ArticleScientific reports2018
The temporal distribution of new H7N9 avian influenza infections based on laboratory-confirmed cases in Mainland China, 2013-2017.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Spatiotemporal distribution of COVID-19 during the first 7 months of the epidemic in Vietnam.BMC infectious diseases · 2021Article
- Review
- Antiviral activity of iridoid glycosides extracted from Fructus Gardeniae against influenza A virus by PACT-dependent suppression of viral RNA replication.Scientific reports · 2020Article
- Global patterns of avian influenza A (H7): virus evolution and zoonotic threats.FEMS microbiology reviews · 2019Review
- One health insights to prevent the next HxNy viral outbreak: learning from the epidemiology of H7N9.BMC infectious diseases · 2019Article
- Review
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7 authors.
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Abstract
In this study, estimates of the growth rate of new infections, based on the growth rate of new laboratory-confirmed cases, were used to provide a statistical basis for in-depth research into the epidemiological patterns of H7N9 epidemics. The incubation period, interval from onset to laboratory confirmation, and confirmation time for all laboratory-confirmed cases of H7N9 avian influenza in Mainland China, occurring between January 2013 and June 2017, were used as the statistical data. Stochastic processes theory and maximum likelihood were used to calculate the growth rate of new infections. Time-series analysis was then performed to assess correlations between the time series of new infections and new laboratory-confirmed cases. The rate of new infections showed significant seasonal fluctuation. Laboratory confirmation was delayed by a period of time longer than that of the infection (average delay, 13 days; standard deviation, 6.8 days). At the lags of -7.5 and -15 days, respectively, the time-series of new infections and new confirmed cases were significantly correlated; the cross correlation coefficients (CCFs) were 0.61 and 0.16, respectively. The temporal distribution characteristics of new infections and new laboratory-confirmed cases were similar and strongly correlated.
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