Dataset Information

Real-time predictive seasonal influenza model in Catalonia, Spain.

ABSTRACT: Influenza surveillance is critical to monitoring the situation during epidemic seasons and predictive mathematic models may aid the early detection of epidemic patterns. The objective of this study was to design a real-time spatial predictive model of ILI (Influenza Like Illness) incidence rate in Catalonia using one- and two-week forecasts. The available data sources used to select explanatory variables to include in the model were the statutory reporting disease system and the sentinel surveillance system in Catalonia for influenza incidence rates, the official climate service in Catalonia for meteorological data, laboratory data and Google Flu Trend. Time series for every explanatory variable with data from the last 4 seasons (from 2010-2011 to 2013-2014) was created. A pilot test was conducted during the 2014-2015 season to select the explanatory variables to be included in the model and the type of model to be applied. During the 2015-2016 season a real-time model was applied weekly, obtaining the intensity level and predicted incidence rates with 95% confidence levels one and two weeks away for each health region. At the end of the season, the confidence interval success rate (CISR) and intensity level success rate (ILSR) were analysed. For the 2015-2016 season a CISR of 85.3% at one week and 87.1% at two weeks and an ILSR of 82.9% and 82% were observed, respectively. The model described is a useful tool although it is hard to evaluate due to uncertainty. The accuracy of prediction at one and two weeks was above 80% globally, but was lower during the peak epidemic period. In order to improve the predictive power, new explanatory variables should be included.

SUBMITTER: Basile L

PROVIDER: S-EPMC5841785 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Real-time predictive seasonal influenza model in Catalonia, Spain.

Basile Luca L Oviedo de la Fuente Manuel M Torner Nuria N Martínez Ana A Jané Mireia M

PloS one 20180307 3

Influenza surveillance is critical to monitoring the situation during epidemic seasons and predictive mathematic models may aid the early detection of epidemic patterns. The objective of this study was to design a real-time spatial predictive model of ILI (Influenza Like Illness) incidence rate in Catalonia using one- and two-week forecasts. The available data sources used to select explanatory variables to include in the model were the statutory reporting disease system and the sentinel surveil ...[more]

PMID: 29513710

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Project description:Seasonal influenza results in substantial annual morbidity and mortality in the United States and worldwide. Accurate forecasts of key features of influenza epidemics, such as the timing and severity of the peak incidence in a given season, can inform public health response to outbreaks. As part of ongoing efforts to incorporate data and advanced analytical methods into public health decision-making, the United States Centers for Disease Control and Prevention (CDC) has organized seasonal influenza forecasting challenges since the 2013/2014 season. In the 2017/2018 season, 22 teams participated. A subset of four teams created a research consortium called the FluSight Network in early 2017. During the 2017/2018 season they worked together to produce a collaborative multi-model ensemble that combined 21 separate component models into a single model using a machine learning technique called stacking. This approach creates a weighted average of predictive densities where the weight for each component is determined by maximizing overall ensemble accuracy over past seasons. In the 2017/2018 influenza season, one of the largest seasonal outbreaks in the last 15 years, this multi-model ensemble performed better on average than all individual component models and placed second overall in the CDC challenge. It also outperformed the baseline multi-model ensemble created by the CDC that took a simple average of all models submitted to the forecasting challenge. This project shows that collaborative efforts between research teams to develop ensemble forecasting approaches can bring measurable improvements in forecast accuracy and important reductions in the variability of performance from year to year. Efforts such as this, that emphasize real-time testing and evaluation of forecasting models and facilitate the close collaboration between public health officials and modeling researchers, are essential to improving our understanding of how best to use forecasts to improve public health response to seasonal and emerging epidemic threats.

Project description:BACKGROUND:Influenza viruses (FLUV) are continuously evolving, which explain the occurrence of seasonal influenza epidemics and the need to review the vaccine strain composition annually. The aim is to describe the genetic diversity and clinical outcomes of FLUV detected at a tertiary university hospital in Barcelona (Spain) during the 2012-2016 seasons. METHODS:The detection of FLUV from patients attended at the Emergency Department or admitted to the hospital was performed by either immunofluorescence or PCR-based assays. A specific real-time one-step multiplex RT-PCR was performed for influenza A (FLUAV) subtyping. The complete coding haemagglutinin domain 1 (HA1) and neuraminidase (NA) (2015-2016) protein sequences from a representative sampling were molecular characterised. RESULTS:A total 1774 (66.1%) FLUAV and 910 (33.9%) influenza B (FLUBV) cases were laboratory-confirmed. The hospitalisation rate was different between seasons, being the highest (81.4%) during the 2014-2015 season. FLUV were genetically close to vaccine strains except to the 2014-2015, in which most characterised A(H3N2) viruses belonged to a genetic group different from the vaccine strain. During the 2015-2016 season, B/Victoria-like viruses were the most predominant, but this component was not included in the trivalent vaccine used. Mutations D222G or D222N in HA1-domain were found in 3 A(H1N1)pdm09 strains from ICU-admitted cases. Three A(H1N1)pdm09 strains carried the NA H275Y (2) and S247N (1) mutations, respectively related to resistance or decreased susceptibility to oseltamivir. CONCLUSIONS:The circulation of drifted A(H3N2) strains during the 2014-2015 season was related to the high hospitalisation rate due to the mismatch with the vaccine strains. The predominance of a FLUBV lineage not included in the trivalent influenza vaccine during the 2015-2016 season highlights the need to use a tetravalent influenza vaccine. Virological surveillance of viral variants carrying protein changes that alter tropism and susceptibility to antivirals features should be strengthened in hospital settings.

Project description:Understanding the immune response after vaccination against new influenza strains is highly important in case of an imminent influenza pandemic and for optimization of seasonal vaccination strategies in high risk population groups, especially the elderly. Models predicting the best sero-conversion response among the three strains in the seasonal vaccine were recently suggested. However, these models use a large number of variables and/or information post- vaccination. Here in an exploratory pilot study, we analyzed the baseline immune status in young (<31 years, N = 17) versus elderly (≥50 years, N = 20) donors sero-negative to the newly emerged A(H1N1)pdm09 influenza virus strain and correlated it with the serological response to that specific strain after seasonal influenza vaccination. Extensive multi-chromatic FACS analysis (36 lymphocyte sub-populations measured) was used to quantitatively assess the cellular immune status before vaccination. We identified CD4+ T cells, and amongst them particularly naive CD4+ T cells, as the best correlates for a successful A(H1N1)pdm09 immune response. Moreover, the number of influenza strains a donor was sero-negative to at baseline (NSSN) in addition to age, as expected, were important predictive factors. Age, NSSN and CD4+ T cell count at baseline together predicted sero-protection (HAI≥40) to A(H1N1)pdm09 with a high accuracy of 89% (p-value = 0.00002). An additional validation study (N = 43 vaccinees sero-negative to A(H1N1)pdm09) has confirmed the predictive value of age, NSSN and baseline CD4+ counts (accuracy = 85%, p-value = 0.0000004). Furthermore, the inclusion of donors at ages 31-50 had shown that the age predictive function is not linear with age but rather a sigmoid with a midpoint at about 50 years. Using these results we suggest a clinically relevant prediction model that gives the probability for non-protection to A(H1N1)pdm09 influenza strain after seasonal multi-valent vaccination as a continuous function of age, NSSN and baseline CD4 count.

Dataset Information

Real-time predictive seasonal influenza model in Catalonia, Spain.

Publications

Real-time predictive seasonal influenza model in Catalonia, Spain.

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