Spatial, Temporal and Genetic Dynamics Characteristics of Influenza B Viruses in China, 1973-2018

Antigenic analysis of the influenza B virus hemagglutinin protein

Influenza B viruses (IBVs) primarily infect humans and are a common cause of respiratory infections in humans. Here, to systematically analyze the antigenicity of the IBVs Hemagglutinin (HA) protein, 31 ​B/Victoria and 19 ​B/Yamagata representative circulating strains were selected from Global Initiative of Sharing All Influenza Data (GISAID), and pseudotyped viruses were constructed with the vesicular stomatitis virus system. Guinea pigs were immunized with three doses of vaccines (one dose of DNA vaccines following two doses of pseudotyped virus vaccines) of the seven IBV vaccine strains, and neutralizing antibodies against the pseudotyped viruses were tested. By comparing differences between various vaccine strains, we constructed several pseudotyped viruses that contained various mutations based on vaccine strain BV-21. The vaccine strains showed good neutralization levels against the epidemic virus strains of the same year, with neutralization titers ranging from 370 to 840, while the level of neutralization against viruses prevalent in previous years decreased 1-10-fold. Each of the high-frequency epidemic strains of B/Victoria and B/Yamagata not only induced high neutralizing titers, but also had broadly neutralizing effects against virus strains of different years, with neutralizing titers ranging from 1000 to 7200. R141G, D197 ​N, and R203K were identified as affecting the antigenicity of IBV. These mutation sites provide valuable references for the selection and design of a universal IBV vaccine strain in the future.

Exploration of influenza incidence prediction model based on meteorological factors in Lanzhou, China, 2014-2017

Humans are susceptible to influenza. The influenza virus spreads quickly and behave seasonally. The seasonality and spread of influenza are often associated with meteorological factors and have spatio-temporal differences. Based on the influenza cases and daily average meteorological factors in Lanzhou from 2014 to 2017, this study firstly aimed to analyze the characteristics of influenza incidence in Lanzhou and the impact of meteorological factors on influenza activities. Then, SARIMA(X) models for the prediction were established. The influenza cases in Lanzhou from 2014 to 2017 was more male than female, and the younger the age, the higher the susceptibility; the epidemic characteristics showed that there is a peak in winter, a secondary peak in spring, and a trough in summer and autumn. The influenza cases in Lanzhou increased with increasing daily pressure, decreasing precipitation, average relative humidity, hours of sunshine, average daily temperature and average daily wind speed. Low temperature was a significant driving factor for the increase of transmission intensity of seasonal influenza. The SARIMAX (1,0,0)(1,0,1)[12] multivariable model with average temperature has better prediction performance than the university model. This model is helpful to establish an early warning system, and provide important evidence for the development of influenza control policies and public health interventions.

The Epidemiology of Influenza and the Associated Vaccines Development in China: A Review

Influenza prevention and control has been one of the biggest challenges encountered in the public health domain. The vaccination against influenza plays a pivotal role in the prevention of influenza, particularly for the elderly and small children. According to the epidemiology of influenza in China, the nation is under a heavy burden of this disease. Therefore, as a contribution to the prevention and control of influenza in China through the provision of relevant information, the present report discusses the production and batch issuance of the influenza vaccine, analysis of the vaccination status and vaccination rate of the influenza vaccine, and the development trend of the influenza vaccine in China.

Epidemiological Characteristics of Influenza A and B in Macau, 2010-2018

Influenza is one of the major respiratory diseases in humans. Macau is a tourist city with high density of population and special population mobility. The study on the epidemiological characteristics of influenza in Macau should bring great value for preventing influenza in tourist cities like Macau in the world. In this study, we collected a total of 104,874 samples with influenza-like illness (ILI) in Macau from 2010 to 2018. Chi-square test and binary multivariable logistic regression were used to investigate the epidemiological characteristics of influenza A and B in Macau. Among these ILI samples, the overall positive rate is 17.17% for influenza A and 6.97% for influenza B. The epidemics of influenza in three years (i.e., 2012, 2017 and 2018) differ from the remaining years (i.e., normal years). In a normal year, influenza A occurs year-round whereas influenza B is seasonal. Our research shows significant differences in influenza infections between different age groups in normal years. Interestingly, our analysis shows no significant difference between locals and tourists in influenza A and B infection in a normal year, whereas the odds of influenza A in tourists were significantly higher than those in locals in July 2017 and the odds of influenza B in tourists were significantly higher than those in locals in January-February 2012 and January-February 2018. This is possibly attributed by the policy of free vaccination to everyone in Macau. These findings should be valuable for preventing influenza in not only Macau but also the world.

Spatial transmission network construction of influenza-like illness using dynamic Bayesian network and vector-autoregressive moving average model

BACKGROUND

Although vaccination is one of the main countermeasures against influenza epidemic, it is highly essential to make informed prevention decisions to guarantee that limited vaccination resources are allocated to the places where they are most needed. Hence, one of the fundamental steps for decision making in influenza prevention is to characterize its spatio-temporal trend, especially on the key problem about how influenza transmits among adjacent places and how much impact the influenza of one place could have on its neighbors. To solve this problem while avoiding too much additional time-consuming work on data collection, this study proposed a new concept of spatio-temporal route as well as its estimation methods to construct the influenza transmission network.

METHODS

The influenza-like illness (ILI) data of Sichuan province in 21 cities was collected from 2010 to 2016. A joint pattern based on the dynamic Bayesian network (DBN) model and the vector autoregressive moving average (VARMA) model was utilized to estimate the spatio-temporal routes, which were applied to the two stages of learning process respectively, namely structure learning and parameter learning. In structure learning, the first-order conditional dependencies approximation algorithm was used to generate the DBN, which could visualize the spatio-temporal routes of influenza among adjacent cities and infer which cities have impacts on others in influenza transmission. In parameter learning, the VARMA model was adopted to estimate the strength of these impacts. Finally, all the estimated spatio-temporal routes were put together to form the final influenza transmission network.

RESULTS

The results showed that the period of influenza transmission cycle was longer in Western Sichuan and Chengdu Plain than that in Northeastern Sichuan, and there would be potential spatio-temporal routes of influenza from bordering provinces or municipalities into Sichuan province. Furthermore, this study also pointed out several estimated spatio-temporal routes with relatively high strength of associations, which could serve as clues of hot spot areas detection for influenza surveillance.

CONCLUSIONS

This study proposed a new framework for exploring the potentially stable spatio-temporal routes between different places and measuring specific the sizes of transmission effects. It could help making timely and reliable prediction of the spatio-temporal trend of infectious diseases, and further determining the possible key areas of the next epidemic by considering their neighbors' incidence and the transmission relationships.

The moving epidemic method applied to influenza surveillance in Guangdong, China

OBJECTIVES

The moving epidemic method (MEM) has been well used for assessing seasonal influenza epidemics in temperate regions. This study used the MEM to establish epidemic threshold for influenza in Guangdong, a subtropical province in China.

METHODS

Influenza virology surveillance data from 2011/2012 to 2017/2018 seasons in Guangdong were used with the MEM to calculate the epidemic thresholds and timeously detect the 2018/2019 influenza season epidemic. The weekly positive proportion of influenza A(H1N1)pdm09, A(H3N2), B/Victoria-lineage and B/Yamagata-lineage were separately adapted to calculate the subtype-specific epidemic thresholds. The performance of MEM was evaluated using a cross-validation procedure.

RESULTS

For the 2018/2019 influenza season, the epidemic threshold of a weekly positive proportion was 15.08%. Epidemic detection for the 2018/2019 season was 1 week in advance. Influenza A(H1N1)pdm09, B/Yamagata-lineage and B/Victoria-lineage prevailed during winter and spring and their epidemic thresholds were 5.12%, 4.53% and 4.38%, respectively. Influenza A(H3N2) was active in the summer, with an epidemic threshold of 11.99%.

CONCLUSIONS

Using influenza virology surveillance data stratified by types of influenza virus, the MEM was effectively used in Guangdong, China. This study provided a practical way for subtropical regions to establish local influenza epidemic thresholds.