title: Inference and forecast of H7N9 influenza in China, 2013 to 2015.
abstract: The recent emergence of A(H7N9) avian influenza poses a significant challenge to public health in China and around the world; however, understanding of the transmission dynamics and progression of influenza A(H7N9) infection in domestic poultry, as well as spillover transmission to humans, remains limited. Here, we develop a mathematical model-Bayesian inference system which combines a simple epidemic model and data assimilation method, and use it in conjunction with data on observed human influenza A(H7N9) cases from 19 February 2013 to 19 September 2015 to estimate key epidemiological parameters and to forecast infection in both poultry and humans. Our findings indicate a high outbreak attack rate of 33% among poultry but a low rate of chicken-to-human spillover transmission. In addition, we generated accurate forecasts of the peak timing and magnitude of human influenza A(H7N9) cases. This work demonstrates that transmission dynamics within an avian reservoir can be estimated and that real-time forecast of spillover avian influenza in humans is possible.
authors.affiliation: State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China.|Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, United States.
authors.forename: Ruiyun
authors.initials: R
authors.lastname: Li
authors.affiliation: Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing, China.
authors.forename: Yuqi
authors.initials: Y
authors.lastname: Bai
authors.affiliation: Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, United States.
authors.forename: Alex
authors.initials: A
authors.lastname: Heaney
authors.affiliation: Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, United States.
authors.forename: Sasikiran
authors.initials: S
authors.lastname: Kandula
authors.affiliation: Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Cai
authors.affiliation: Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing, China.
authors.forename: Xuyi
authors.initials: X
authors.lastname: Zhao
authors.affiliation: State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China.|Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing, China.
authors.forename: Bing
authors.initials: B
authors.lastname: Xu
authors.affiliation: Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, United States.
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Shaman
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D001717:Birds; D002681:China / Q000453:epidemiology; D004196:Disease Outbreaks; D005544:Forecasting; D006801:Humans; D064766:Influenza A Virus, H7N9 Subtype; D005585:Influenza in Birds / Q000453:epidemiology* / Q000821:virology; D007251:Influenza, Human / Q000453:epidemiology / Q000821:virology; D008962:Models, Theoretical; D011200:Poultry / Q000821:virology
keywords: H7N9; avian influenza; data assimilation; forecasting