Scholarly Works, Sanghani Center for Artificial Intelligence and Data Analytics

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  • Data analysis and modeling pipelines for controlled networked social science experiments
    Cedeno-Mieles, Vanessa; Hu, Zhihao; Ren, Yihui; Deng, Xinwei; Contractor, Noshir; Ekanayake, Saliya; Epstein, Joshua M.; Goode, Brian J.; Korkmaz, Gizem; Kuhlman, Christopher J.; Machi, Dustin; Macy, Michael; Marathe, Madhav V.; Ramakrishnan, Naren; Saraf, Parang; Self, Nathan (PLOS, 2020-11-24)
    There is large interest in networked social science experiments for understanding human behavior at-scale. Significant effort is required to perform data analytics on experimental outputs and for computational modeling of custom experiments. Moreover, experiments and modeling are often performed in a cycle, enabling iterative experimental refinement and data modeling to uncover interesting insights and to generate/refute hypotheses about social behaviors. The current practice for social analysts is to develop tailor-made computer programs and analytical scripts for experiments and modeling. This often leads to inefficiencies and duplication of effort. In this work, we propose a pipeline framework to take a significant step towards overcoming these challenges. Our contribution is to describe the design and implementation of a software system to automate many of the steps involved in analyzing social science experimental data, building models to capture the behavior of human subjects, and providing data to test hypotheses. The proposed pipeline framework consists of formal models, formal algorithms, and theoretical models as the basis for the design and implementation. We propose a formal data model, such that if an experiment can be described in terms of this model, then our pipeline software can be used to analyze data efficiently. The merits of the proposed pipeline framework is elaborated by several case studies of networked social science experiments.
  • Collaborative efforts to forecast seasonal influenza in the United States, 2015–2016
    McGowan, Craig J.; Biggerstaff, Matthew; Johansson, Michael; Apfeldorf, Karyn M.; Ben-Nun, Michal; Brooks, Logan; Convertino, Matteo; Erraguntla, Madhav; Farrow, David C.; Freeze, John; Ghosh, Saurav; Hyun, Sangwon; Kandula, Sasikiran; Lega, Joceline; Liu, Yang; Michaud, Nicholas; Morita, Haruka; Niemi, Jarad; Ramakrishnan, Naren; Ray, Evan L.; Reich, Nicholas G.; Riley, Pete; Shaman, Jeffrey; Tibshirani, Ryan; Vespignani, Alessandro; Zhang, Qian; Reed, Carrie; Rosenfeld, Roni; Ulloa, Nehemias; Will, Katie; Turtle, James; Bacon, David; Riley, Steven; Yang, Wan; The Influenza Forecasting Working Group (Nature Publishing Group, 2019-01-24)
    Since 2013, the Centers for Disease Control and Prevention (CDC) has hosted an annual influenza season forecasting challenge. The 2015–2016 challenge consisted of weekly probabilistic forecasts of multiple targets, including fourteen models submitted by eleven teams. Forecast skill was evaluated using a modified logarithmic score. We averaged submitted forecasts into a mean ensemble model and compared them against predictions based on historical trends. Forecast skill was highest for seasonal peak intensity and short-term forecasts, while forecast skill for timing of season onset and peak week was generally low. Higher forecast skill was associated with team participation in previous influenza forecasting challenges and utilization of ensemble forecasting techniques. The mean ensemble consistently performed well and outperformed historical trend predictions. CDC and contributing teams will continue to advance influenza forecasting and work to improve the accuracy and reliability of forecasts to facilitate increased incorporation into public health response efforts. © 2019, The Author(s).
  • What to know before forecasting the flu
    Chakraborty, Prithwish; Lewis, Bryan L.; Eubank, Stephen; Brownstein, John S.; Marathe, Madhav V.; Ramakrishnan, Naren (PLOS, 2018-10-12)
    Accurate and timely influenza (flu) forecasting has gained significant traction in recent times. If done well, such forecasting can aid in deploying effective public health measures. Unlike other statistical or machine learning problems, however, flu forecasting brings unique challenges and considerations stemming from the nature of the surveillance apparatus and the end utility of forecasts. This article presents a set of considerations for flu forecasters to take into account prior to applying forecasting algorithms.