Alessandro Vespignani - US grants

The award supports the "International Workshop and Conference on Network Science," to be held at Indiana University, Bloomington, Indiana, during May 2006. The primary objective of these activities is to facilitate interactions among social and behavioral scientists and the many new disciplines interested in and utilizing network science. The Workshop and Conference will bring together leading researchers and practitioners in network science --- analysts, modeling experts, and visualization specialists --- with graduate students from many different research areas for interdisciplinary communication and collaboration. The Workshop will feature tutorials focusing on a variety of network science research areas. The Workshop will lead into the Conference, which will feature intellectual discourses and conference presentations from the invited graduate students.

For network research to achieve its potential impact on scientific research, students and faculty need to be broadly trained in this area, learning not only the basic measurement and modeling tools but also their meaning and range of applicability. The Workshop will provide cross training in the theory and methods of network science, incorporating the perspectives of diverse disciplines such as sociology, organizational science, information science, mathematics, statistics, informatics, biology, and physics. By bringing together leading researchers and emerging network scholars, the Workshop and the Conference will also promote the development of new mathematical and statistical models with broad application in the social and behavioral sciences. Dissemination products include an easily accessible website containing tutorials and invited and contributed papers. This award was supported as part of the fiscal year 2005 Mathematical Sciences priority area special competition on Mathematical Social and Behavioral Sciences (MSBS).

This project will design, evaluate, and operate a unique distributed, shared resources environment for large-scale network analysis, modeling, and visualization, named NetWorkBench (NWB). The envisioned data-code-computing resources environment will provide a one-stop online portal for researchers, educators, and practitioners interested in the study of biomedical, social and behavioral science, physics, and other networks.

The NWB will support network science research across scientific boundaries. Users of the NWB will have online access to major network datasets or can upload their own networks. They will be able to perform network analysis with the most effective algorithms available. In addition, they will be able to generate, run, and validate network models to advance their understanding of the structure and dynamics of particular networks. NWB will provide advanced visualization tools to interactively explore and understand specific networks, as well as their interaction with other types of networks.

A major computer science challenge is the development of an algorithm integration framework that supports the easy integration and dissemination of existing and new algorithms and can deal with the multitude of network data formats in existence today. Another challenge is the design and implementation of an easy to use menu-based, online portal interface for interactive algorithm selection, data manipulation, user and session management. The NWB will be evaluated in diverse research projects and educational settings in biology, social and behavioral science, and physics research. It will be well documented and available as open source for easy duplication and usage at other sites. An annual summer school and a series of workshops and tutorials are planned to introduce the tool to diverse research communities.

The NWB will provide members of the scientific research community at large (biologists, physicists, computer scientists, social and behavioral scientists, engineers, etc.) with the means to carry out network analysis, modeling, and visualization projects in their own fields. This will result in a direct transfer of knowledge and results from the fields of specialist network research to a wider scientific community. Researchers will have access to validated algorithms that in the past have been obtained through time-consuming personal developments of ad hoc computer programs. The NWB is expected to enhance and encourage the empirical analysis and model validation of networks, generating an eventual acceleration in the development of network science research. Online instructional material will support the use of the NWB in educational settings.

The NWB will provide a unique tool for network science researchers in many disciplines. In effect, NWB can deploy the knowledge accumulated in network theory and practice across sciences with just one web click to any interested researcher, practitioner, or student. The NWB shared resources environment will speed up and ease network science applications and education in biology, social and behavioral science, and large infrastructure analysis, thereby accelerating the rate of scientific discovery.

[unreadable] DESCRIPTION (provided by applicant): 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. [unreadable] [unreadable] [unreadable]

The significant advances realized in recent years in the study of complex networks are severely limited by an almost exclusive focus on the behavior of single networks. However, most networks in the real world are not isolated but are coupled and hence depend upon other networks, which in turn depend upon other networks. Real networks communicate with each other and may exchange information, or, more importantly, may rely upon one another for their proper functioning. A simple but real example is a power station network that depends on a computer network, and the computer network depends on the power network. Our social networks depend on technical networks, which, in turn, are supported by organizational networks. Surprisingly, analyzing complex systems as coupled interdependent networks alters the most basic assumptions that network theory has relied on for single networks. A multidisciplinary, data driven research project will: 1) Study the microscopic processes that rule the dynamics of interdependent networks, with a particular focus on the social component; 2) Define new mathematical models/foundational theories for the analysis of the robustness/resilience and contagion/diffusive dynamics of interdependent networks. This project will afford the opportunity of greatly expanding the understanding of realistic complex networks by joining theoretical analysis of coupled networks with extensive analysis of appropriately chosen large-scale databases. These databases will be made publicly available, except for special cases where it is illegal to do so.

This research has important implications for the understanding the social and technical systems that make up a modern society. A recent US Scientific Congressional Report concludes ?No currently available modeling and simulation tools exist that can adequately address the consequences of disruptions and failures occurring simultaneously in different critical infrastructures that are dynamically inter-dependent.? Understanding the interdependence of networks and its effect on the system robustness and on the structural and functional behavior is crucial for properly modeling many real world systems and applications, from disaster preparedness, to building effective organizations, to comprehending the complexity of the macro economy. In addition to these intellectual objectives, the research project includes the development of an extensive outreach program to the public, especially K-12 students.

The project is aimed at modeling the diffusion of information online and empirically discriminating among models of mechanisms driving the spread of memes. We explore why some ideas cause viral explosions while others are quickly forgotten. Our analysis goes beyond the traditional approach of applied epidemic diffusion processes and focuses on cascade size distributions and popularity time series in order to model the agents and processes driving the online diffusion of information, including: users and their topical interests, competition for user attention, and the chronological age of information. Completion of our project will result in a better understanding of information flow and could assist in elucidating the complex mechanisms that underlie a variety of human dynamics and organizations. The analysis will involve studying meme diffusion in large-scale social media by collecting and analyzing massive streams of public micro-blogging data.

The project stands to benefit both the research community and the public significantly. Our data will be made available via APIs and include information on meme propagation networks, statistical data, and relevant user and content features. The open-source platform we develop will be made publicly available and will be extensible to ever more research areas as a greater preponderance of human activities are replicated online. Additionally, we will create a web service open to the public for monitoring trends, bursts, and suspicious memes. This service could mitigate the diffusion of false and misleading ideas, detect hate speech and subversive propaganda, and assist in the preservation of open debate.

PROJECT SUMMARY In this proposal we plan to contribute addressing the above foundational and operational challenges by advancing the science of influenza modeling and contributing novel methods and data sources that will increase the accuracy and availability of seasonal and pandemic influenza models. To address these challenges, we plan to build on the unique mechanistic spatially structured modeling approaches developed by our consortium, that includes stochastic metapopulation models and fully developed agent-based models nested together in our global epidemic and mobility modeling (GLEAM) approach. The objective of this project is to generate novel and actionable scientific insights from dynamic transmission models of influenza transmission that effectively integrate key socio-demographic indicators of the focus population, as well as a wide spectrum of pharmaceutical and non-pharmaceutical interventions. Our proposed work in specific aim 1 (A1) will leverage our global modeling (from the global to local scale) framework that can be used to explore the multi-year impact of influenza vaccination, antiviral prophylaxis/treatment, and community mitigation during influenza seasons and pandemics. Our specific aim 2 (A2) will focus on using high quality data to model heterogeneous transmission drivers and novel contact pattern stratifications that will allow us to guide mitigation strategies and prioritization for interventions. In our Aim 3 (A3) we will use artificial intelligence approaches to identify interventions that are particularly synergistic and well-suited to particular epidemic scenarios, for seasonal and pandemic influenza. Our overarching goal is to provide a modeling portfolio with flexible and innovative mathematical and computational approaches. We aim to address several questions commonly asked about seasonal and pandemic influenza and match these with analytical methods and outbreak projections. The modeling and data developed in this project can help facilitate and justify transparent public health decisions, while contributing to the definition of standard methods for model selection and validation. Finally, our influenza modeling platform can also benefit the broader network of modeling teams and can be used to improve result sharing and harmonization of modeling approaches.

Mathematical and Statistical Methods for the Control of Global Infectious Disease Threats ABSTRACT Outbreaks of emerging and re-emerging infectious diseases have become more frequent over time and pose a critical threat to human health. Pandemic and seasonal influenza, dengue and other arboviruses continue to spread on a global scale. Other specific infectious disease problems include Ebola, Lassa fever, plague, and Middle East Respiratory Syndrome (MERS-CoV). The consistent and rapid deployment of control measures, especially vaccines and antimicrobials, is crucial for reducing transmission and preventing or mitigating outbreaks caused by these infectious diseases. The goal of this research is to develop, validate, and implement novel mathematical and statistical techniques for modeling the transmission of major infectious disease threats. The resultant models will be applied to assess the impact of various layered control interventions and to guide the optimal allocation of resources for disease mitigation and control. Our specific aims correspond to this research challenge. (Aim 1) Develop innovative methods for mathematical modeling of important infectious disease threats. (Aim 2) To derive a portfolio of innovative statistical methods to improve estimation of key model parameters from surveillance data. (Aim 3) Optimize the use of layered interventions using the mathematical models. Overall, we will develop mathematical models with realistic transmission dynamics that achieve superior computational tractability. We will also derive statistical methods and apply these approaches to specific infectious disease threats. The output of our research will include comprehensive modeling results useful for understanding the transmission and control of the targeted infectious diseases. We hypothesize that the output of our research will provide a comprehensive analytic framework for understanding the transmission and control of the infectious diseases modeled, and to deal with future threats. The contribution of this research is significant because we will provide methods for modeling and analyzing the transmission and control of the significant infectious disease threats. The mathematical models with allow us to understand and predict the infectious disease transmission, and to devise optimal control strategies using vaccines, anti-microbial agents and non-pharmaceutical interventions. The statistical modeling will provide parameter estimation and fitting methods for the mathematical models, while the optimal control strategies devised will provide the decision method for the effective control of the infectious disease threats. This work be integrated into the infectious disease control efforts of the WHO Research and Development Blueprint for Action to Prevent Epidemics and the Emerging Pathogens Institute at the University of Florida. Our team has over 30 years of experience in this work, and it is uniquely positioned to conduct this research. The proposed work is innovative because it challenges existing paradigms on the integration of methods for the mathematical modeling and statistical analysis of infectious disease transmission into a comprehensive framework to determine the most effective combination of control measures for epidemic containment and mitigation.

The COVID-19 pandemic has highlighted the importance of accurate information as a vehicle for helping the public take needed steps to ensure their health and safety. But social media contain both accurate and inaccurate information. This project will analyze how social media affects the quality of information received by people during the extended crisis. Who receives what information? And in what ways do social media amplify or dampen informational inequalities? The project will build a real-time monitor of information consumption regarding the corona virus, drawn largely from Twitter. Specifically, the project will: (1) build a real time monitor of information regarding the corona virus that would be made available to state and local officials; and (2) evaluate how a medium such as Twitter amplifies/dampens existing informational inequalities around socioeconomic status. The project will focus on identification of misinformation (e.g., ersatz cures) that pose health risks. The project will supply aggregate information to relevant state and local officials regarding the type and quality of information regarding corona virus circulating in their communities, thus informing interventions that public officials can make to combat that misinformation. More generally, the project will identify patterns of information that governmental officials can use to combat misinformation during other extended crises, including those with public health as well as other origins.

Responding appropriately to COVID-19 requires that individuals have accurate information about how it is spread and what they can do to mitigate virus effects. However, misinformation is prevalent, with Twitter being a major source of both accurate and inaccurate information. This project will utilize a matched sample of 1.8 million Twitter handles and voter registration data. The large scale of the data will permit production of reasonable inferences of content sharing at subnational levels?at the state level, and within regions for large states. Because the Twitter data will be linked to voter registration records, and because voter registration data includes information on age, gender, race, partisanship, and address, thus allowing linkage to census tract information, the project will be able to evaluate the relationship between socioeconomic status and information exposure. Further, the project will augment with a survey of about 2000 of the matched data to further examine the factors that affect the quality of information people receive about the corona virus. Findings from the project will inform theories in the social sciences regarding information diffusion, socioeconomic inequality, social media usage, the security of cyberspace, and political differentiation.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.