2020
DOI: 10.1002/jmv.26015
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Epidemiology of influenza in hospitalized children with respiratory tract infection in Suzhou area from 2016 to 2019

Abstract: Influenza is a contagious respiratory disease and risks public health in China, and it has caused wide public concern in recent years. Immunocompromised patients, such as children and elderly people, suffer more severe influenza complication and some extreme cases are even life threatening. To identify the influenza characteristics and its correlation with various climatic and environmental pollution factors, we collected the reported influenza epidemic of hospitalized children in Children's Hospital of Soocho… Show more

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Cited by 7 publications
(12 citation statements)
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References 32 publications
(57 reference statements)
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“…For examples, [ 7 12 ] used ARIMA model and its variants flexibly to predict the propagation path and the number of COVID-19 cases in various countries in the world, and achieved a satisfactory forecasting accuracy. In the study of influenza incidence prediction, Rao et al collected the data set of a reported influenza epidemic of hospitalized children in a certain hospital, identified the characteristics of the prevalent influenza virus subtypes in different months, seasons, years, and patients' age, and finally used ARIMA model to make the short-term prediction [ 13 ]. Wu et al constructed a stochastic forest regression model for predicting the weekly incidence of influenza-like illness [ 14 ].…”
Section: Literature Reviewmentioning
confidence: 99%
“…For examples, [ 7 12 ] used ARIMA model and its variants flexibly to predict the propagation path and the number of COVID-19 cases in various countries in the world, and achieved a satisfactory forecasting accuracy. In the study of influenza incidence prediction, Rao et al collected the data set of a reported influenza epidemic of hospitalized children in a certain hospital, identified the characteristics of the prevalent influenza virus subtypes in different months, seasons, years, and patients' age, and finally used ARIMA model to make the short-term prediction [ 13 ]. Wu et al constructed a stochastic forest regression model for predicting the weekly incidence of influenza-like illness [ 14 ].…”
Section: Literature Reviewmentioning
confidence: 99%
“…The ARIMA model is based on the sequential lag relationship existing in time-series data. [4] However, the SARIMA model is more suitable for forecasting when the data has obvious seasonal characteristics. The SARIMA model can be expressed as: SARIMA(p, d, q)(P, D, Q) s .…”
Section: Data Resourcesmentioning
confidence: 99%
“…Letters p, d, q is the order of autoregression, the order of difference and the order of moving average; Letters P, D, Q are the order of seasonal autoregression, the order of difference and the order of moving average, and s is the speci c value of cycle, the cycle of American in uenza is 52 weeks(s = 52). [4] The process of establishing the SARIMA model is divided into three steps: First, a weekly time-series plot of incidence(per 100,000 populations) was drawn to check for stationarity and seasonality. The model was constructed according to the auto-correlation function (ACF) and partial auto-correlation function (PACF) of the model residuals.…”
Section: Data Resourcesmentioning
confidence: 99%
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