Working Paper
Mele A, Obayashi Y, Yang S. Pricing options and futures on a government bond volatility index. Working Paper.
McKeough K, Yang S, Kashyap V, Siemiginowska A, Meng X-L, Campos L, Zezas A. Defining Regions that Contain Complex Astronomical Structures. Working Paper.
In Preparation
Yang S, Wong SWK, Kou SC. Bayesian inference of dynamic systems via constrained Gaussian processes. Proceedings of the National Academy of Sciences. In Preparation.
Kehl KL, Yang S, Awad MM, Palmer N, Kohane IS, Schrag D. Pre-existing autoimmune disease and the risk of immune-related adverse events among real-world patients receiving checkpoint inhibitors for cancer. Cancer Immunology, Immunotherapy. Submitted.
Yang S, Yu K-H, Palmer N, Fox K, Kou SC, Kohane IS. Autoimmune effects of lung cancer immunotherapy revealed by data-driven analysis on a nationwide cohort. BMJ. Submitted.
Ning S, Yang S, Kou SC. Accurate regional influenza epidemics tracking using Internet search data. Scientific Reports. Submitted.
Yang S, Chen Y, Bernton E, Liu JS. On parallelizable Markov chain Monte Carlo algorithms with waste-recycling. Statistics and Computing. 2017 :1–9.
Yang S, Kou SC, Lu F, Brownstein JS, Brooke N, Santillana M. Advances in using Internet searches to track dengue. PLOS Computational Biology. 2017;13 (7) :1-14. Publisher's VersionAbstract
Author summary As communicable diseases spread in our societies, people frequently turn to the Internet to search for medical information. In recent years, multiple research teams have investigated how to utilize Internet users’ search activity to track infectious diseases around our planet. In this article, we show that a methodology, originally developed to track flu in the US, can be extended to improve dengue surveillance in multiple countries/states where dengue has been observed in the last several years. Our result suggests that our methodology performs best in dengue-endemic areas with high number of yearly cases and with sustained seasonal incidence.
Yang S, Santillana M, Brownstein JS, Gray J, Richardson S, Kou SC. Using electronic health records and Internet search information for accurate influenza forecasting. BMC Infectious Diseases. 2017;17 (1) :332. Publisher's VersionAbstract
Accurate influenza activity forecasting helps public health officials prepare and allocate resources for unusual influenza activity. Traditional flu surveillance systems, such as the Centers for Disease Control and Prevention's (CDC) influenza-like illnesses reports, lag behind real-time by one to 2 weeks, whereas information contained in cloud-based electronic health records (EHR) and in Internet users' search activity is typically available in near real-time. We present a method that combines the information from these two data sources with historical flu activity to produce national flu forecasts for the United States up to 4 weeks ahead of the publication of CDC's flu reports.
Obayashi Y, Protter P, Yang S. The lifetime of a financial bubble. Mathematics and Financial Economics. 2017;11 (1) :45–62.
Yang S, Santillana M, Kou SC. Accurate estimation of influenza epidemics using Google search data via ARGO. Proceedings of the National Academy of Sciences. 2015;112 (47) :14473–14478.