Rep RTI Press MR-0023-1201:1-24 |
| Hwang, Grace M; Mahoney, Paula J; James, John H et al. (2012) A model-based tool to predict the propagation of infectious disease via airports. Travel Med Infect Dis 10:32-42 |
| Cooley, Philip; Brown, Shawn; Cajka, James et al. (2011) The role of subway travel in an influenza epidemic: a New York City simulation. J Urban Health 88:982-95 |
| Chasteen, Bernadette M; Wheaton, William D; Cooley, Philip C et al. (2011) Including the Group Quarters Population in the US Synthesized Population Database. Methods Rep RTI Press 20:1-26 |
| Levine, Burton; Wilcosky, Tim; Wagener, Diane et al. (2010) Mass commuting and influenza vaccination prevalence in new york city: protection in a mixing environment. Epidemics 2:183-8 |
| Cajka, James C; Cooley, Philip C; Wheaton, William D (2010) Attribute Assignment to a Synthetic Population in Support of Agent-Based Disease Modeling. Methods Rep RTI Press 19:1-14 |
| Huang, Susan S; Avery, Taliser R; Song, Yeohan et al. (2010) Quantifying interhospital patient sharing as a mechanism for infectious disease spread. Infect Control Hosp Epidemiol 31:1160-9 |
| Cooley, Philip; Lee, Bruce Y; Brown, Shawn et al. (2010) Protecting health care workers: a pandemic simulation based on Allegheny County. Influenza Other Respir Viruses 4:61-72 |
| Wheaton, William D; Cajka, James C; Chasteen, Bernadette M et al. (2009) Synthesized Population Databases: A US Geospatial Database for Agent-Based Models. Methods Rep RTI Press 2009:905 |
| Chrest, David P; Wheaton, William D (2009) Using Geographic Information Systems to Define and Map Commuting Patterns as Inputs to Agent-Based Models. Methods Rep RTI Press 2009:906 |
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