Temporal Patterns of Influenza A and B in Tropical and Temperate Countries: What Are the Lessons for Influenza Vaccination?

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Abstract

Introduction

Determining the optimal time to vaccinate is important for influenza vaccination programmes. Here, we assessed the temporal characteristics of influenza epidemics in the Northern and Southern hemispheres and in the tropics, and discuss their implications for vaccination programmes.

Methods

This was a retrospective analysis of surveillance data between 2000 and 2014 from the Global Influenza B Study database. The seasonal peak of influenza was defined as the week with the most reported cases (overall, A, and B) in the season. The duration of seasonal activity was assessed using the maximum proportion of influenza cases during three consecutive months and the minimum number of months with ≥80% of cases in the season. We also assessed whether co-circulation of A and B virus types affected the duration of influenza epidemics.

Results

212 influenza seasons and 571,907 cases were included from 30 countries. In tropical countries, the seasonal influenza activity lasted longer and the peaks of influenza A and B coincided less frequently than in temperate countries. Temporal characteristics of influenza epidemics were heterogeneous in the tropics, with distinct seasonal epidemics observed only in some countries. Seasons with co-circulation of influenza A and B were longer than influenza A seasons, especially in the tropics.

Discussion

Our findings show that influenza seasonality is less well defined in the tropics than in temperate regions. This has important implications for vaccination programmes in these countries. High-quality influenza surveillance systems are needed in the tropics to enable decisions about when to vaccinate.

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Characterization of Regional Influenza Seasonality Patterns in China and Implications for Vaccination Strategies: Spatio-Temporal Modeling of Surveillance Data

Hongjie Yu, Wladimir J Alonso, Luzhao Feng … (2013)

Introduction The seasonality of influenza has been well studied in temperate regions of the world but remains poorly characterized in tropical and subtropical areas [1],[2]. A growing body of evidence suggests that seasonal patterns are highly diverse in tropical settings, particularly in Asia, where influenza can display semi-annual or annual epidemic cycles, as well as year-round activity [2]–[4]. Experimental and modeling studies have suggested that low levels of absolute humidity and cold temperature favor influenza transmission and survival in temperate settings [5]–[8], while rainfall fluctuations may drive influenza activity in low latitudes [8]. From a public health perspective, local information on influenza seasonality and circulating strains is crucial to inform the timing and composition of influenza vaccines, particularly for large tropical countries [9]. In parallel, there is growing interest in establishing routine immunization programs in low- and middle-income regions [10], due to strengthening of laboratory surveillance systems and increased recognition of disease burden [11]–[14]. China is a geographically, economically, and climatologically diverse country with a population of 1.34 billion, which experiences substantial influenza mortality burden, estimated at 11–18 excess deaths per 100,000 in pandemic and inter-pandemic seasons [11],[15]. Although seasonal influenza vaccination was introduced in 1998, China has yet to initiate a national immunization program [16]. Previous work has suggested intriguing differences in the seasonality and evolutionary dynamics of influenza between Northern and Southern China [4],[17],[18], indicating that analysis of high-resolution epidemiological data will be required to guide control strategies in this country. The goals of our study were to characterize the seasonality of the disease across China, assess the role of putative drivers of seasonality, and identify broad epidemiological regions that could be used as a basis to optimize the timing of future vaccination programs. Methods Influenza Surveillance Dataset We used weekly reports from a national sentinel hospital-based influenza surveillance network, providing the number of laboratory-confirmed influenza cases by virus type (influenza A and B) and the number of specimens tested in 30 Chinese provinces. Influenza laboratory surveillance was initiated in 2000 in China; here we focused on the period 2005–2011 where sampling was more intense. We briefly describe the surveillance system below; refer to [11] for more details. Each week, 193 sentinel hospitals located in 88 cities representing all 30 provinces with exception of Tibet (Figure 1) reported the number of patients with influenza-like-illness (ILI) and total visits to outpatient and/or emergency departments to a centralized online system maintained by Chinese Center for Disease Control and Prevention (China CDC, Beijing). Identification of patients with ILI was based on a standard case definition, including body temperature ≥38°C with either cough or sore throat, in the absence of an alternative diagnosis. In each sentinel hospital, nasopharyngeal swabs were collected daily from the first one or two ILI cases and placed in sterile viral transport medium for influenza virus testing, resulting in ten to 15 specimens per hospital per surveillance week. Samples were inoculated into Madin-Darby canine kidney (MDCK) cells and/or specific pathogen free (SPF) chicken embryo for virus isolation. Hemagglutination inhibition (HI) and/or conventional or real-time reverse transcription PCR (RT-PCR) assay were performed to identify the types/subtype of influenza virus, following a standard protocol [19]. 10.1371/journal.pmed.1001552.g001 Figure 1 Map of Chinese provinces conducting influenza surveillance (n = 30). Dots indicate the location of the capital city in each province. A total of 193 hospitals participate in disease surveillance, representing 88 cities. Colors illustrate different climatic domains (black, cold-temperate; blue, mid-temperate; green, warm-temperate; orange, subtropical; red, tropical). Different symbols indicate the type of surveillance scheme (circles, year-round surveillance; triangles, Oct through Mar surveillance). Because of limited understanding of the seasonality of influenza in China prior to this study, and following general WHO recommendations, influenza surveillance was implemented year-round in 99 Southern Chinese hospitals, representing 45 cities below latitude 34°N. To confirm the seasonality of influenza in Northern China, surveillance activities were also conducted year-round in 22 of the 94 northern hospitals, while surveillance was restricted to the period October to March in the remaining hospitals (representing 43 cities total; Figure 1). In response to the evolving A/H1N1 pandemic, all 193 sentinel hospitals were asked to implement surveillance year-round starting in May 2009. All 193 participating hospitals have contributed systematic information during the study period, so that the number of participating sites remained constant. Since China is located in the Northern Hemisphere, we defined the respiratory season as the period running from August 1st to July 31st each year. Analysis excluded the A/H1N1 pandemic season April 2009–April 2010 to focus on influenza patterns in inter-pandemic seasons. Climate, Geographic, and Demographic Data To assess the role of putative drivers of influenza seasonality, we collected province-level demographic, economic, and geographic data, including population size and density [20], gross domestic product [20], and human mobility patterns between provinces (including the per capita number of passengers travelling by air, rail, road, and boat [21]). Average latitude and longitude coordinates for each province were obtained by weighting the coordinates of cities participating in influenza surveillance by their population sizes (Table 1). 10.1371/journal.pmed.1001552.t001 Table 1 Background characteristics of the 30 provinces involved in influenza surveillance and information on influenza sampling intensity, 2005–2011, China. Province (Climate) n Cities (Hosp)a Population Size (M) Latitude (°N) Longitude (°E) Per Capita GRP (2009) $ Per Capita Annual Passenger Fluxes Mean Monthly Temperature (Min, Max) °C Mean Monthly Air Pressure (Min, Max) Bar Mean Monthly Relative Humidity (Min, Max) % Mean Monthly Rainfall (Min, Max) mm Mean Monthly Hours Sunshine (Min, Max) Mean Annual n Specimens Tested (rate per 10,000) Mean Annual n Influenza Positive Anhuib (ST) 2 (5) 5.9 31.8 117.5 2,402 219.5 17 (3, 29) 1.01 (1, 1.02) 74 (68, 83) 32 (30, 37) 11 (4, 20) 2,273 (38.6) 360 Beijing (WT) 1 (5) 16.1 39.9 116.4 10,313 77.7 13 (−2, 27) 1.01 (1, 1.02) 53 (39, 70) 23 (17, 32) 4 (0, 9) 2,122 (13.2) 485 Chongqingb (ST) 1 (5) 13.8 29.6 106.6 3,355 77.2 19 (8, 29) 0.98 (0.97, 0.99) 79 (73, 86) 34 (32, 38) 12 (3, 22) 1,892 (13.7) 436 Fujianb (ST) 4 (9) 19.9 25.3 118.8 4,954 36.1 21 (11, 29) 1 (0.99, 1.01) 73 (65, 80) 32 (29, 34) 15 (3, 29) 4,231 (21.3) 701 Gansub (MT) 3 (6) 8.4 35.6 104.7 1,884 54.4 9 (−6, 22) 0.84 (0.83, 0.84) 59 (47, 71) 26 (21, 31) 3 (0, 6) 2,213 (26.2) 389 Guangdongb (ST) 3 (9) 21.1 22.9 113.4 6,026 224.8 23 (14, 29) 1.01 (1, 1.02) 73 (64, 79) 32 (28, 34) 17 (4, 39) 5,671 (26.8) 772 Guangxib (ST) 1 (4) 6.6 22.9 108.4 2,349 96.2 22 (12, 28) 1 (0.99, 1.01) 79 (74, 83) 34 (32, 37) 12 (5, 27) 2,082 (31.5) 245 Guizhoub (ST) 2 (5) 10.9 27.4 106.8 1,509 37.5 15 (4, 24) 0.9 (0.89, 0.9) 76 (73, 80) 33 (31, 36) 11 (4, 21) 1,920 (17.7) 179 Hainanb (T) 5 (6) 4.1 19.6 110.1 2,818 93.5 25 (18, 29) 1 (1, 1.01) 79 (76, 82) 34 (32, 36) 16 (2, 36) 1,860 (45.9) 240 Hebei (WT) 4 (10) 33.7 38.1 115.8 3,598 21.7 14 (−2, 27) 1.01 (1, 1.02) 59 (45, 74) 26 (20, 33) 4 (0, 10) 1,899 (5.6) 344 Heilongjiang (MT) 3 (7) 18.6 46.1 126.2 3,286 22.4 5 (−15, 23) 0.99 (0.98, 1) 63 (50, 74) 27 (22, 33) 5 (1, 12) 1,634 (8.8) 198 Henan (WT) 2 (5) 13.2 34.7 113.1 3,015 98.2 15 (1, 26) 0.98 (0.97, 0.99) 61 (51, 79) 27 (22, 35) 5 (1, 11) 1,581 (11.9) 207 Hubeib (ST) 4 (9) 21.1 30.9 112.6 3,320 41.7 17 (4, 28) 1 (0.99, 1.01) 74 (70, 78) 32 (30, 35) 11 (4, 19) 3,829 (18.2) 838 Hunanb (ST) 3 (9) 14.2 27.4 113.0 2,990 92.5 18 (6, 30) 1 (0.99, 1.01) 73 (66, 78) 32 (30, 34) 15 (7, 25) 3,750 (26.5) 657 Mongolia (MT) 2 (6) 4.7 40.8 110.8 5,897 42.4 8 (−10, 24) 0.9 (0.89, 0.9) 49 (34, 59) 21 (15, 26) 3 (0, 7) 971 (20.5) 129 Jiangsub (ST) 4 (9) 24.2 32.9 118.6 6,550 75.7 16 (3, 29) 1.01 (1, 1.03) 73 (67, 80) 32 (29, 36) 10 (4, 19) 3,912 (16.2) 709 Jiangxib (ST) 3 (5) 11.4 28.2 115.3 2,538 58.0 19 (6, 30) 1.01 (1, 1.02) 75 (69, 78) 33 (30, 34) 17 (7, 30) 1,698 (15.0) 274 Jilin (MT) 3 (6) 14.0 44.1 125.4 3,893 40.2 6 (−15, 23) 0.99 (0.98, 1) 60 (46, 76) 26 (20, 34) 5 (1, 12) 1,805 (12.9) 321 Liaoningb (WT) 7 (11) 26.5 40.7 122.6 5,158 34.0 10 (−7, 24) 1.01 (1, 1.02) 63 (52, 80) 27 (21, 36) 5 (1, 13) 3,409 (12.9) 341 Ningxia (MT) 4 (5) 4.8 37.6 106.0 3,188 24.7 10 (−7, 23) 0.87 (0.87, 0.88) 52 (37, 65) 23 (16, 28) 2 (0, 5) 1,122 (23.3) 164 Qinghai (C) 3 (5) 4.0 36.6 101.8 2,848 23.4 5 (−8, 17) 0.76 (0.75, 0.76) 56 (43, 71) 24 (18, 31) 4 (0, 9) 611 (15.2) 64 Shaanxi (WT) 2 (7) 12.8 34.3 108.8 3,175 59.2 10 (−4, 22) 0.88 (0.87, 0.89) 70 (55, 85) 30 (24, 37) 5 (1, 13) 1,258 (9.8) 309 Shandong (WT) 3 (7) 19.6 36.3 118.4 5,254 109.2 11 (−2, 23) 0.95 (0.94, 0.96) 64 (53, 82) 28 (23, 37) 8 (1, 22) 1,686 (8.6) 372 Shanghaib(ST) 1 (5) 18.2 31.3 121.5 11,563 4.7 18 (5, 29) 1.02 (1, 1.03) 72 (68, 76) 31 (29, 33) 12 (4, 20) 1,897 (10.5) 500 Shanxi (WT) 3 (5) 7.9 37.8 112.8 3,150 45.5 11 (−4, 25) 0.93 (0.92, 0.94) 57 (43, 73) 25 (19, 32) 4 (0, 8) 1,012 (12.7) 216 Sichuanb(ST) 4 (5) 18.3 30.2 104.0 2,538 111.6 18 (9, 26) 0.93 (0.92, 0.94) 70 (59, 78) 30 (25, 34) 11 (2, 24) 1,021 (5.6) 187 Tianjinb (WT) 1 (5) 9.7 39.2 117.2 9,160 23.5 13 (−3, 27) 1.02 (1, 1.03) 60 (48, 74) 26 (21, 33) 4 (0, 11) 1,342 (13.8) 316 Xinjiang (MT) 2 (4) 4.0 43.8 87.6 2,919 81.6 7 (−15, 23) 0.92 (0.91, 0.93) 58 (43, 78) 25 (18, 34) 2 (1, 3) 1,271 (31.6) 202 Yunnanb (ST) 4 (6) 19.1 24.8 103.0 1,982 17.8 17 (10, 21) 0.83 (0.82, 0.83) 68 (55, 78) 30 (23, 35) 9 (2, 22) 2,801 (14.7) 285 Zhejiangb (ST) 4 (8) 22.2 30.0 120.4 6,535 97.7 18 (5, 30) 1.01 (1, 1.02) 72 (70, 75) 31 (30, 33) 14 (6, 19) 3,288 (14.8) 672 a Number of cities and hospitals participating in surveillance. b Indicates year-round influenza surveillance before 2009 (all provinces switched to year-round surveillance in the post-2009 pandemic period). C, cold temperate; GRP, gross regional product; MT, mid-temperate; ST, subtropical; T, tropical; WT, warm temperate; We obtained daily meteorological data for each participating city during the study period, including temperature (minimum, maximum, mean), vapor pressure (minimum, maximum, mean), relative humidity (minimum, maximum, mean), rainfall, and hours of sunshine, as recorded by China Meteorological Administration (Table 1; Text S1) [22]. Province-level meteorological indicators were calculated as population-weighted averages of city-level data. Summary climate indicators were obtained by averaging the daily values of each climate factor by season (winter, Dec–Feb; spring, Mar–May; summer, Jun–Aug; fall, Sep–Nov), as well as calculating annual minimums and maximums. We also categorized the 30 provinces into six climatic zones on the basis of previous work [23], ranging from tropical to cold-temperate climates (Figure 1; Table 1). Estimates of Seasonal Characteristics To visualize the average seasonal signature of influenza in each province, we first estimated the proportion of influenza cases identified in each week of the respiratory season, averaged across all complete years available for study. This method provided an empirical measure of seasonality, while adjusting for differences in sampling intensity and viral activity over time and between provinces [1],[24]. Weekly province-level influenza virus positive isolates were standardized by the annual number of influenza specimens tested prior to further modeling [25]. Preliminary analyses using a wavelet approach [26],[27] did not reveal changes in periodicity over time, so we elected to use stationary methods to characterize influenza seasonality. To obtain quantitative seasonality estimates, we fitted multiple linear regression models to weekly influenza time series separately in each province, including harmonic terms representing annual and semi-annual periodicities (see Text S1 for full details and [4],[26],[28],[29]). Briefly, the model follows: where flui(t) are the weekly standardized counts of influenza positive A isolates (or B, or A+B combined) in province i; t is a running index for week; ai , bi , ci , di , and ei are the intercept and seasonality coefficients to be estimated from the data; and ε i (t) are normally distributed errors. On the basis of the estimated model coefficients representing harmonic terms, we extracted the amplitude of annual and semi-annual periodicities (AnnAmpi = sqrt(bi 2+ci 2) and SemiAnnAmpi = sqrt(di 2+ei 2)), and the annual peak timing (AnnPeakTimingi = −atan(ci /bi )). To control for different levels of influenza activity across provinces, we calculated the relative amplitudes of annual and semi-annual periodicities, obtained by dividing AnnAmpi and SemiAnnAmpi estimates by the mean of the flui (t) time series [26]. To estimate the relative contribution of semi-annual periodicity, we calculated the ratio between the amplitude of the semi-annual periodicity and the sum of the amplitudes of annual and semi-annual periodicities (ratioi = SemiAnnAmpi/(AnnAmpi +SemiAnnAmpi )). A ratio close to 1 is indicative of dominant semi-annual periodicity while a ratio close to 0 indicates dominant annual periodicity. Confidence intervals on estimates of relative amplitude, peak timing, and periodicity ratio were obtained by fitting seasonal regression models to 1,000 datasets resampled from the original data by block-bootstrap, which accounts for auto-correlation in weekly influenza incidences (Text S1). As a sensitivity analysis, we fitted joint seasonal regression models in all 30 provinces using mixed effects models, accounting for fixed effects for broad geographic regions and random effects for provinces (Text S1). This approach revealed nearly identical seasonal curves as in the province-stratified analysis, indicating that the information contained in province-specific influenza data was sufficient to fit separate models. In the remainder of the paper, we report the results of province-stratified analyses. Further sensitivity analyses were conducted by fitting logistic regression models to the weekly influenza percent positive (weekly number of positive/weekly number specimens tested), which has also been used in past influenza research (Text S1) [27],[30]. In addition to seasonal parameters derived from regression models, we also quantified the median epidemic duration in each of the 30 provinces, defined as the number of weeks in which the reported number of influenza viruses exceeded a relative threshold, set at 2.5% or 5% of the total number of influenza viruses reported during the respiratory season. As a sensitivity analysis we estimated epidemic duration on the basis of the weekly percent positive exceeding an absolute threshold, set at 5% or 10% of all specimens tested in the week. We also assessed whether duration estimates were affected by sampling scheme, in particular whether surveillance was conducted year-round or limited to the October–March period. As all analyses revealed important differences in the seasonality of influenza A and B, we present influenza A- and B-specific seasonal parameter estimates in the main text and refer to the Text S1 for aggregate analyses. Predictors of Influenza Seasonality Next, we searched for predictors of influenza seasonal characteristics, including geography (latitude, longitude), population size and density, human mobility patterns, surveillance intensity (number of viruses sampled, number of participating hospitals and cities), and climate variables (Table 1). As seasonal characteristics were not fixed parameters but rather parameters estimated from seasonal regression models, we used a hierarchical Bayesian approach with non-informative priors to regress seasonal parameters against putative predictors (Text S1). A subset of predictors was first identified by classical stepwise multivariate analysis and these predictors were then used in the Bayesian approach. The errors obtained by bootstrap resampling were considered as observation variances in the Bayesian approach. Epidemiological Regions Relevant for Control To assist with the design of routine influenza vaccination programs in China, in particular with regard to the optimal timing of vaccination, we set out to identify broad regions that share similar influenza epidemiological patterns. We applied hierarchical clustering using Ward's minimum variance method [31] to identify regional clusters, relying on the squared Euclidian pairwise difference between standardized influenza time series as the distance metrics [32]. We also performed sensitivity analyses using an alternative distance metric (Manhattan distance [32]) and clustering approach (complete linkage [33]). Finally, we applied stepwise linear discriminant analysis to identify the putative geographic, demographic, and climate predictors of the epidemiological regions defined by the cluster analysis. Results Sampling Intensity During the study period 2005–2011, the average number of samples tested for influenza averaged 2,200 annually by province, with most intense sampling in Guangdong province in Southern China (5,661 samples per year) and thinnest sampling in Qinghai province in Northwest China (611 samples per year; Table 1). This level of sampling corresponds to 1.81 respiratory samples tested on average per year per 10,000 population in China (range across provinces 0.56–4.58). On average, 371 influenza virus positive specimens were identified annually by province (range across provinces 64–838). Influenza Seasonal Characteristics by Province Empirical seasonality patterns and seasonal regression models Heatmaps representing weekly province-level laboratory-confirmed influenza time series and their empirical seasonal signature are provided in Figure 2, revealing a diversity of seasonality patterns across China. While northern China experienced epidemics concentrated in winter, and southernmost provinces experienced influenza activity in spring–summer, provinces at intermediate latitudes did not exhibit clear annual seasonality. 10.1371/journal.pmed.1001552.g002 Figure 2 Heatmaps of influenza epidemiological data by Chinese province, Oct 2005-Dec 2011. (A) Time series of weekly standardized influenza cases, sorted by increasing latitude from bottom to top. Dashed vertical lines represent the influenza A/H1N1pdm pandemic period, Apr 2009–Apr 2010. (B) Average seasonal distribution of influenza cases (excluding the pandemic period), plotted as the proportion of viruses isolated in each week of the year. Provinces conducting year-round surveillance are denoted by an asterix. Week 0 is the first week of October of each year. Seasonal regression of time series data allowed further quantification of influenza seasonal characteristics and confirmed important differences by geography and virus type. Seasonal models fitted reasonably well for all influenza subtypes combined, influenza A, and high latitude provinces (median R2 = 23%, range 3%–60%); however fit was poorer for influenza B (median R2 = 10%, range 0.1%–26%; see Figures S1 and S2 and Text S1 for time series plots and residuals). Model fit however was not related to sampling intensity for any of the influenza outcomes (p>0.19; Text S1). Periodicity Influenza A displayed strong annual periodicity in provinces above ∼33°N latitude, and weaker annual periodicity at lower latitudes (median relative amplitude of annual cycle, 140% [95% CI 128%–151%] in the 15 northern temperate provinces versus 37% [95% CI 27%–47%] in the 15 southern provinces, Wilcoxon test, p 0.6) (Figures 3 and 4). Overall, there was weak latitudinal gradient in importance of the semi-annual cycle, indicative of more intense semi-annual influenza A activity in southern China (slope −0.016 [95% CI −0.025 to −0.008], R2 = 0.31, p 0.79; p 0.24). Differences in epidemic duration by geography and virus type were robust to using more conservative epidemic thresholds (i.e., using higher thresholds, resulting in shorter duration estimates). Prevalence of influenza A and B Given observed differences in the seasonality of influenza by geography and virus type, we checked whether the relative predominance of these viruses also differed across China. We found that the median proportion of influenza B among all influenza virus positive specimens ranged between 5%–55% across provinces in the 7 study years, with increasing prevalence towards the south (Spearman rho = −0.71 between latitude and influenza B proportion; p 33°N) experience winter epidemics, southernmost provinces (latitude 60 y and school-age children, while Xi'an City in Shaanxi province and Ningbo City in Zhejiang province have provided annual influenza vaccines through government health insurance since 2004 and 2010, respectively. Vaccine coverage is expected to rise substantially with increasing data on national- and province-specific influenza disease burden [11],[15], seasonality, cost-effectiveness, and initiation of national and provincial-level immunization programs. Our study is prone to a number of limitations. Our results are based on a relatively short period of time, 2005–2011, which limited our ability to capture multiyear influenza periodicities. Sampling intensity was not constant throughout time and could have affected our results, although data standardization and sensitivity analyses suggest this was not a major issue. Since a fraction of the northern provinces did not conduct year-round surveillance before 2009, it would be useful to revisit seasonality patterns in key provinces bordering the 33°N latitude threshold with longer year-round surveillance time series. Our dataset was too coarse to evaluate influenza patterns at the city or hospital level. However, surveillance data were based on a single city for five of the 30 studied provinces (including two northern and three southern provinces; Table 1), and hence influenza surveillance in these five provinces is not subject to aggregation issues. In particular, two of these “single-city” provinces, Beijing and Tianjin, are immediate neighbors with nearly identical influenza patterns, suggesting that surveillance information from this dataset is robust and captures true geographical differences in disease dynamics. Since most of the influenza information came from relatively large cities, however, we were unable to assess the finer details of influenza spatial transmission, including potential differences between rural and urban areas. Previous work suggests that influenza activity in rural areas of Western China is generally synchronous with that of the more populous Eastern coastal areas [18]. We chose to exclude information pertaining the 2009 pandemic period to focus on the seasonality of inter-pandemic influenza, and it is unclear how the emergence of the pandemic virus perturbed seasonality of the resident (sub)types. However, sensitivity analyses limited to data from the pre-pandemic period confirmed our findings (not shown). Another caveat is the lack of information on influenza A/H1N1 seasonality, as sampling was too thin to explore A/H3N2 and A/H1N1 viruses separately, and the patterns reported here for influenza A reflect those of the dominant A/H3N2 subtype. We have provided here a statistical description of influenza seasonality in China to inform timing of vaccination, using a two-stage approach used in past research to characterize the circulation of influenza and other infections in large regions and assess potential links with climate [2],[26],[49]–[51]. Our approach improves on previous work by integrating uncertainty in seasonal estimates obtained in the first stage analysis in hierarchical Bayesian models. However, our relatively simple seasonal models (see also [4]) explain only a fraction of the variance in weekly influenza surveillance data (typically ≤50% for influenza A and ≤30% for influenza B). Year-to-year variability in influenza epidemiology, complex virus circulation patterns specific to China, and sampling issues, may all contribute to weak model fit. Further, there was residual auto-correlation in some of our province-specific models, although auto-correlation was taken into account in error estimates via a block-bootstrap approach. Further work could focus on fitting compartmental transmission model to evaluate the transmissibility of the virus in different regions of China, and assess differences in herd immunity thresholds for vaccination. Interestingly, the effective reproduction number of influenza is thought to be relatively similar between temperate and tropical countries, ranging from 1.1–1.4 [52], suggesting that background immunity and transmission dynamics are broadly similar across regions (although it likely varies between years [53]). Overall, more modeling work is needed to evaluate regional differences in the evolutionary and transmission dynamics of influenza and their association with climate and demographic factors [3],[8],[18],[54]–[56]. Conclusion In conclusion, our study uncovered intriguing differences in influenza seasonality between regions and virus types in China, some of which can be associated with climatic factors, and confirm previous reports from other regions [3],[26]. Further work should focus on quantifying the balance between climatic drivers, population mixing, and other factors affecting influenza seasonality patterns globally, which could differ by virus types and subtypes. Our work has practical implications for the design of routine immunization programs in China, and suggests the need for staggered timing of vaccination in three broad epidemiological regions. Further surveillance studies are warranted to confirm these seasonality patterns and assess the match between influenza strains circulating in different provinces and WHO vaccine recommendations [18]. As routine immunization campaigns are rolled out and local vaccine production improves in resource-limited regions, it will become increasingly important to ensure that vaccination strategies are optimally tailored to the local epidemiology of the disease. Supporting Information Figure S1 Fit of type-specific seasonal influenza models in three provinces representative of broad influenza epidemiological regions in China. Shanxi (latitude 37.8°N, northern temperate province experiencing winter seasonal influenza A and B epidemics), Hubei (latitude 30.9°N, mid-latitude subtropical province experiencing semi-annual influenza A epidemics), and Guangdong (22.9°N, southern subtropical province experiencing late spring influenza A epidemics). Blue curve, observed cases standardized by the annual number of specimens tested; red curve, seasonal model. Grey lines mark Jan 1st of each year, while the green lines mark the 2009 A/H1N1 pandemic season, which was not included in the model fitting procedure. Model is based on a linear regression with harmonic terms for annual and semi-annual periodicities. (TIFF) Click here for additional data file. Figure S2 Residuals of seasonal models presented in Figure S1 in three selected provinces. (TIF) Click here for additional data file. Figure S3 Estimates of periodicity and timing of influenza epidemics in China (A and B combined). (Left) Timing of annual influenza peaks, in weeks. Timing is color coded by season. (Center) Amplitude of annual periodicity, ranging from low (yellow) to high (red), as indicated in the legend. Amplitude is relative to the mean of the weekly influenza time series in each province. (Right) Importance of semi-annual periodicities, measured by the ratio of the amplitude of the semi-annual periodicity to the sum of the amplitudes of annual and semi-annual periodicities. Yellow indicates strongly annual influenza epidemics, while red indicates marked semi-annual activity. See also Figure S4. (TIF) Click here for additional data file. Figure S4 Latitudinal gradients in seasonality of total influenza activity (A and B combined) in China. Left: Relative amplitude of annual periodicity. Middle: Peak timing. Right: Contribution of the semi-annual cycle, measured by the ratio of the amplitude of the semi-annual cycle to the sum of the amplitudes of annual and semi-annual cycles. Open circles represent point estimates from seasonal regression models and horizontal dashed lines represent 95% confidence intervals based on 1,000 block-bootstrap samples. Purple lines represent linear regression of seasonal parameters against latitude (dashed line, unweighted regression; solid line, regression weighted by the inverse of the variance of province-specific seasonal estimates); R2 and p-values are indicated on the graphs. Colors represent different climatic zones (black, cold-temperate; blue, mid-temperate; green, warm-temperate; orange, subtropical; red, tropical). (TIFF) Click here for additional data file. Figure S5 Sensitivity analysis on seasonal estimates for influenza A (top) and B (bottom) using a different model structure. Same as Figure 4 but using a logistic seasonal model with binomial errors to model weekly percent positive for influenza A and B (weekly number of influenza positive/weekly number of specimens tested). Left: Relative amplitude of annual periodicity. Middle: Peak timing. Right: Contribution of the semi-annual cycle, measured by the ratio of the amplitude of the semi-annual cycle to the sum of the amplitudes of annual and semi-annual cycles. Open circles represent point estimates from seasonal regression models. Purple lines represent linear regression of seasonal parameters against latitude; p-values are indicated on the graphs. Colors represent different climatic zones (black, cold-temperate; blue, mid-temperate; green, warm-temperate; orange, subtropical; red, tropical). (TIF) Click here for additional data file. Figure S6 Latitudinal gradient in duration of influenza epidemics, by province and virus type. Duration is based on a relative measure (number of weeks with more than 2.5% of annual influenza virus isolated), or an absolute measure (number of weeks with more than 5% influenza percent positive). Top panels: influenza A and B combined; middle panels: influenza A; bottom panels: influenza B. Horizontal grey bars represent ±2 standard deviations based on inter-annual variability in the 7 study years. (TIF) Click here for additional data file. Figure S7 Influenza A (top) and B (bottom) epidemiological regions identified by cluster analysis. Epidemiological regions are based on hierarchical clustering (Ward's method), using the Euclidian distance between weekly standardized influenza time series. Provinces are color-coded by climatic region (black, cold-temperate; blue, mid-temperate; green, warm-temperate; orange, subtropical; red, tropical). (TIFF) Click here for additional data file. Figure S8 Sensitivity analyses on cluster algorithms used to define influenza epidemiological regions (compare with Figure 6 ). Top: using a different distance metric for pairwise differences between influenza time series (absolute distance, also known as Manhattan distance, instead of Euclidian distance). Bottom: using a different clustering algorithm (complete linkage instead of Ward). Analyses are based on total influenza activity. (TIFF) Click here for additional data file. Text S1 Description of supplementary information. (DOC) Click here for additional data file.

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Seasonality, timing, and climate drivers of influenza activity worldwide.

Eduardo Azziz Baumgartner, Christine N. Dao, Sharifa Nasreen … (2012)

Although influenza is a vaccine-preventable disease that annually causes substantial disease burden, data on virus activity in tropical countries are limited. We analyzed publicly available influenza data to better understand the global circulation of influenza viruses. We reviewed open-source, laboratory-confirmed influenza surveillance data. For each country, we abstracted data on the percentage of samples testing positive for influenza each epidemiologic week from the annual number of samples testing positive for influenza. The start of influenza season was defined as the first week when the proportion of samples that tested positive remained above the annual mean. We assessed the relationship between percentage of samples testing positive and mean monthly temperature with use of regression models. We identified data on laboratory-confirmed influenza virus infection from 85 countries. More than one influenza epidemic period per year was more common in tropical countries (41%) than in temperate countries (15%). Year-round activity (ie, influenza virus identified each week having ≥ 10 specimens submitted) occurred in 3 (7%) of 43 temperate, 1 (17%) of 6 subtropical, and 11 (37%) of 30 tropical countries with available data (P = .006). Percentage positivity was associated with low temperature (P = .001). Annual influenza epidemics occur in consistent temporal patterns depending on climate.

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Seasonal influenza epidemiology in sub-Saharan Africa: a systematic review.

Bradford Gessner, Nahoko Shindo, Sylvie Briand (2011)

Acute respiratory infection (ARI) is a leading cause of mortality worldwide, of which influenza is an important cause that can be prevented with vaccination. We did a systematic review of research published from 1980 to 2009 on seasonal influenza epidemiology in sub-Saharan Africa to identify data strengths and weaknesses that might affect policy decisions, to assess the state of knowledge on influenza disease burden, and to ascertain unique features of influenza epidemiology in the region. We assessed 1203 papers, reviewed 104, and included 49 articles. 1-25% of outpatient ARI visits were caused by influenza (11 studies; mean 9·5%; median 10%), whereas 0·6-15·6% of children admitted to hospital for ARI had influenza identified (15 studies; mean 6·6%; median 6·3%). Influenza was highly seasonal in southern Africa. Other data were often absent, particularly direct measurement of influenza incidence rates for all ages, within different patient settings (outpatient, inpatient, community), and for all countries. Data from sub-Saharan Africa are insufficient to allow most countries to prioritise strategies for influenza prevention and control. Key data gaps include incidence and case-fatality ratios for all ages, the contribution of influenza towards admission of adults to hospital for ARI, representative seasonality data, economic burden, and the interaction of influenza with prevalent disorders in Africa, such as malaria, HIV, and malnutrition. Copyright © 2011 Elsevier Ltd. All rights reserved.

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Author and article information

Contributors

Dena L. Schanzer: Role: Editor

Journal

Journal ID (nlm-ta): PLoS One

Journal ID (iso-abbrev): PLoS ONE

Journal ID (publisher-id): plos

Journal ID (pmc): plosone

Title: PLoS ONE

Publisher: Public Library of Science (San Francisco, CA USA )

ISSN (Electronic): 1932-6203

Publication date (Electronic): 31 March 2016

Publication date Collection: 2016

Volume: 11

Issue: 3

Electronic Location Identifier: e0152310

Affiliations

[1 ]Netherlands Institute for Health Services Research (NIVEL), Utrecht, The Netherlands

[2 ]Sección de Virus Respiratorios y Exantemáticos, Instituto de Salud Pública de Chile, Santiago de Chile, Chile

[3 ]Istanbul University, Istanbul, Turkey

[4 ]National Influenza Center, Ministry of Health, Managua, Nicaragua

[5 ]National Influenza Center, Institut National d'Hygiène, Ministry of Health, Rabat, Morocco

[6 ]National Center for Epidemiology, Surveillance and Health Promotion National Institute of Health, Rome, Italy

[7 ]Epidemiology and Prevention Branch, Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, United States of America

[8 ]Centro de Referencia Nacional de Influenza y Otros Virus Respiratorios, Instituto Nacional de Investigación en Salud Pública (INSPI), Guayaquil, Ecuador

[9 ]National Influenza Center, Ministry of Health, Guatemala City, Guatemala

[10 ]US Centers for Disease Control, Central American Region, Guatemala City, Guatemala

[11 ]Centre for Respiratory Diseases and Meningitis (CRDM), National Institute for Communicable Diseases, Johannesburg, South Africa

[12 ]School of Public Health, Faculty of Health Science, University of the Witwatersrand, Johannesburg, South Africa

[13 ]Communicable Diseases Division, Ministry of Health, Singapore, Singapore

[14 ]Respiratory Viruses Unit, Pasteur Institute of Côte d’Ivoire, Abidjan, Côte d’Ivoire

[15 ]National Influenza Center, Ministry of Health, San Salvador, El Salvador

[16 ]Public Health Laboratory, Department of Public Health, Ministry of Health, Thimphu, Bhutan

[17 ]US Centers for Disease Control and Prevention, Nairobi, Kenya

[18 ]Division of Infectious Disease, Key Laboratory of Surveillance and Early-warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China

[19 ]Ministry of Health, Brasília, DF, Brazil

[20 ]National Influenza Reference Laboratory, Infectious Diseases Department, National Institute of Health, Lisbon, Portugal

[21 ]National Influenza Center, Virology Unit, Institut Pasteur of Madagascar, Antananarivo, Madagascar

[22 ]L.V.Gromashevsky Institute of Epidemiology and Infectious Diseases National Academy of Medical Science of Ukraine, Kiev, Ukraine

[23 ]Institute of Environmental Science and Research, Wellington, New Zealand

[24 ]Zonal Virology Laboratory, Astana Center of Sanitary Epidemiology Expertise, Astana, Kazakhstan

[25 ]US Naval Medical Research Unit No. 2, Jakarta, Indonesia

[26 ]Instituto Nacional de Enfermedades Respiratorias “Dr. Emilio Coni,” Santa Fe, Argentina

[27 ]National Influenza Center, Ministry of Health, San José, Costa Rica

[28 ]National Institute of Hygiene and Epidemiology, Hanoi, Vietnam

[29 ]National Influenza Center, Ministry of Health, Tegucigalpa, Honduras

[30 ]National Influenza Center, IC Gorgas, Panama City, Panama

[31 ]Service de Virologie, Centre Pasteur du Cameroun, Yaounde, Cameroon

[32 ]Vaccine Preventable Diseases Surveillance Section, Health Policy Protection Branch, Office of Health Protection, Department of Health, Woden, Australia

[33 ]Respiratory Diseases Department, Public Health England, Colindale, United Kingdom

[34 ]National Influenza Center, National Institute of Health, Rome, Italy

[35 ]National Institute of Health, Epidemiology Department, Lisbon, Portugal

[36 ]Global Disease Detection, US-CDC, Pretoria, South Africa

[37 ]Zoonoses Research Center, Department of Medical Virology, University of Pretoria, Pretoria, South Africa

[38 ]Respiratory Virus Unit, Public Health England, Colindale, United Kingdom

Public Health Agency of Canada, CANADA

Author notes

Competing Interests: The Global Influenza B Study was supported by an unrestricted grant from Sanofi Pasteur; this does not alter the authors' adherence to PLOS ONE policies on sharing data and materials. The authors have no other potential competing interests to declare.

Conceived and designed the experiments: SC JP FS MAC QSH. Performed the experiments: SC JP FS. Analyzed the data: SC JP. Contributed reagents/materials/analysis tools: WA SB A. Balmaseda A. Barakat A. Bella A. Bimohuen LB JB A. Bruno LC MAC AWC CC JC CD CdL DDM KD GOE RAF LF WAFA RG JMH OH QSH HAK LK HK GK JL ML LL PVMH CMPH MLM AM BM JAM RN Nurhayati AO RO RP KP SP MTQL NHR AR JMR RTPL MV MAV SW JY HY MZ. Wrote the paper: SC JP FS.

¶ Membership of the Global Influenza B Study Group is listed in the Acknowledgments.

* E-mail: s.caini@ 123456nivel.nl

Article

Publisher ID: PONE-D-15-45474

DOI: 10.1371/journal.pone.0152310

PMC ID: 4816507

PubMed ID: 27031105

SO-VID: 57cfb99e-b5b8-4529-b0cb-44353433542d

License:

This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

History

Date received : 16 October 2015

Date accepted : 11 March 2016

Page count

Figures: 4, Tables: 3, Pages: 15

Funding

The Global Influenza B Study is supported by an unrestricted research grant from Sanofi Pasteur. The study sponsor had no role in the design of the study; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication. The study sponsor had no access to the data in the study.

Custom metadata

Data Availability The database of the Global Influenza B Study was created by collecting surveillance datasets from each of the participating countries. These datasets are owned by the participating countries, and thus cannot be shared publicly by the study group at NIVEL. Researchers interested in obtaining the country-specific datasets may contact the individuals listed in the Supporting Information for further details regarding data access.

Temporal Patterns of Influenza A and B in Tropical and Temperate Countries: What Are the Lessons for Influenza Vaccination?

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Characterization of Regional Influenza Seasonality Patterns in China and Implications for Vaccination Strategies: Spatio-Temporal Modeling of Surveillance Data

Seasonality, timing, and climate drivers of influenza activity worldwide.

Seasonal influenza epidemiology in sub-Saharan Africa: a systematic review.

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