Seasonality of Influenza A(H7N9) Virus in China-Fitting Simple Epidemic Models to Human Cases

• Published in: PloS one Vol. 11; no. 3; p. e0151333
• Main Authors: Lin, Qianying, Lin, Zhigui, Chiu, Alice P Y, He, Daihai
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 10.03.2016; Public Library of Science (PLoS)
• Subjects: Analysis; Animal diseases; Animals; Applied mathematics; Avian influenza; Bayes Theorem; Bayesian analysis; Biology and life sciences; Chickens; China; China - epidemiology; Closures; Comparative analysis; Criteria; Diagnosis; Disease transmission; Economic conditions; Environmental changes; Epidemics; Epidemiology; Health aspects; Humans; Influenza; Influenza A; Influenza A Virus, H7N9 Subtype - physiology; Influenza, Human - epidemiology; Information systems; Mathematical models; Medicine and health sciences; Models, Statistical; Orthomyxoviridae; Outbreaks; Pandemics; Parameter estimation; People and Places; Poultry; Research and Analysis Methods; Risk factors; Seasonal variations; Seasons; Shedding; Viruses; China; Hong Kong China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0151333

Three epidemic waves of influenza A(H7N9) (hereafter 'H7N9') human cases have occurred between March 2013 and July 2015 in China. However, the underlying transmission mechanism remains unclear. Our main objective is to use mathematical models to study how seasonality, secular changes and environmental transmission play a role in the spread of H7N9 in China. Data on human cases and chicken cases of H7N9 infection were downloaded from the EMPRES-i Global Animal Disease Information System. We modelled on chicken-to-chicken transmission, assuming a constant ratio of 10-6 human case per chicken case, and compared the model fit with the observed human cases. We developed three different modified Susceptible-Exposed-Infectious-Recovered-Susceptible models: (i) a non-periodic transmission rate model with an environmental class, (ii) a non-periodic transmission rate model without an environmental class, and (iii) a periodic transmission rate model with an environmental class. We then estimated the key epidemiological parameters and compared the model fit using Akaike Information Criterion and Bayesian Information Criterion. Our results showed that a non-periodic transmission rate model with an environmental class provided the best model fit to the observed human cases in China during the study period. The estimated parameter values were within biologically plausible ranges. This study highlighted the importance of considering secular changes and environmental transmission in the modelling of human H7N9 cases. Secular changes were most likely due to control measures such as Live Poultry Markets closures that were implemented during the initial phase of the outbreaks in China. Our results suggested that environmental transmission via viral shedding of infected chickens had contributed to the spread of H7N9 human cases in China.