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Inference of seasonal and pandemic influenza transmission dynamics

2015; National Academy of Sciences; Volume: 112; Issue: 9 Linguagem: Inglês

10.1073/pnas.1415012112

1091-6490

Wan Yang, Marc Lipsitch, Jeffrey Shaman,

Data-Driven Disease Surveillance

Significance Infectious disease surveillance systems are powerful tools for monitoring and understanding infectious disease dynamics; however, underreporting (due to both unreported and asymptomatic infections) and observation errors in these systems create challenges for delineating a complete picture of infectious disease epidemiology. This issue is true for influenza, an infectious disease of pandemic potential. Here we develop and present influenza inference systems capable of compensating for observational biases and underreporting. Using both Google Flu Trends and Centers for Disease Control and Prevention data in conjunction with Bayesian model inference methods, we are able to infer the evolving epidemiological features of influenza and its impacts among the large population during 2003−2013, including the 2009 pandemic. In addition, differences among regions within the United States are identified.