Faryad Darabi Sahneh, Ph.D. - US grants

This project advances the boundaries of network theory by analyzing spreading processes over multilayer and interconnected networks, which abound in nature and man-made infrastructures, and about which many interesting questions remain unanswered. Multilayer networks are an abstract representation where multiple types of links exist among nodes. Interconnected networks are an abstract representation where two or more simple networks, possibly with different and separate dynamics, are coupled to each other. The rationale for this project is that viral-spreading dynamics over multilayer and interconnected networks exhibit behaviors that cannot be attributed to single-network characteristics and play a highly relevant role in practice. The first part of the project extends the concept of the epidemic threshold value, which determines the conditions for outbreak, to the threshold curve for interconnected and multilayer networks. This research further develops measures for quantification of coupling strength in interconnected networks and seeks optimal interconnection designs for them. The second part of the project aims at predicting competitive spreading over multilayer networks and possible emergent phenomena. This research analyzes transient dynamics and steady-state behavior of multiple-virus competitive spreading in multilayer networks, and investigates competition policy in a game-theoretic framework. This project will use rigorous mathematical tools from network science, spectral graph theory, nonlinear dynamics, stochastic processes, control theory, game theory, and optimization.

Successful completion of this project will greatly advance the state of the art in network theory, with specific, relevant applications in communications and information technologies leading to more efficient and robust design of these complex networked systems. In a broader view, this research will contribute positively to society through a better understanding of how to prevent large-scale catastrophes, including cascading failures in power grids, financial contagions in market trading, infectious disease pandemics, and outbreaks of computer malware. Furthermore, the investigators will put forth significant effort to involve students from under-represented groups, and disseminate project outcomes in both general society and academia through publications, webinars, and public webpages.

The current outbreak of Ebola is the largest thus far, with more than 13,000 reported cases to date in West Africa. Secondary infections have also been reported in Spain and the United States, raising concerns about training of medical personnel and safety of the entire population. In an effort to stop the transmission of the virus within the USA during its very early stage, the Center for Disease Control and Prevention is adopting a "contact tracing" approach ? finding all individuals who have had close contact with an Ebola patient and monitoring the health status of those people for 21 days. This approach requires careful data collection, and is labor and cost intensive. A quantitative measure to evaluate the effectiveness of contact tracing is currently missing, due to the lack of previous experience of Ebola in the USA and insufficient supporting data from current cases. The goal of this project is to evaluate risk detection capabilities of contact tracing efforts for Ebola before the epidemic phase, and estimate the associated cost in potential scenarios. Not only will understanding the effectiveness of contact tracing be important for the current Ebola epidemic, but this project will also provide information for developing contact tracing guidelines and identifying critical circumstances hampering effective contact tracing in possible future epidemic threats.

This project will develop a network-based stochastic modeling framework of Ebola transmission for the local contact network of infected individuals (household, workplace, hospital, airplane, etc.). This simulation framework will allow investigators to synthesize scenarios and activities compatible with daily news about Ebola. "Missed- detection probability" versus "contact tracing cost" will be estimated through extensive simulations. Missed-detection probability, in this case, denotes the probability that a secondary infected individual is not detected before transmitting the infection to others. The team will perform sensitivity analysis to account for inherent uncertainties in different scenarios. The in-silico analysis will allow the following: 1) test performance and associated cost of contact tracing efforts in multiple realistic scenarios and different parameter spaces, 2) propose contact tracing guidelines under limited resources, and 3) identify critical circumstances for which contact tracing is not fully effective. A successful implementation of this project will have immediate benefits to USA public health and security against infectious disease.

Scientists in diverse domains from astronomy and atmospheric sciences, to earth sciences and genomics are generating massive datasets at an unprecedented scale. Rapidly evolving computational and data management technologies for harnessing value from these datasets are providing the foundation for a vibrant ecosystem by establishing robust collaborations and building communities of domain scientists, data scientists, and engineers. These collaborations are central for transforming these datasets into information and knowledge. Barriers of both a technical and non-technical nature can hamper productivity for such transdisciplinary teams and collaborations, especially when highly productive teams with diverse expertise and computational backgrounds work on common problems. These barriers are often associated with frictions at the boundaries of computational technologies and human communications, especially when working at scale. Overcoming such challenges is critical for ensuring successful outcomes.

This project will bring together participants representing thought-leaders and practitioners in data-driven open science projects, TRIPODS+X project teams, and participants from the astronomy and earth sciences communities through two Innovation Labs. The first Lab will introduce participants to the national NSF-funded cyberinfrastructure and commercial cloud infrastructure, providing the opportunity to evaluate and learn from exemplary projects that have utilized these platforms for their collaborations, allowing participants to explore how their communities can extend these platforms for their data science projects in a reliable, scalable and reproducible manner. The second Innovation Lab will establish an early prototype TRIPODS Commons, a cohesive platform for showcasing, experimenting with, and sharing research products (code, data, methods), eventually becoming an avenue that provides visibility to the vibrancy and productivity of projects occurring at all NSF TRIPODS Institutes. Through these Innovation labs, the project will provide pragmatic approaches and pathways for establishing successful transdisciplinary collaborations that enable teams to work across domains and institutional boundaries, and at scales essential for addressing the research, education, and advanced cyberinfrastructure needs as outlined in NSF's 10 Big Ideas.

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.

This project will develop and deploy a data-driven mathematical modeling framework for predicting the spread of COVID-19 at regional levels and for informing potential mitigation efforts. The models will also provide a means to test the impact of social distancing and mobility reduction on the future course of the pandemic. The proposed modeling framework relies on a two-component structure that does not require prior knowledge of the epidemiological characteristics of the disease. This approach is especially useful during the initial stages of an emerging outbreak, where little is known and validated about the contagion. Moreover, this project will bring a novel perspective on the mathematical modeling of disease spread, which will complement other ongoing efforts and provide access to diverse models critical to decision-making under uncertainty.

This project builds upon a data-driven mathematical modeling approach leveraging a surprisingly simple behavior examined in epidemiological data sets and models that allows forecasts for case counts with no parameter estimations. The first thrust is to integrate data-driven modeling into explicit network interactions in order to investigate spatial aspects of COVID-19 outbreak propagation. The second thrust of the project is to implement a calibration layer that takes into account mitigation efforts. The rationale for this approach is that, in a constantly evolving environment, epidemiological predictions are difficult to make due to the performativity effect, whereby model predictions affect social behavior and mitigation efforts, which in turn alters the spread of the outbreak predicted by the mathematical models. From a conceptual point of view, this project will address performativity in the context of epidemiological modeling. At the practical level, it will develop a general calibration module that will learn how to incorporate reactions to predictions into epidemiological forecasts. By design, this ?performativity-aware? calibration module will be independent of any specific epidemic model; hence, once developed, it will be possible to be integrated into other existing predictive models.

This award is being funded by the CARES supplemental funds allocated to MPS.

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.