Dataset Information

Global Spatio-temporal Patterns of Influenza in the Post-pandemic Era.

ABSTRACT: We study the global spatio-temporal patterns of influenza dynamics. This is achieved by analysing and modelling weekly laboratory confirmed cases of influenza A and B from 138 countries between January 2006 and January 2015. The data were obtained from FluNet, the surveillance network compiled by the the World Health Organization. We report a pattern of skip-and-resurgence behavior between the years 2011 and 2013 for influenza H1N1pdm, the strain responsible for the 2009 pandemic, in Europe and Eastern Asia. In particular, the expected H1N1pdm epidemic outbreak in 2011/12 failed to occur (or "skipped") in many countries across the globe, although an outbreak occurred in the following year. We also report a pattern of well-synchronized wave of H1N1pdm in early 2011 in the Northern Hemisphere countries, and a pattern of replacement of strain H1N1pre by H1N1pdm between the 2009 and 2012 influenza seasons. Using both a statistical and a mechanistic mathematical model, and through fitting the data of 108 countries, we discuss the mechanisms that are likely to generate these events taking into account the role of multi-strain dynamics. A basic understanding of these patterns has important public health implications and scientific significance.

SUBMITTER: He D

PROVIDER: S-EPMC4457022 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature

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Global Spatio-temporal Patterns of Influenza in the Post-pandemic Era.

He Daihai D Lui Roger R Wang Lin L Tse Chi Kong CK Yang Lin L Stone Lewi L

Scientific reports 20150605

We study the global spatio-temporal patterns of influenza dynamics. This is achieved by analysing and modelling weekly laboratory confirmed cases of influenza A and B from 138 countries between January 2006 and January 2015. The data were obtained from FluNet, the surveillance network compiled by the the World Health Organization. We report a pattern of skip-and-resurgence behavior between the years 2011 and 2013 for influenza H1N1pdm, the strain responsible for the 2009 pandemic, in Europe and ...[more]

PMID: 26046930

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Project description:BACKGROUND:Seasonal patterns of influenza A subtypes and B lineages in tropical/subtropical regions across age have remained to be explored. The impact of the 2009 H1N1 pandemic on seasonal influenza activity have not been well understood. METHODS:Based on a national sentinel hospital-based influenza surveillance system, the epidemiology of influenza virus during 2006/07-2015/16 was characterized in the subtropical city, Chengdu. Chengdu is one of the most populous cities in southwestern China, where the first reported case of A/H1N1pdm09 in mainland China was identified. Wavelet analysis was applied to identify the periodicities of A/H3N2, seasonal A/H1N1, A/H1N1pdm09, Victoria, and Yamagata across age, respectively. The persistence and age distribution patterns were described during the pre-pandemic (2006/07-2008/09), pandemic (2009/10), and post-pandemic (2010/11-2015/16) seasons. RESULTS:A total of 10,981 respiratory specimens were collected, of which 2516 influenza cases were identified. Periodicity transition from semi-annual cycles to an annual cycle was observed for composite influenza virus as well as A/H3N2 along in Chengdu since the 2009 H1N1 pandemic. Semi-annual cycles of composite influenza virus and A/H3N2 along were observed again during 2014/15-2015/16, coinciding with the emergence and predominance of A/H3N2 significant antigenic drift groups. However, A/H1N1pdm09, Victoria, and Yamagata generally demonstrated an annual winter-spring peak in non-pandemic seasons. Along with periodicity transitions, age groups with higher positive rates shifted from school-aged children and adults to adults and the elderly for A/H1N1pdm09 during 2009/10-2010/11 and for A/H3N2 during 2014/15-2015/16. CONCLUSIONS:Differences in periodicity and age distribution by subtype/lineage and by season highlight the importance of increasing year-round influenza surveillance and developing subtype/lineage- and age-specific prevention and control measures. Changes of periodicity and age shifts should be considered in public health response to influenza pandemics and epidemics. In addition, it is suggested to use quadrivalent influenza vaccines to provide protection against both influenza B lineages.

Project description:BACKGROUND: The impact of socio-demographic factors and baseline health on the mortality burden of seasonal and pandemic influenza remains debated. Here we analyzed the spatial-temporal mortality patterns of the 1918 influenza pandemic in Spain, one of the countries of Europe that experienced the highest mortality burden. METHODS: We analyzed monthly death rates from respiratory diseases and all-causes across 49 provinces of Spain, including the Canary and Balearic Islands, during the period January-1915 to June-1919. We estimated the influenza-related excess death rates and risk of death relative to baseline mortality by pandemic wave and province. We then explored the association between pandemic excess mortality rates and health and socio-demographic factors, which included population size and age structure, population density, infant mortality rates, baseline death rates, and urbanization. RESULTS: Our analysis revealed high geographic heterogeneity in pandemic mortality impact. We identified 3 pandemic waves of varying timing and intensity covering the period from Jan-1918 to Jun-1919, with the highest pandemic-related excess mortality rates occurring during the months of October-November 1918 across all Spanish provinces. Cumulative excess mortality rates followed a south-north gradient after controlling for demographic factors, with the North experiencing highest excess mortality rates. A model that included latitude, population density, and the proportion of children living in provinces explained about 40% of the geographic variability in cumulative excess death rates during 1918-19, but different factors explained mortality variation in each wave. CONCLUSIONS: A substantial fraction of the variability in excess mortality rates across Spanish provinces remained unexplained, which suggests that other unidentified factors such as comorbidities, climate and background immunity may have affected the 1918-19 pandemic mortality rates. Further archeo-epidemiological research should concentrate on identifying settings with combined availability of local historical mortality records and information on the prevalence of underlying risk factors, or patient-level clinical data, to further clarify the drivers of 1918 pandemic influenza mortality.

Project description:BackgroundAgencies such as the Centers for Disease Control and Prevention (CDC) currently release influenza-like illness incidence data, along with descriptive summaries of simple spatio-temporal patterns and trends. However, public health researchers, government agencies, as well as the general public, are often interested in deeper patterns and insights into how the disease is spreading, with additional context. Analysis by domain experts is needed for deriving such insights from incidence data.ObjectiveOur goal was to develop an automated approach for finding interesting spatio-temporal patterns in the spread of a disease over a large region, such as regions which have specific characteristics (eg, high incidence in a particular week, those which showed a sudden change in incidence) or regions which have significantly different incidence compared to earlier seasons.MethodsWe developed techniques from the area of transactional data mining for characterizing and finding interesting spatio-temporal patterns in disease spread in an automated manner. A key part of our approach involved using the principle of minimum description length for representing a given target set in terms of combinations of attributes (referred to as clauses); we considered both positive and negative clauses, relaxed descriptions which approximately represent the set, and used integer programming to find such descriptions. Finally, we designed an automated approach, which examines a large space of sets corresponding to different spatio-temporal patterns, and ranks them based on the ratio of their size to their description length (referred to as the compression ratio).ResultsWe applied our methods using minimum description length to find spatio-temporal patterns in the spread of seasonal influenza in the United States using state level influenza-like illness activity indicator data from the CDC. We observed that the compression ratios were over 2.5 for 50% of the chosen sets, when approximate descriptions and negative clauses were allowed. Sets with high compression ratios (eg, over 2.5) corresponded to interesting patterns in the spatio-temporal dynamics of influenza-like illness. Our approach also outperformed description by solution in terms of the compression ratio.ConclusionsOur approach, which is an unsupervised machine learning method, can provide new insights into patterns and trends in the disease spread in an automated manner. Our results show that the description complexity is an effective approach for characterizing sets of interest, which can be easily extended to other diseases and regions beyond influenza in the US. Our approach can also be easily adapted for automated generation of narratives.

Project description:BackgroundThe complexity of influenza seasonal patterns in the inter-tropical zone impedes the establishment of effective routine immunization programs. China is a climatologically and economically diverse country, which has yet to establish a national influenza vaccination program. Here we characterize the diversity of influenza seasonality in China and make recommendations to guide future vaccination programs.Methods and findingsWe compiled weekly reports of laboratory-confirmed influenza A and B infections from sentinel hospitals in cities representing 30 Chinese provinces, 2005-2011, and data on population demographics, mobility patterns, socio-economic, and climate factors. We applied linear regression models with harmonic terms to estimate influenza seasonal characteristics, including the amplitude of annual and semi-annual periodicities, their ratio, and peak timing. Hierarchical Bayesian modeling and hierarchical clustering were used to identify predictors of influenza seasonal characteristics and define epidemiologically-relevant regions. The annual periodicity of influenza A epidemics increased with latitude (mean amplitude of annual cycle standardized by mean incidence, 140% [95% CI 128%-151%] in the north versus 37% [95% CI 27%-47%] in the south, p<0.0001). Epidemics peaked in January-February in Northern China (latitude ≥33°N) and April-June in southernmost regions (latitude <27°N). Provinces at intermediate latitudes experienced dominant semi-annual influenza A periodicity with peaks in January-February and June-August (periodicity ratio >0.6 in provinces located within 27.4°N-31.3°N, slope of latitudinal gradient with latitude -0.016 [95% CI -0.025 to -0.008], p<0.001). In contrast, influenza B activity predominated in colder months throughout most of China. Climate factors were the strongest predictors of influenza seasonality, including minimum temperature, hours of sunshine, and maximum rainfall. Our main study limitations include a short surveillance period and sparse influenza sampling in some of the southern provinces.ConclusionsRegional-specific influenza vaccination strategies would be optimal in China; in particular, annual campaigns should be initiated 4-6 months apart in Northern and Southern China. Influenza surveillance should be strengthened in mid-latitude provinces, given the complexity of seasonal patterns in this region. More broadly, our findings are consistent with the role of climatic factors on influenza transmission dynamics. Please see later in the article for the Editors' Summary.

Dataset Information

Global Spatio-temporal Patterns of Influenza in the Post-pandemic Era.

Publications

Global Spatio-temporal Patterns of Influenza in the Post-pandemic Era.

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