The study of epidemic and social contagion processes is crucial for the understanding, prediction, and prevention of many phenomena affecting public health, such as infectious disease, alcohol use, and smoking habits. Improving epidemic and social contagion research is therefore important to the goals of the NIDA, NIAID and NIGMS institutes. Research progress in this area is difficult because the datasets and processes unfold over multiple temporal and spatial scales, requiring applied mathematical and computational approaches that can cope with non-linear complex phenomena. This requires an integrated research approach where the many layers - from the single individual to the global society - are analyzed at once. Such an approach calls for qualitatively new technology that supports the easy exchange, combination, and application of data analysis capabilities, methodologies, and visualization tools developed in very different areas of research. This project proposes the design, implementation, deployment, and maintenance of a computational infrastructure for epidemic research called the Epidemics Cyber infrastructure (EpiC). EpiC is a qualitatively new type of cyber infrastructure -- an """"""""empty shell"""""""" that supports the easy plug-and-play of datasets, algorithms, and visualization components in customized EpiC Tools. The proposed EpiC infrastructure is also unique in the utilization of a """"""""scholarly marketplace"""""""" for sharing commonly used datasets, algorithms, and visualization components - the """"""""fillings"""""""" of the EpiC Tools. The marketplace might be best compared with popular file and content sharing community sites like Flickr (http://flickr.com/), YouTube (http://youtube.com/), or Wikipedia (http://wikipedia.org/). However, instead of sharing images, movies, or encyclopedia entries, scholars will use EpiC to share datasets, algorithms, and any other items relevant to the study of epidemics. The overarching goals of EpiC are the improvement and facilitation of multi-scale analysis of social data integrated into dynamic systems modeling, agent-based modeling, and other simulation techniques for epidemic processes; the direct transfer of knowledge and results from fields of specialist research to the wider interdisciplinary scientific community; and the development of a cyber infrastructure technology that is open, usable, extensible, and sustainable. This project proposes the design, implementation, deployment, and maintenance of a computational infrastructure for epidemic research called Epidemics Cyber infrastructure (EpiC) for the improvement and facilitation of the multi-scale analysis of social data and their integration in systems dynamic modeling, agent-based modeling, and other simulation techniques for epidemic processes. EpiC is a qualitatively new type of cyber infrastructure -- an """"""""empty shell"""""""" that supports the easy plug-and-play of datasets, algorithms, and visualization components in customized EpiC Tools. The proposed EpiC infrastructure is also unique in the utilization of a """"""""scholarly marketplace"""""""" to share commonly used datasets, algorithms, and visualization components - the """"""""fillings"""""""" of the EpiC Tools. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute on Drug Abuse (NIDA)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21DA024259-01
Application #
7363874
Study Section
Special Emphasis Panel (ZDA1-GXM-A (27))
Program Officer
Onken, Lisa
Project Start
2007-09-26
Project End
2011-07-31
Budget Start
2007-09-26
Budget End
2008-07-31
Support Year
1
Fiscal Year
2007
Total Cost
$298,440
Indirect Cost
Name
Indiana University Bloomington
Department
Type
Other Domestic Higher Education
DUNS #
006046700
City
Bloomington
State
IN
Country
United States
Zip Code
47401
Fumanelli, Laura; Ajelli, Marco; Manfredi, Piero et al. (2012) Inferring the structure of social contacts from demographic data in the analysis of infectious diseases spread. PLoS Comput Biol 8:e1002673
Tizzoni, Michele; Bajardi, Paolo; Poletto, Chiara et al. (2012) Real-time numerical forecast of global epidemic spreading: case study of 2009 A/H1N1pdm. BMC Med 10:165
Balcan, Duygu; Vespignani, Alessandro (2012) Invasion threshold in structured populations with recurrent mobility patterns. J Theor Biol 293:87-100
Singer, Andrew C; Colizza, Vittoria; Schmitt, Heike et al. (2011) Assessing the ecotoxicologic hazards of a pandemic influenza medical response. Environ Health Perspect 119:1084-90
Bajardi, Paolo; Poletto, Chiara; Ramasco, Jose J et al. (2011) Human mobility networks, travel restrictions, and the global spread of 2009 H1N1 pandemic. PLoS One 6:e16591
Balcan, Duygu; Vespignani, Alessandro (2011) Phase transitions in contagion processes mediated by recurrent mobility patterns. Nat Phys 7:581-586
Perra, Nicola; Balcan, Duygu; Gonçalves, Bruno et al. (2011) Towards a characterization of behavior-disease models. PLoS One 6:e23084
Meloni, Sandro; Perra, Nicola; Arenas, Alex et al. (2011) Modeling human mobility responses to the large-scale spreading of infectious diseases. Sci Rep 1:62
Van den Broeck, Wouter; Gioannini, Corrado; Goncalves, Bruno et al. (2011) The GLEaMviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale. BMC Infect Dis 11:37
Balcan, Duygu; Gonçalves, Bruno; Hu, Hao et al. (2010) Modeling the spatial spread of infectious diseases: the GLobal Epidemic and Mobility computational model. J Comput Sci 1:132-145

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