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 Soochow University from 2016 to 2019. Our results show that the main influenza virus subtypes are A/H1N1, A/H3N2, B/BV, and B/BY. We also identified the characteristics of the prevalent influenza virus subtypes in different months, seasons, years, and patients' age. Of all the influenza infected patients, the most susceptible groups are children over 3 to 5 years of age, and more cases are reported in winter than other seasons. We also found that influenza is also highly correlated with climatic and environmental pollution factors, and the autoregressive integrated moving average model is employed for the short‐term influenza prediction in Suzhou city, which can provide scientific basis for the prevention and control of influenza and public health decision‐making.

Forecasting emergency medicine reserve demand with a novel decomposition-ensemble methodology

“…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 ].…”

Spatio-temporal analysis of influenza-like illness and prediction of incidence in high-risk regions, in the United States from 2011 to 2020

“…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 .…”

“…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.…”

“…[3] Therefore, it is necessary to strengthen the early warning and public health forecasting of in uenza. [4] Since in uenza might be with fever, cough, sore throat, runny or stuffy nose, body aches, headache, chills or fatigue and so on, it's hard to be diagnosed as in uenza based on symptoms alone. A number of in uenza tests are available to detect in uenza viruses in respiratory specimens.…”

“…[9] For diseases that show recurrent seasonal patterns or occur in cyclic patterns, time series models are the most widely used statistical models by health researchers for forecasting. [10] The autoregressive integrated moving average (ARIMA) is a widely used predictive analysis model for non-stationary time series, [4] which is of great signi cance to the study of non-stationary time series. In this study, the model was used to predict ILI outbreaks, and combined with spatial geographic analysis to provide theoretical guidance and scienti c basis for the prevention and treatment of in uenza.…”