Inference of seasonal and pandemic influenza transmission dynamics.
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.
Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032; and wy2202@columbia.edu.
Wan
W
Yang
Center for Communicable Disease Dynamics, Harvard School of Public Health, Harvard University, Boston, MA 02115.
Marc
M
Lipsitch
Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032; and.
Jeffrey
J
Shaman
D001499:Bayes Theorem; D062665:Epidemiological Monitoring; D006801:Humans; D009980:Influenza A virus; D007251:Influenza, Human / Q000453:epidemiology* / Q000635:transmission*; D008954:Models, Biological; D058873:Pandemics; D012621:Seasons; D014481:United States / Q000453:epidemiology
asymptomatic infections; data assimilation; influenza; spatial patterns; transmission dynamics