ArticlePloS one2016
Seasonality of Influenza A(H7N9) Virus in China-Fitting Simple Epidemic Models to Human Cases.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Bayesian phylodynamics reveals the transmission dynamics of avian influenza A(H7N9) virus at the human-live bird market interface in China.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Identifying Risk Factors Of A(H7N9) Outbreak by Wavelet Analysis and Generalized Estimating Equation.International journal of environmental research and public health · 2019Article
- Spatiotemporal Variation and Hotspot Detection of the Avian Influenza A(H7N9) Virus in China, 2013⁻2017.International journal of environmental research and public health · 2019Article
- Structural and Molecular Characterization of the Hemagglutinin from the Fifth-Epidemic-Wave A(H7N9) Influenza Viruses.Journal of virology · 2018Article
- The temporal distribution of new H7N9 avian influenza infections based on laboratory-confirmed cases in Mainland China, 2013-2017.Scientific reports · 2018Article
- Avian Influenza A (H7N9) Model Based on Poultry Transport Network in China.Computational and mathematical methods in medicine · 2018Article
- A Potent Germline-like Human Monoclonal Antibody Targets a pH-Sensitive Epitope on H7N9 Influenza Hemagglutinin.Cell host & microbe · 2017Article
- Inference and forecast of H7N9 influenza in China, 2013 to 2015.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2017Article
- Modelling the transmission and control strategies of varicella among school children in Shenzhen, China.PloS one · 2017Article
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4 authors.
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Abstract
backgroundThree 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.
methodsData 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.
resultsOur 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.
conclusionsThis 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.
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