Yang Wan, Lipsitch Marc, Shaman Jeffrey
Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032; and
Center for Communicable Disease Dynamics, Harvard School of Public Health, Harvard University, Boston, MA 02115.
Proc Natl Acad Sci U S A. 2015 Mar 3;112(9):2723-8. doi: 10.1073/pnas.1415012112. Epub 2015 Feb 17.
The inference of key infectious disease epidemiological parameters is critical for characterizing disease spread and devising prevention and containment measures. The recent emergence of surveillance records mined from big data such as health-related online queries and social media, as well as model inference methods, permits the development of new methodologies for more comprehensive estimation of these parameters. We use such data in conjunction with Bayesian inference methods to study the transmission dynamics of influenza. We simultaneously estimate key epidemiological parameters, including population susceptibility, the basic reproductive number, attack rate, and infectious period, for 115 cities during the 2003-2004 through 2012-2013 seasons, including the 2009 pandemic. These estimates discriminate key differences in the epidemiological characteristics of these outbreaks across 10 y, as well as spatial variations of influenza transmission dynamics among subpopulations in the United States. In addition, the inference methods appear to compensate for observational biases and underreporting inherent in the surveillance data.
关键传染病流行病学参数的推断对于描述疾病传播以及制定预防和控制措施至关重要。近期，从诸如健康相关在线查询和社交媒体等大数据中挖掘出的监测记录以及模型推断方法的出现，使得开发用于更全面估计这些参数的新方法成为可能。我们将此类数据与贝叶斯推断方法相结合，以研究流感的传播动态。我们同时估计了2003 - 2004年至2012 - 2013年季节（包括2009年大流行）期间115个城市的关键流行病学参数，包括人群易感性、基本再生数、发病率和传染期。这些估计区分了这10年间这些疫情在流行病学特征上的关键差异，以及美国亚人群中流感传播动态的空间变化。此外，推断方法似乎弥补了监测数据中固有的观察偏差和报告不足。
