Epidemiological features and time-series analysis of influenza incidence in urban and rural areas of Shenyang, China, 2010-2018

Abstract

In recent years, there have been a significant influenza activity and emerging influenza strains in China, resulting in an increasing number of influenza virus infections and leading to public health concerns. The aims of this study were to identify the epidemiological and aetiological characteristics of influenza and establish seasonal autoregressive integrated moving average (SARIMA) models for forecasting the percentage of visits for influenza-like illness (ILI%) in urban and rural areas of Shenyang. Influenza surveillance data were obtained for ILI cases and influenza virus positivity from 18 sentinel hospitals. The SARIMA models were constructed to predict ILI% for January-December 2019. During 2010-2018, the influenza activity was higher in urban than in rural areas. The age distribution of ILI cases showed the highest rate in young children aged 0-4 years. Seasonal A/H3N2, influenza B virus and pandemic A/H1N1 continuously co-circulated in winter and spring seasons. In addition, the SARIMA (0, 1, 0) (0, 1, 2)12 model for the urban area and the SARIMA (1, 1, 1) (1, 1, 0)12 model for the rural area were appropriate for predicting influenza incidence. Our findings suggested that there were regional and seasonal distinctions of ILI activity in Shenyang. A co-epidemic pattern of influenza strains was evident in terms of seasonal influenza activity. Young children were more susceptible to influenza virus infection than adults. These results provide a reference for future influenza prevention and control strategies in the study area.

Keywords: Influenza strains; influenza-like illness; seasonal distinctions; surveillance.

Fig. 1.

The monthly percentage of visits for influenza-like illness (ILI%) of urban and rural areas in Shenyang, 2010–2018.

Fig. 2.

Monthly distribution of laboratory-confirmed influenza cases and ILI% of Shenyang during 2010–2018.

Fig. 3.

The time series for monthly ILI% at non-seasonal difference and (or) seasonal difference during 2011–2018. (a) Original data of urban area; (b) data of urban area at first-order non-seasonal difference (d = 1) and first-order seasonal difference (D = 1); (c) original data of rural area; (d) data of rural area at d = 1 and D = 1.

Fig. 4.

Auto-correlation function (ACF) and partial auto-correlation function (PACF) graphs of monthly ILI% at non-seasonal difference and (or) seasonal difference. (a) ACF and PACF graphs for urban area at d = 1, D = 1. (b) ACF and PACF graphs for rural area at d = 1, D = 1.

Fig. 5.

Time-series plots for predicted values of monthly ILI% by SARIMA model during 2011–2019. (a) Urban area. (b) Rural area. Dotted lines indicate the 95% confidence intervals (CIs) (UCL: upper limit of 95% CI; LCL: lower limit of 95% CI).