Epidemiological Features and Forecast Model Analysis for the Morbidity of Influenza in Ningbo, China, 2006–2014

Abstract

This study aimed to identify circulating influenza virus strains and vulnerable population groups and investigate the distribution and seasonality of influenza viruses in Ningbo, China. Then, an autoregressive integrated moving average (ARIMA) model for prediction was established. Influenza surveillance data for 2006–2014 were obtained for cases of influenza-like illness (ILI) (n = 129,528) from the municipal Centers for Disease Control and virus surveillance systems of Ningbo, China. The ARIMA model was proposed to predict the expected morbidity cases from January 2015 to December 2015. Of the 13,294 specimens, influenza virus was detected in 1148 (8.64%) samples, including 951 (82.84%) influenza type A and 197 (17.16%) influenza type B viruses; the influenza virus isolation rate was strongly correlated with the rate of ILI during the overall study period (r = 0.20, p < 0.05). The ARIMA (1, 1, 1) (1, 1, 0)12 model could be used to predict the ILI incidence in Ningbo. The seasonal pattern of influenza activity in Ningbo tended to peak during the rainy season and winter. Given those results, the model we established could effectively predict the trend of influenza-related morbidity, providing a methodological basis for future influenza monitoring and control strategies in the study area.

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

“…In total, influenza B virus accounted for the maximum number of laboratory-confirmed influenza cases, followed by pandemic A/H1N1 and seasonal A/H3N2. These results corresponded with the facts that a co-epidemic pattern of these three strains of influenza virus has formed in China since 2009, with seasonal A/H3N2 predominantly circulating in the south of China while influenza B virus and pandemic A/ H1N1 are more frequent in northern China [4,18,19,[33][34][35]. It is noteworthy that the number of influenza-positive cases was much lower in 2011 and 2012 than other years.…”

A comparative study of two methods to predict the incidence of hepatitis B in Guangxi, China

“…ARIMA (p, d, q) model is an important time series analysis and prediction model, which is also called Autoregressive Integrated Moving Average Model [25,26]. Because the model can capture the trend and randomness of data, it is widely used in the prediction of infectious diseases, and has achieved good prediction results, such as, Wang et al [16] found that ARIMA model could predict the morbidity of influenza in Ningbo, China, 2006-2014, successfully; Shen et al [18] analyzed that ARIMA model was successful in predicting hemorrhagic fever with renal syndrome in China; Anokye et al [21] found that ARIMA model had good performance in predicting malaria incidence; etc [17,19,20].…”

“…In recent years, many mathematical model methods were used to predict the incidence of infectious diseases, such as linear model [11,12], dynamics model [13,14], grey model [15], time series ARIMA model, neural network model, and so on. Since the time series of infectious diseases often have the characteristics of trend and randomness, ARIMA model and neural network model can capture the regularity of such data well, so they were most widely used and obtained good prediction performance and high prediction accuracy [16][17][18][19][20][21][22][23][24].…”

Analysis and Estimation of COVID-19 Spreading in Russia Based on ARIMA Model

“…For instance, Earnest et al [7] used the ARIMA model to predict and monitor the number of beds occupied during a SARS outbreak in a tertiary hospital in Singapore. Wang et al [8] estimated the morbidity of Influenza in Ningbo, China, 2006-2014 by the ARIMA model. Singh et al [9] forecasted the transmission trajectory of COVID-19 disease in the 15 worst-hit countries in the next 2 months based on the ARIMA model.…”